Publikationen des IFF

Hier finden Sie Publikationen, die am IFF entstanden sind.

Publikationsliste (peer-reviewed)

  1. 2026

    1. J. Wirth, A. Zoefeld, G. Bitsch, and T. Bauernhansl, “Development Of A Process Model For The Selection And Development Of Service-Oriented And Data-Driven Business Models In Mechanical And Plant Engineering,” in Proceedings of the Conference on Production Systems and Logistics : Instituto Superior de Engenharia do Porto (ISEP) Porto, Portugal. 14th - 17th April 2026. Proceedings, Offenburg: publish-Ing, 2026, pp. 365–375. doi: 10.15488/20999.
    2. P. Livotov et al., “Comparative Study on AI-Augmented Inventive Problem Solving with TRIZ, TRIZ-AI Hybrid, and Autonomous AI: An Inline Coating Measurement Case in Lithium-Ion Cell Production,” in World Conference of AI-Powered Innovation and TRIZ Methodology : 2nd IFIP WG 5.4 International TRIZ Future Conference, TRAI 2025, Paris, France, November 5-7, 2025, Proceedings, Part I, Cham: Springer, 2026, pp. 35–54. doi: 10.1007/978-3-032-08847-5_3.
    3. V. Bezold, P. Wagner, J. Hofmann, M. Huber, and A. Sauer, “Trustworthy and explainable deep reinforcement learning for safe and energy-efficient process control: A use case in industrial compressed air systems,” Energy and AI, p. 13, 2026, doi: 10.1016/j.egyai.2026.100685.
    4. J. Wirth, T. Scheible, Y. Schneider, and T. Bauernhansl, “Challenges in Implementing Service-Oriented and Data-Driven Business Models in Mechanical and Plant Engineering: A Literature Review and Empirical Analysis,” in Proceedings of the Conference on Production Systems and Logistics : Instituto Superior de Engenharia do Porto (ISEP) Porto, Portugal. 14th - 17th April 2026. Proceedings, Offenburg: publish-Ing, 2026, pp. 376–390. doi: 10.15488/21000.
    5. M. Sonnenberg, R. Miehe, and T. Bauernhansl, “Designing Biointelligence: Essential Features for Adaptive, Life-Like Systems,” Procedia CIRP, vol. 142, pp. 328–333, 2026, doi: 10.1016/j.procir.2026.05.269.
    6. K. Rösch, “Schleifscheiben aus Schleifvlies-Abfall : Ressourcenschonende Herstellung von Schleifmitteln,” wt Werkstattstechnik online, vol. 116, Art. no. 1–2, 2026, doi: 10.37544/1436-4980-2026-01-02-89.
    7. E. Gamero, J. Kendler, Y. Baumgarten, R. Miehe, and T. Bauernhansl, “Life cycle assessment of biosensor for virus production: A case study in sustainable design of biointelligent products,” Procedia CIRP, vol. 140, pp. 479–484, 2026, doi: 10.1016/j.procir.2026.05.081.
    8. H. Himmelstoß and T. Bauernhansl, “Systematic Literature Review of Use Cases for Data-Driven Product Development in Manufacturing Companies,” in Advances in Production Management Systems : Cyber-Physical-Human Production Systems: Human-AI Collaboration and Beyond. 44th IFIP WG 5.7 International Conference, APMS 2025, Kamakura, Japan, August 31 - September 4, 2025, Proceedings, Part VI, Cham: Springer, 2026, pp. 3–15. doi: 10.1007/978-3-032-03550-9_1.
    9. D. Shi, O. Meyer, F. Zhi, H. Wang, and T. Bauernhansl, “Cognitive digital twins for capability matching toward reconfigurable manufacturing: Leveraging asset administration shells and large language models,” Robotics and Computer-Integrated Manufacturing, vol. 97, 2026, doi: 10.1016/j.rcim.2025.103105.
    10. S. Kiemel, Y. Song, S. Glöser-Chahoud, R. Miehe, and A. Sauer, “Towards a circular bioeconomy: A bi-criteria site level decision support for potential cluster nodes exemplified by hydrogen production in Baden-Württemberg,” Journal of Cleaner Production, vol. 546, 2026, doi: 10.1016/j.jclepro.2026.147589.
    11. A. Shoshi, P. Reimann, D. Schel, T. Bauernhansl, and R. Miehe, “Data Platforms in Biointelligent Value Creation Systems - Towards a Biointelligence Metaverse,” Procedia Computer Science, vol. 277, pp. 3521–3530, 2026, doi: 10.1016/j.procs.2026.02.387.
    12. M. Maier, J. Bortloff, and J. N. Oster, “Methodik zur Erzeugung von Wirkungsgradkennlinien : Parametrisierung sektorübergreifender agentenbasierter Energiesystemmodelle,” wt Werkstattstechnik online, vol. 116, Art. no. 4, 2026, doi: 10.37544/1436-4980-2026-04-32.
    13. M. Schneider, E. Groß, and T. Bauernhansl, “Designing a Methodology for the Selection and Evaluation of Smart Services in Manufacturing: An Explorative Analysis of Challenges, Best Practices, and Requirements,” Procedia CIRP, vol. 142, pp. 630–635, 2026, doi: 10.1016/j.procir.2026.05.320.
    14. M. Oberle, D. Schel, and T. Bauernhansl, “Agentic Manufacturing Operations Management: Concept and First Prototype,” Procedia CIRP, vol. 142, pp. 392–397, 2026, doi: 10.1016/j.procir.2026.05.280.
    15. B. Götz, M. Schneider, D. Schel, and T. Bauernhansl, “Implementation and Validation of Elastic Transformation Engine for Asset Administration Shells,” in Proceedings of the Conference on Production Systems and Logistics : Instituto Superior de Engenharia do Porto (ISEP) Porto, Portugal. 14th - 17th April 2026. Proceedings, Offenburg: publish-Ing, 2026, pp. 32–42. doi: 10.15488/20971.
    16. H. Russell, H. Himmelstoß, F. Böttinger, S. Kern, and T. Bauernhansl, “Causal Models and a Prototypical Implementation of a Pipeline for Causal Analysis in Manufacturing,” in Product Lifecycle Management. PLM in the Age of Model-Based Engineering in Industry : 22nd IFIP WG 5.1 International Conference, PLM 2025, Seville, Spain, July 6-9, 2025, Revised Selected Papers, Part I, Cham: Springer, 2026, pp. 182–190. doi: 10.1007/978-3-032-09700-2_18.
    17. T. Teriete, K. Erlach, T. Bauernhansl, and W. van der Aalst, “Combining value stream mapping and process mining in production: a systematic literature review,” Discover Applied Sciences, 2026, doi: 10.1007/s42452-026-08446-y.
    18. C. Rietdorf et al., “Assessing Material Circularity and Carbon Footprint at Component Level in Electronic Products,” Procedia CIRP, vol. 140, pp. 359–364, 2026, doi: 10.1016/j.procir.2026.05.061.
    19. R. Miehe and P. Schrader, “On Ambidexterity of Leadership and Organizational Design in the Context of Relative and Absolute Sustainability,” Procedia CIRP, vol. 140, pp. 569–574, 2026, doi: 10.1016/j.procir.2026.05.096.
    20. R. Miehe and T. Mueller-Kirschbaum, “The Next Neighbor Problem of the Circular Economy - A Life Cycle Theoretic Perspective on Occurrence and Mitigation Strategies,” Procedia CIRP, vol. 140, pp. 557–562, 2026, doi: 10.1016/j.procir.2026.05.094.
    21. S. Steinmeier, C. Rietdorf, M. Rentschler, and E. Gamero, “Decarbonising Industrial Supply Chains: A Strategic Framework for Managing Scope 3 Emissions in Procurement,” Procedia CIRP, vol. 140, pp. 425–430, 2026, doi: 10.1016/j.procir.2026.05.072.
    22. J. Wirth, F. Meldt, F. Hoffmann, M. Weigold, and T. Bauernhansl, “From Uncertainty to Value: The Scoreboard and Feasibility Gate for Early-Stage Financial Planning in Industrial Data Ecosystems,” in Proceedings of the Conference on Production Systems and Logistics : Instituto Superior de Engenharia do Porto (ISEP) Porto, Portugal. 14th - 17th April 2026. Proceedings, Offenburg: publish-Ing, 2026, pp. 329–351. doi: 10.15488/20997.
    23. F. Mauthe, C. Braun, J. Raible, P. Zeiler, and M. Huber, “A Novel Taxonomy and Approaches for the Identification of Frequently Occurring Regularities in Degradation Processes of Engineering Systems,” International Journal of Prognostics and Health Management, p. 25, 2026, doi: 10.36001/IJPHM.2026.v17i1.4411.
    24. C. Buchner et al., “Comparing few-shot learning methods in various application scenarios for quality prediction in ultrasonic wire bonding,” The international journal of advanced manufacturing technology, vol. 142, Art. no. 7–8, 2026, doi: 10.1007/s00170-025-17356-z.
    25. T. Bauernhansl, K. Erlach, H.-H. Wiendahl, D. Ranke, and M. Trierweiler, “Dreistufige Systematik der Organisationsformen in der Produktion : Bestehende Systematiken und Grundaufbau der neuen Systematik,” ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb : Organ des VDI-Kompetenzfeldes Informationstechnik, vol. 121, Art. no. 6, 2026, doi: 10.1515/zwf-2026-1087.
    26. N. Silber, J. Scheurich, S. Eigner, K. Zhang, J. Renz, and R. Miehe, “BPA Bioremediation for Circular Economy Strategies : Microbial strategies for BPA removal from recycling and wastewater streams,” wt Werkstattstechnik online, vol. 116, Art. no. 4, 2026, doi: 10.37544/1436-4980-2026-04-103.
    27. J. Wirth, D. Unic, and T. Bauernhansl, “Structuring the Shift: Designing and Applying a Smart PSS Transformation Framework with the TriProGenix Approach,” in Proceedings of the Conference on Production Systems and Logistics : Instituto Superior de Engenharia do Porto (ISEP) Porto, Portugal. 14th - 17th April 2026. Proceedings, Offenburg: publish-Ing, 2026, pp. 314–328. doi: 10.15488/20996.
    28. L. Langenfeld and T. Bauernhansl, “Towards Principles for Designing Lean Intralogistics Systems: Systematic Transfer of Lean Thinking to Reduce Waste in Intralogistics,” in Proceedings of the Conference on Production Systems and Logistics : Instituto Superior de Engenharia do Porto (ISEP) Porto, Portugal. 14th - 17th April 2026. Proceedings, Offenburg: publish-Ing, 2026, pp. 449–461. doi: 10.15488/21006.
    29. A. Shoshi, T. Bauernhansl, and R. Miehe, “Information Logistics as a Key Enabler for Biointelligent Value Creation Systems - Challenges and Requirements,” in Safe and Sustainable Value Creation by Design : Proceedings of the 21st Global Conference on Sustainable Manufacturing (GCSM 2025) September 10-12, 2025, Bologna, Italy, Volume 2, Cham: Springer, 2026, pp. 372–380. doi: 10.1007/978-3-032-21154-5_41.
    30. M. Schutzbach, M. Rentschler, L. Hans, S. Niethammer, R. Miehe, and A. Sauer, “Integrated Approach for Calculating Corporate and Product Carbon Footprints in the Textile Industry,” in Safe and Sustainable Value Creation by Design : Proceedings of the 21st Global Conference on Sustainable Manufacturing (GCSM 2025) September 10-12, 2025, Bologna, Italy, Volume 1, Cham: Springer, 2026, pp. 172–180. doi: 10.1007/978-3-032-21157-6_20.
    31. M. Sonnenberg, D. Petrik, R. Miehe, and T. Bauernhansl, “Foundations of design for biointelligence: expert insights on engineering challenges and design heuristics,” Research in Engineering Design, vol. 37, Art. no. 18, 2026, doi: 10.1007/s00163-026-00477-1.
    32. M. Höding, J. Wirth, S. Ekanayake, T. Prefi, and T. Bauernhansl, “Design-Framework for Product-Service Systems in Subscription-Based Business Models,” in Proceedings of the Conference on Production Systems and Logistics : Instituto Superior de Engenharia do Porto (ISEP) Porto, Portugal. 14th - 17th April 2026. Proceedings, Offenburg: publish-Ing, 2026, pp. 352–364. doi: 10.15488/20998.
    33. A. Shoshi, M. Sonnenberg, R. Miehe, and T. Bauernhansl, “Architectures of biointelligent value creation systems: A systematic literature review and typology,” Journal of Manufacturing Systems, vol. 88, pp. 279–297, 2026, doi: 10.1016/j.jmsy.2026.06.010.
    34. M. Schneider, B. Götz, D. Schel, and T. Bauernhansl, “Self-Description Of Heterogeneous System Landscapes In Manufacturing Environments,” in Proceedings of the Conference on Production Systems and Logistics : Instituto Superior de Engenharia do Porto (ISEP) Porto, Portugal. 14th - 17th April 2026. Proceedings, Offenburg: publish-Ing, 2026, pp. 43–53. doi: 10.15488/20972.
    35. L. Rauh, M.-R. Süner, D. Schel, and T. Bauernhansl, “AI Asset Management for Manufacturing (AIM4M): Development of a Process Model for Operationalization,” Procedia Computer Science, vol. 277, pp. 524–533, 2026, doi: 10.1016/j.procs.2026.02.094.
    36. C. Rietdorf et al., “An Automated Workflow for the PCF Calculation of Battery Cells,” in Safe and Sustainable Value Creation by Design : Proceedings of the 21st Global Conference on Sustainable Manufacturing (GCSM 2025) September 10-12, 2025, Bologna, Italy, Volume 1, Cham: Springer, 2026, pp. 189–196. doi: 10.1007/978-3-032-21157-6_22.
    37. M. Tröster et al., “Person-specific evaluation method for occupational exoskeletons - Biomechanical body heat map,” Applied Ergonomics : human factors in technology and society, vol. 132, pp. 104671, 30, 2026, doi: 10.1016/j.apergo.2025.104671.
    38. A. S. Calepso et al., “Exoskeletons and Augmented Reality: Opening Pathways to Improved Coordination in Collaborative Tasks,” in Human-Computer Interaction - INTERACT 2025 : 20th IFIP TC 13 International Conference, Belo Horizonte, Brazil, September 8-12, 2025, Proceedings, Part I, Cham: Springer, 2026, pp. 489–509. doi: 10.1007/978-3-032-04999-5_29.
    39. A. Formanek, “Ecological Cognition and Robotic Affordance Mixtures in HRI: The ‘Kickable’ challenge to Vice-Virtue Asymmetry in Moral Status : Chapter 9,” in Philosophy of Artificial Intelligence : The State of the Art, Cham: Springer, 2026, pp. 161–176. doi: 10.1007/978-3-032-10073-3_9.

  2. 2025

    1. B. Azmi and M. Bernreuther, “On the forward-backward method with nonmonotone linesearch for infinite-dimensional nonsmooth nonconvex problems,” Computational Optimization and Applications, vol. 91, pp. 1263–1308, 2025, doi: 10.1007/s10589-025-00684-x.
    2. J. Schiebl et al., “Quantification of the Mechanical Properties in the Human-Exoskeleton Upper Arm Interface During Overhead Work Postures in Healthy Young Adults,” Sensors, vol. 25, Art. no. 15, 2025, doi: 10.3390/s25154605.
    3. J. Gram, J. Drees, S. Stambera, M. Jestädt, M. Di Cagno, and T. Bauernhansl, “Real-Time Anomaly Detection in Manufacturing: A Data-Driven Approach to Enhancing Production Efficiency through Online KPI Monitoring,” Procedia CIRP, vol. 134, pp. 145–150, 2025, doi: 10.1016/j.procir.2025.03.047.
    4. M. Rentschler, S. Kiemel, and R. Miehe, “Requirement Engineering for effectively Assessing and Prioritizing Emission Reduction Measures to achieve Net Zero in Enterprises,” Procedia CIRP, vol. 135, pp. 1009–1014, 2025, doi: 10.1016/j.procir.2024.12.121.
    5. M. Schutzbach, R. Miehe, and A. Sauer, “Simplifying life cycle Assessment: Basic considerations for approximating product carbon footprints based on corporate carbon footprints,” Ecological Indicators, vol. 176, 2025, doi: 10.1016/j.ecolind.2025.113710.
    6. A. Formanek, “Is It Wrong to Kick Kickable? Sociomorphing and Robotic Affordance Mixtures for Differentiation in Moral Status,” in Social Robots with AI: Prospects, Risks, and Responsible Methods : Proceedings of Robophilosophy 2024 19-23 August 2024, Aarhus University, Denmark, and online, Amsterdam: IOS Press, 2025, pp. 199–209. doi: 10.3233/FAIA241505.
    7. D. Bargmann, W. Kraus, and M. Huber, “Force Plates for Analyzing, Recording and Teaching Forces of Dis-/Assembly Processes for Robot Programming-by-Demonstration,” in 2025 IEEE 21st International Conference on Automation Science and Engineering (CASE), IEEE, 2025, pp. 533–540. doi: 10.1109/case58245.2025.11163935.
    8. C. Buchner et al., “Machine learning-driven multi-objective parameter optimization for sustainable, efficient, and high-quality ultrasonic wire bonding,” Journal of Intelligent Manufacturing, vol. 36, 2025, doi: 10.1007/s10845-025-02615-3.
    9. C. Hennebold and M. Huber, “Graphical Causal Model based Bayesian Optimization for Industrial Process Parameterization,” in 2025 IEEE Conference on Artificial Intelligence (CAI) : 05.-07.05.2025, Santa Clara, California, USA, IEEE, 2025, pp. 33–38. doi: 10.1109/CAI64502.2025.00012.
    10. H. Monke, B. Sae-Chew, B. Fresz, and M. Huber, “From Confusion to Clarity: ProtoScore - A Framework for Evaluating Prototype-Based XAI,” in ACM FAccT ’25: Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency : 23.-26.06.2024, Athens, Greece, New York: Association for Computing Machinery, 2025, pp. 2215–2231. doi: 10.1145/3715275.3732151.
    11. A. Kernbach et al., “Automotive Wire Harness Connector Installation Using Skill-Based Robotic Programming,” in European Robotics Forum 2025 : Boosting the Synergies between Robotics and AI for a Stronger Europe, Cham: Springer, 2025, pp. 168–173. doi: 10.1007/978-3-031-89471-8_26.
    12. A. Benkiser, V. Kopp, U. Daub, and U. Schneider, “Biomechanical Evaluation of Shoulder Exoskeletons: Effects on the Shoulder Girdle Using a Test Rig,” in Proceedings of the 4th WearRAcon Europe Conference : 05.-06.11.2025, Düsseldorf, Stuttgart, 2025, pp. 36–37.
    13. F. Fuchs, M. Biller, and M. Huber, “Conformity Assessment of Machine Learning-Based Quality Assurance: A Gap Analysis,” in 2025 IEEE 23rd International Conference on Industrial Informatics : 12.-15.07.2025, Kunming, China, IEEE, 2025, p. 8. doi: 10.1109/INDIN64977.2025.11279536.
    14. M.-P. Radtke, M. Huber, and J. Bock, “RDF-based knowledge graph integration with deep learning for fault diagnosis,” Advanced Engineering Informatics, vol. 71, pp. 104422, 14, 2025, doi: 10.1016/j.aei.2026.104422.
    15. K. Engelhardt, J. Finkbeiner, L. Keefer, A. Renz, and R. Miehe, “Conceptual Basics of a Structured Method Toolbox for Continuous Improvement towards Positive Impact Factory Design,” Procedia CIRP, vol. 136, pp. 758–764, 2025, doi: 10.1016/j.procir.2025.08.129.
    16. T. Gramberg, M. Schneider, E. Groß, M. Kellner, F. Mais, and T. Bauernhansl, “Enhancing Product Portfolios: Strategic Selection and Evaluation of Smart Services for Manufacturing Companies,” Journal of Production Systems and Logistics, vol. 5, 2025, doi: 10.15488/19019.
    17. P. Schrader, J. Heidelbach, R. Ungern-Sternberg, E. Groß, and T. Bauernhansl, “Organizing digital innovations,” Journal of Open Innovation: Technology, Market, and Complexity, vol. 11, Art. no. 3, 2025, doi: 10.1016/j.joitmc.2025.100615.
    18. H. Himmelstoß, M. Risling, J. Rohde, D. Shi, and T. Bauernhansl, “Developing And Piloting A Procedure For Systematically Deriving The Digital Twin Based On The Asset Administration Shell,” in Proceedings of the Conference on Production Systems and Logistics : University of San Ignacio de Loyola Lima, Peru. 18th - 21th March 2025 Proceedings, Offenburg: publish-Ing, 2025, pp. 103–113. doi: 10.15488/18860.
    19. F. Mais, T. Gramberg, E. Groß, M. Schneider, and T. Bauernhansl, “Developing a Business Model for Platform-Based Applications in SME Sawmills: a Systematic Approach,” Journal of Production Systems and Logistics, vol. 5, 2025, doi: 10.15488/19020.
    20. J. Wirth, T. Seidel, and T. Bauernhansl, “Development of a Framework for a Structural Analysis and Examination Regarding the Need for Service-oriented and Data-driven Business Models in Mechanical and Plant Engineering,” in Proceedings of the Conference on Production Systems and Logistics : University of San Ignacio de Loyola Lima, Peru. 18th - 21th March 2025 Proceedings, Offenburg: publish-Ing, 2025, pp. 257–267. doi: 10.15488/18872.
    21. N. E. Bances Purizaca, V. Dalvi, U. Schneider, and T. Bauernhansl, “Deploying a Transformer-Based Model in Microservices Architecture: An Approach for Real Time Body Pose Classification,” in Human Interaction and Emerging Technologies (IHIET-AI 2025): Artificial Intelligence and Future Applications : Proceedings of the 13th International Conference on Human Interaction & Emerging Technologies: Artificial Intelligence & Future Applications. 22.-24.04.2025, Costa Del Sol, Universidad de Málaga, Spain, 2025, pp. 290–300. doi: 10.54941/ahfe1005921.
    22. D. Ranke and T. Bauernhansl, “Identification of Planning Periods Based on Time Series Characteristics for the Design of Matrix Production Systems,” Procedia Computer Science, vol. 253, pp. 1055–1062, 2025, doi: 10.1016/j.procs.2025.01.167.
    23. M. Zürn et al., “Robotic Wiring Harness Bin Picking Solution Using a Deep-Learning-Based Spline Prediction and a Multi-stereo Camera Setup,” in Advances in Automotive Production Technology - Digital Product Development and Manufacturing : Stuttgart Conference on Automotive Production (SCAP2024), Cham: Springer, 2025, pp. 319–334. doi: 10.1007/978-3-031-88831-1_25.
    24. M. Liewald, B. Vogel-Heuser, T. Bergs, M. Huber, and P. Kröger, “Advancing data-driven process modeling in metal forming,” at Automatisierungstechnik : Methoden und Anwendungen der Steuerungs-, Regelungs- und Informationstechnik, vol. 73, Art. no. 3, 2025, doi: 10.1515/auto-2024-0118.
    25. S. Durnagöz, M. Berg, M. Mayer, and M. Huber, “Direct process control in resistance spot welding in the series production line,” in 2025 11th International Conference on Control, Automation and Robotics : April 18-20, 2025, Kyoto, Japan, Piscataway, NJ: IEEE, 2025, pp. 399–404. doi: 10.1109/ICCAR64901.2025.11072981.
    26. M. Hagelüken, M. Huber, and M. Roth, “Data Efficient Prediction of excited-state properties using Quantum Neural Networks,” New Journal of Physics, vol. 27, 2025, doi: 10.1088/1367-2630/add203.
    27. D. Shi, O. Meyer, M. Oberle, and T. Bauernhansl, “Dual data mapping with fine-tuned large language models and asset administration shells toward interoperable knowledge representation through Predictive Process Stability,” Robotics and Computer-Integrated Manufacturing, vol. 91, 2025, doi: 10.1016/j.rcim.2024.102837.
    28. R. C. Garcia et al., “Synergies Between Extended Producer Responsibility and Digital Product Passport in the Path to Sustainable Production,” Procedia CIRP, vol. 136, pp. 516–521, 2025, doi: 10.1016/j.procir.2025.08.089.
    29. M. Sonnenberg, R. Miehe, S. Kiemel, and T. Bauernhansl, “Pre-structuring Product Development Challenges in the Context of Advanced Biomanufacturing,” Procedia CIRP, vol. 136, pp. 296–301, 2025, doi: 10.1016/j.procir.2025.08.052.
    30. A. Shoshi et al., “An Analysis of Monitoring Solutions for CAR T Cell Production,” Healthcare Technology Letters, vol. 12, Art. no. 1, 2025, doi: 10.1049/htl2.70012.
    31. K. Nordwig, M. Kunze, T. Bauernhansl, and W. Sopha, “Matrixproduktionssysteme: steuerungsrelevante Kennzahlen : Analyse von Störgrößen und Kennzahlen in Matrixproduktionssystemen,” wt Werkstattstechnik online, vol. 115, Art. no. 3, 2025, doi: 10.37544/1436-4980-2025-03-75.
    32. M.-A. Zöller, M. Lindauer, and M. Huber, “Auto-sktime: Automated Time Series Forecasting,” in Learning and Intelligent Optimization : 18th International Conference, LION 18, Ischia Island, Italy, June 9-13, 2024, Revised Selected Papers, Cham: Springer, 2025, pp. 456–471. doi: 10.1007/978-3-031-75623-8_35.
    33. F. Rapp, D. Kreplin, and M. Roth, “Reinforcement learning-based architecture search for quantum machine learning,” Machine Learning: Science and Technology, p. 16, 2025, doi: 10.1088/2632-2153/adaf75.
    34. H. Amirkhanian Namagerdi, M. Walker, U. Hanebeck, and M. Huber, “Bridging Bayesian Inference and Neural Network Training: Equivalence of KBNN and Statistical Linearization,” in 28th International Conference on Information Fusion : 07.-10.07.2025, Rio de Janeiro, Brazil, IEEE, 2025. doi: 10.23919/FUSION65864.2025.11124135.
    35. K. Leiner, K. K. Kollar, and M. Huber, “Learning from Failure: Improving the Machine Learning Performance in Laser Cutting Defect Detection,” Procedia CIRP, vol. 134, pp. 645–650, 2025, doi: 10.1016/j.procir.2025.02.176.
    36. P. Wagner, T. Nagel, P. Leube, and M. Huber, “Sample-Efficient Bayesian Transfer Learning for Online Machine Parameter Optimization,” in 2025 IEEE Conference on Artificial Intelligence (CAI) : 05.-07.05.2025, Santa Clara, California, USA, IEEE, 2025, pp. 9–15. doi: 10.1109/CAI64502.2025.00008.
    37. K. Abdou, A. Ibrahim, K. Binder, and M. Huber, “Nest smarter, not harder: a hybrid vision-based deep reinforcement learning agent for packing 2D irregular geometries by rotational placement,” Journal of Intelligent Manufacturing, vol. 36, 2025, doi: 10.1007/s10845-025-02620-6.
    38. A. Al Assadi, Y. Wang, F. Nägele, W. Kraus, and M. Huber, “Deep reinforcement learning based approach for the search and engagement phase of the robotic screw unfastening process,” Journal of Intelligent Manufacturing, vol. 36, 2025, doi: 10.1007/s10845-025-02643-z.
    39. M. Willmann, M. Albus, J. Schnabel, and M. Roth, “Application of quantum annealing for scalable robotic assembly line optimization: a case study,” Procedia CIRP, vol. 134, pp. 25–30, 2025, doi: 10.1016/j.procir.2025.01.099.
    40. M. Tröster et al., “Digitale altersgerechte Arbeitsgestaltung : Modellierung und altersgerechte ergonomische Gestaltung von manuellen Arbeitsprozessen,” wt Werkstattstechnik online, vol. 115, Art. no. 3, 2025, doi: 10.37544/1436-4980-2025-03-5.
    41. D. Shi, J. Li, O. Meyer, and T. Bauernhansl, “Enhancing retrieval-augmented generation for interoperable industrial knowledge representation and inference toward cognitive digital twins,” Computers in Industry, vol. 171, 2025, doi: 10.1016/j.compind.2025.104330.
    42. J. Wirth, M. Hermann, and T. Bauernhansl, “Navigating Towards New Businesses: An Implementation Framework for Data-Driven And Service-Oriented Business Models,” in Proceedings of the Conference on Production Systems and Logistics : University of San Ignacio de Loyola Lima, Peru. 18th - 21th March 2025 Proceedings, Offenburg: publish-Ing, 2025, pp. 233–256. doi: 10.15488/18871.
    43. J. Wirth, D. Neller, and T. Bauernhansl, “The Servitization Ecosystem Canvas (SEC): Towards a framework for developing service-oriented and data-driven business models in ecosystems,” Procedia CIRP, vol. 134, pp. 360–365, 2025, doi: 10.1016/j.procir.2025.02.139.
    44. Q. Ruan, D. Shi, and T. Bauernhansl, “Fine-tuning large language models with contrastive margin ranking loss for selective entity matching in product data integration,” Advanced Engineering Informatics, vol. 67, 2025, doi: 10.1016/j.aei.2025.103538.
    45. L. Rödel, J. Krebs, G. Müller, and T. Bauernhansl, “Adaptable Control System for Electrode Wet Coating in Lithium-Ion Battery Cell Production: A Systematic Literature Review,” in Proceedings of the Conference on Production Systems and Logistics : University of San Ignacio de Loyola Lima, Peru. 18th - 21th March 2025 Proceedings, Offenburg: publish-Ing, 2025, pp. 35–49. doi: 10.15488/18853.
    46. M. Rentschler, S. Hohmann, P. Heuermann, L. Valenti, and R. Miehe, “Designing innovation ecosystems for biointelligent value creation - Identification of promising technology fields and pioneer countries,” Journal of Open Innovation: Technology, Market, and Complexity, vol. 11, Art. no. 1, 2025, doi: 10.1016/j.joitmc.2025.100484.
    47. L. Keefer, S. Schmid, R. C. Garcia, A.-K. Nuffer, and R. Miehe, “Structured derivation of measures to optimize digital sustainability management in companies - A case study on how to make a supply chain more sustainable,” Procedia CIRP, vol. 135, pp. 527–532, 2025, doi: 10.1016/j.procir.2024.12.047.
    48. V. Hofmann, C. Maufroy, U. Schneider, and P. Pott, “Investigating Bimanual Tasks to Characterize Activities of Daily Living,” in Converging Clinical and Engineering Research on Neurorehabilitation V : Proceedings of the 6th International Conference on Neurorehabilitation (ICNR 2024), November 5-8, 2024, La Granja, Spain - Volume 1, Cham: Springer, 2025, pp. 208–212. doi: 10.1007/978-3-031-77588-8_41.
    49. K. Rösch, T. Mayer, and S. T. Karnick, “Environmentally friendly manufacturing of grinding wheels from nonwoven abrasive waste: method and performance evaluation,” The International Journal of Advanced Manufacturing Technology, 2025, doi: 10.1007/s00170-025-16456-0.
    50. W. Ilg et al., “iAssistADL: Intelligent assistive device for patients with neurodegenerative movement disorder: Concepts and first implementations,” in 2025 International Conference On Rehabilitation Robotics (ICORR 2025) : 12.-16.05.2025, Chicago, USA, IEEE, 2025, pp. 443–449. doi: 10.1109/ICORR66766.2025.11063050.
    51. M. Albus, T. Hornek, W. Kraus, and M. Huber, “Towards scalability for resource reconfiguration in robotic assembly line balancing problems using a modified genetic algorithm,” Journal of Intelligent Manufacturing, vol. 36, Art. no. 2, 2025, doi: 10.1007/s10845-023-02292-0.
    52. T. Körner, M. C. May, and M. Huber, “LLMs as Data Preprocessors for Natural Language Processing in Manufacturing,” in 30th IEEE International Conference on Emerging Technologies and Factory Automation : 09.-12.09.09.2025, Porto, Portugal, IEEE, 2025. doi: 10.1109/ETFA65518.2025.11205804.
    53. B. Fresz, D. Brajovic, and M. Huber, “AI Certification: Empirical Investigations into Possible Cul-De-Sacs and Ways Forward,” in Symposium on Scaling AI Assessments : 30.09.-01.10.2024, Cologne, Saarbrücken and Wadern: Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2025, pp. 13:1–13:4. doi: 10.4230/OASIcs.SAIA.2024.13.
    54. C. Hennebold, J. Krauß, and M. Huber, “Synthetic Data Generation Using Causal Models for Injection Molding Processes,” Procedia CIRP, vol. 134, pp. 831–836, 2025, doi: 10.1016/j.procir.2025.02.202.
    55. T. Eberhardt, F. Fuchs, W. Schaaf, M. Huber, and A. Verl, “Towards Automated or Assisted Requirements Extraction and Analysis Using an LLM-based Multi-Agent System: A Case Study,” in IEEE 31st International Conference on Engineering, Technology and Innovation (ICE 2025) : AI-driven Industrial Transformation: Digital Leadership in Technology, Engineering, Innovation & Entrepreneurship. 16.-19.06.2025, Valencia, Spain, IEEE, 2025. doi: 10.1109/ICE/ITMC65658.2025.11106535.
    56. K. Abdou, Y. Bao, M. P. Jones, N. Kroell, and T. O. McDonald, “Artificial intelligence as an agent of circular transition : Curated Dialogue,” Resources, Conservation & Recycling Advances, 2025, doi: 10.1016/j.resconrec.2025.108615.
    57. J. Yu, T. Pychynski, and M. Huber, “Multi-Domain Causal Discovery and Downstream Applications in Manufacturing Systems,” in 2025 IEEE Sensor Data Fusion: Trends, Solutions, Applications (SDF) : 24.-26.11.2025, Bonn, IEEE, 2025, p. 8. doi: 10.1109/SDF67080.2025.11330694.
    58. D. Brajovic, D. Kreplin, and M. Huber, “How Data-Related AI Research can Support Technical Solutions for Regulatory Compliance,” in Workshop on Regulatable ML at the 39th Conference on Neural Information Processing Systems (NeurIPS 2025) : 02.-07.12.2025, San Diego, USA, San Diego, USA, 2025, p. 23.
    59. C. Glaubitz, M. Rothgänger, H. Monke, E. Ortlieb, J. Peddinghaus, and K. Brunotte, “Grundlagen und Potenziale für Data-Mining-Anwendungen in der In-line-Messung beim Gesenkschmieden,” at Automatisierungstechnik : Methoden und Anwendungen der Steuerungs-, Regelungs- und Informationstechnik, vol. 73, Art. no. 4, 2025, doi: 10.1515/auto-2024-0138.
    60. J. Schuhmacher and V. Hummel, “Concept For Enabling Intralogistics Considering R-Strategies Related Processes In Circular Manufacturing Systems,” in Proceedings of the Conference on Production Systems and Logistics : University of San Ignacio de Loyola Lima, Peru. 18th - 21th March 2025 Proceedings, Offenburg: publish-Ing, 2025, pp. 429–437. doi: 10.15488/18887.
    61. A.-K. Nuffer, C. Schmidt, S. Biggel, S. Deckelmann, R. Schneider, and R. Miehe, “Towards Evaluating Material Compliance Maturity in Manufacturing Companies - Conceptual Basics of a Model-based Assessment Approach,” Procedia CIRP, vol. 135, pp. 1088–1093, 2025, doi: 10.1016/j.procir.2024.12.105.
    62. S. Schmid, L. Keefer, R. C. Garcia, A.-K. Nuffer, and R. Miehe, “A self-assessment framework to determine the status quo of enterprises in digital sustainability management,” Procedia CIRP, vol. 135, pp. 1082–1087, 2025, doi: 10.1016/j.procir.2024.12.104.
    63. J. Wirth, P. Kallen, and T. Bauernhansl, “Adaptation and Operationalization of a Process Model for the Introduction of Service-Oriented and Data-Driven Business Models in Mechanical and Plant Engineering,” Procedia CIRP, vol. 134, pp. 384–389, 2025, doi: 10.1016/j.procir.2025.02.140.
    64. C. Letzgus, J. A. Kirsch, and T. Bauernhansl, “Data-Driven Approach for a Continuous Information Flow in a Closed-Loop Supply Chain,” in Sustainable Manufacturing as a Driver for Growth : Proceedings of the 19th Global Conference on Sustainable Manufacturing, December 4-6, 2023, Buenos Aires, Argentina, Cham, Schweiz: Springer, 2025, pp. 509–515. doi: 10.1007/978-3-031-77429-4_56.
    65. J. Schiebl et al., “Performance characterization of a novel semi-active exoskeleton for overhead work,” Wearable Technologies, vol. 6, 2025, doi: 10.1017/wtc.2025.10019.
    66. R. Miehe, “Life cycle theory of value creation - context and perspectives,” Procedia CIRP, vol. 135, pp. 732–737, 2025, doi: 10.1016/j.procir.2024.12.081.
    67. F. Jordan et al., “Barrel Instance Segmentation and Grasping for (Semi-)Autonomous Excavators,” in Seventeenth International Conference on Machine Vision (ICMV 2024) : 10.-13.10.2024, Edinburg, UK, Bellingham, USA: SPIE, 2025. doi: 10.1117/12.3055172.
    68. C. Glaubitz, J. Raible, H. Monke, M. Rothgänger, J. Peddinghaus, and K. Brunotte, “KI-gestützte Prozessoptimierung in der Massivumformung,” ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb : Organ des VDI-Kompetenzfeldes Informationstechnik, vol. 120, Art. no. SH, 2025, doi: 10.1515/zwf-2024-0124.
    69. R. Wang, X. Hou, S. Schmedding, and M. Huber, “STAY Diffusion: Styled Layout Diffusion Model for Diverse Layout-to-Image Generation,” in 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) : 28.02.-04.03.2025, Tucson, USA, IEEE, 2025, pp. 3855–3865. doi: 10.1109/WACV61041.2025.00379.
    70. F. Jordan, J. Lindermayr, R. Bormann, and M. Huber, “Low-effort Iterative Dataset Generation Pipeline for Unknown Object Instance Segmentation,” in 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems : 19.-25.10.2025, Hangzhou, China, IEEE, 2025, pp. 18612–18619. doi: 10.1109/IROS60139.2025.11246370.

  3. 2024

    1. P. Berkhan, S. Kärcher, and T. Bauernhansl, “Framework for the Classification of Real-time Locating System (RTLS) Use Cases in Matrix Production Systems,” Procedia CIRP, vol. 130, pp. 1346–1351, 2024, doi: 10.1016/j.procir.2024.10.250.
    2. J. Yu, T. Pychynski, K. S. Barsim, and M. Huber, “Causal Knowledge in Data Fusion: Systematic Evaluation on Quality Prediction and Root Cause Analysis,” in 27th International Conference on Information Fusion : 07.-11.07.2024, Venice, Italy, IEEE, 2024. doi: 10.23919/FUSION59988.2024.10706429.
    3. D. Brajovic and M. Huber, “Vertiefung: Anhaltspunkte für eine technische Umsetzung der KI-Verordnung,” in Praxishandbuch KI und Recht : Rechtliche Aspekte beim Einsatz von Künstlicher Intelligenz - inkl. der neuen KI-Verordnung der EU, Freiburg and München and Stuttgart: Haufe Group, 2024, pp. 215–239.
    4. K. Leiner, J. Peter, and M. Huber, “Detection and Handling of Laser Cutting Parameter Changes during the Deployment of Machine Learning Models,” Procedia CIRP, vol. 130, pp. 126–132, 2024, doi: 10.1016/j.procir.2024.10.066.
    5. J. Wirth, M. Höding, L. Hanselmann, O. Schöllhammer, T. Bauernhansl, and J. Puchan, “Development of a methodology for the derivation of technical requirements for (cyber-) physical product-service systems in service-oriented business models,” Procedia CIRP, vol. 128, pp. 816–821, 2024, doi: 10.1016/j.procir.2024.04.024.
    6. M. Oberle and T. Bauernhansl, “Leveraging OpenAPI for Microservice Decomposition: A Comparative Study on Features, Encodings and Algorithms on a real MES,” in 2024 International Conference on Artificial Intelligence in Information and Communication : 19.-22.02.2024, Osaka, Japan, Piscataway, NJ, USA: IEEE Press, 2024, pp. 785–791. doi: 10.1109/ICAIIC60209.2024.10463497.
    7. J. Schuhmacher, V. Hummel, D. Palm, and T. Bauernhansl, “Approach For Autonomous Control Of Intralogistics Considering Deterministic And Probabilistic Material Demand Information In Flexible Production Systems,” in Proceedings of the Conference on Production Systems and Logistics : 9th - 12th July 2024 College of Engineering - University of Hawai’i at Mânoa Honolulu, Hawaii, USA, publish-Ing, 2024, pp. 227–237. doi: 10.15488/17715.
    8. J. Wirth, B. Weiß, T. Bauernhansl, and J. Metternich, “Strategies For Cross-Company Collaboration From An OEM’s Perspective In The Context Of Digital Ecosystems,” in Proceedings of the Conference on Production Systems and Logistics : 9th - 12th July 2024 College of Engineering - University of Hawai’i at Mânoa Honolulu, Hawaii, USA, publish-Ing, 2024, pp. 709–722. doi: 10.15488/17759.
    9. D. Shi, P. Liedl, and T. Bauernhansl, “Interoperable information modelling leveraging asset administration shell and large language model for quality control toward zero defect manufacturing,” Journal of Manufacturing Systems, vol. 77, pp. 678–696, 2024, doi: 10.1016/j.jmsy.2024.10.011.
    10. C. Rietdorf, S. Ziehn, S. Giunta, R. Miehe, and A. Sauer, “Environmental Assessment of Metal Chip Recycling - Quantification of Mechanical Processing’s Global Warming Potential,” Procedia CIRP, vol. 122, pp. 241–246, 2024, doi: 10.1016/j.procir.2024.02.009.
    11. R. Hauf, R. Miehe, O. Schöllhammer, and T. Bauernhansl, “Conceptional Thoughts on a Holistic Support Tool for Biointelligence-Related Strategic Decisions in Enterprises,” Procedia CIRP, vol. 125, pp. 160–165, 2024, doi: 10.1016/j.procir.2024.08.028.
    12. N. E. Bances Purizaca, U. Schneider, B. Garcia Ayala, J. Siegert, and T. Bauernhansl, “Collaborative Tasks in Construction: A Model for Human-Exoskeleton Interaction to Minimize Muscle Exertion,” in Production at the Leading Edge of Technology : Proceedings of the 13th Congress of the German Academic Association for Production Technology (WGP), Freudenstadt, November 2023, Cham, Schweiz: Springer, 2024, pp. 34–43. doi: 10.1007/978-3-031-47394-4_4.
    13. X. Wu, E. Wedernikow, and M. Huber, “Data-Efficient Uncertainty-Guided Model-Based Reinforcement Learning with Unscented Kalman Bayesian Neural Networks,” in 2024 American Control Conference (ACC) : 10.-12.07.2024, Toronto, Canada, IEEE, 2024, pp. 104–110. doi: 10.23919/ACC60939.2024.10644690.
    14. B. Alt et al., “RoboGrind: Intuitive and Interactive Surface Treatment with Industrial Robots,” in 2024 IEEE International Conference on Robotics and Automation (ICRA 2024) : 13.-17.05.2023, Yokohama, Japan, 2024, pp. 2140–2146. doi: 10.1109/ICRA57147.2024.10611143.
    15. K. Abdou et al., “Nestability: A deep learning oracle for nesting scrap prediction in manufacturing industry,” Resources, Conservation & Recycling Advances, vol. 205, 2024, doi: 10.1016/j.resconrec.2024.107540.
    16. K. Abdou, O. Mohammed, G. Eskandar, A. Ibrahim, P.-A. Matt, and M. Huber, “Smart Nesting: Estimating Geometrical Compatibility in the Nesting Problem Using Graph Neural Networks,” Journal of Intelligent Manufacturing, vol. 35, Art. no. 6, 2024, doi: 10.1007/s10845-023-02179-0.
    17. C. Hennebold, M. M. Islam, J. Krauß, and M. Huber, “Combination of Process Mining and Causal Discovery Generated Graph Models for Comprehensive Process Modeling,” Procedia CIRP, vol. 130, pp. 1296–1302, 2024, doi: 10.1016/j.procir.2024.10.242.
    18. M. Walker, H. Amirkhanian Namagerdi, M. Huber, and U. Hanebeck, “Trustworthy Bayesian Perceptrons,” in 27th International Conference on Information Fusion : 07.-11.07.2024, Venice, Italy, IEEE, 2024. doi: 10.23919/FUSION59988.2024.10706490.
    19. D. Bargmann, W. Kraus, and M. Huber, “Enabling Maintainablity of Robot Programs in Assembly by Extracting Compositions of Force- and Position-Based Robot Skills from Learning-from-Demonstration Models,” in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems : 14.-18.10.2024, Abu Dhabi, UAE, IEEE, 2024, pp. 9227–9234. doi: 10.1109/IROS58592.2024.10802802.
    20. F. Stöckl et al., “Autonomous Surface Grinding of Wind Turbine Blades,” in Intelligent Autonomous Systems 18 : Volume 2 Proceedings of the 18th International Conference IAS18-2023, Cham, Schweiz: Springer Nature, 2024, pp. 451–457. doi: 10.1007/978-3-031-44981-9_38.
    21. R. Wang, S. Schmedding, and M. Huber, “Improving the Effectiveness of Deep Generative Data,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) : 04.-08.01.2024, Waikoloa, Hawaii, USA, 2024, pp. 4910–4920. doi: 10.1109/WACV57701.2024.00485.
    22. J. Yu, T. Pychynski, and M. Huber, “Causal Knowledge in Data Fusion Subject to Latent Confounding and Measurement Error,” in 2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) : 04.-06.09.2024, Pilsen, Czechia, IEEE, 2024. doi: 10.1109/MFI62651.2024.10705789.
    23. Y. Baumgarten et al., “Biology-Technology Interfaces - Refining the Core Principle of Biointelligent Systems,” Procedia CIRP, vol. 126, pp. 875–880, 2024, doi: 10.1016/j.procir.2024.08.277.
    24. P. Schrader et al., “Next Stop Metaverse: Opportunities and Barriers of AI-based Virtual Worlds for Companies,” in Portland International Conference on Management of Engineering and Technology (PICMET) : 04.-08.08.2024, Portland, USA, IEEE, 2024. doi: 10.23919/PICMET64035.2024.10653234.
    25. R. Hall, S. Schumacher, and T. Bauernhansl, “Towards Holistic Work System Design: Concept for a Method to Analyze, Represent and Evaluate Industrial Sociotechnical Work Systems,” in Human Factors in Design, Engineering, and Computing : Proceedings of the AHFE International Conference on Human Factors in Design, Engineering, and Computing (AHFE 2024 Hawaii Edition), Hawaii, USA 8-10, December, 2024, 2024, pp. 1771–1781. doi: 10.54941/ahfe1005744.
    26. M. Rettenmeier et al., “Model-driven evaluation of exoskeletons for efficient traction battery dismantling,” Procedia CIRP, vol. 130, pp. 1850–1855, 2024, doi: 10.1016/j.procir.2024.10.327.
    27. J. Gram, B. Sai, and T. Bauernhansl, “Root Cause Analysis Of Productivity Losses In Manufacturing Systems Utilizing Ensemble Machine Learning,” in Proceedings of the Conference on Production Systems and Logistics : 9th - 12th July 2024 College of Engineering - University of Hawai’i at Mânoa Honolulu, Hawaii, USA, publish-Ing, 2024, pp. 368–379. doi: 10.15488/17728.
    28. E. Gamero, A. Shoshi, J. Full, A. Sauer, and R. Miehe, “Data Management in Biorefineries: Conceptual Thoughts on Lean Digital Twinning,” Procedia CIRP, vol. 125, pp. 48–53, 2024, doi: 10.1016/j.procir.2024.08.009.
    29. K. Nordwig, S. Kärcher, and T. Bauernhansl, “Steuerungsbedarfe im operativen Betrieb von Matrixproduktionssystemen : Hohe Komplexität von Matrixproduktionssystemen erfordert eine neue Funktion zur Steuerung,” wt Werkstattstechnik online, vol. 114, Art. no. 9, 2024, doi: 10.37544/1436-4980-2024-09-99.
    30. S. Durnagöz, M. Huber, M. Mayer, and P. Reimann, “An Approach to Inline Monitoring of the Electrode State in Resistance Spot Welding,” International Journal of Electrical and Electronic Engineering & Telecommunications, vol. 13, Art. no. 3, 2024, doi: 10.18178/ijeetc.13.3.245-251.
    31. O. Schwarz et al., “A Conceptual Basis for Reducing the Number of Complex Multi-Parametric Experiments in Bio-Production Engineering via Artificial Intelligence,” in CIRP BioM 2024, S. Ihlenfeldt, Ed., Elsevier, 2024, pp. 237–242. doi: 10.1016/j.procir.2024.08.009.
    32. C. Kaucher, K. Erlach, and T. Bauernhansl, “Conception Of Future-proof Factory Buildings Via Software-based Scenario Creation And Evaluation,” in Proceedings of the Conference on Production Systems and Logistics : 9th - 12th July 2024 College of Engineering - University of Hawai’i at Mânoa Honolulu, Hawaii, USA, publish-Ing, 2024, pp. 107–120. doi: 10.15488/17705.
    33. K. Protte-Freitag, S. Gotzig, H. Rothe, O. Schwarz, N. Silber, and R. Miehe, “Enzyme-Assisted Circular Additive Manufacturing as an Enabling Technology for a Circular Bioeconomy - A Conceptual Review,” Sustainability, vol. 16, Art. no. 5, 2024, doi: 10.3390/su16052167.
    34. F. Graf, J. Lindermayr, B. Graf, W. Kraus, and M. Huber, “HIPer: A Human-Inspired Scene Perception Model for Multifunctional Mobile Robots,” IEEE Transactions on Robotics, vol. 40, pp. 4668–4683, 2024, doi: 10.1109/TRO.2024.3420799.
    35. P. Takenaka, J. Maucher, and M. Huber, “ViPro: Enabling and Controlling Video Prediction for Complex Dynamical Scenarios using Procedural Knowledge,” in Neural-Symbolic Learning and Reasoning : 18th International Conference, NeSy 2024, Barcelona, Spain, September 9-12, 2024, Proceedings, Part I, Cham: Springer, 2024, pp. 62–83. doi: 10.1007/978-3-031-71167-1_4.
    36. C. Buchner, C. Seidler, M. Huber, H. Eigenbrod, H.-G. von Ribbeck, and F. Schlicht, “Machine learning-based shear force quality prediction of ultrasonic wire bonds: utilizing process data and machine data without additional sensors,” The International Journal of Advanced Manufacturing Technology, vol. 133, Art. no. 11–12, 2024, doi: 10.1007/s00170-024-14055-z.
    37. M.-L. Schumacher and M. Huber, “Probabilistic Global Robustness Verification of Arbitrary Supervised Machine Learning Models,” in 27th International Conference on Information Fusion : 07.-11.07.2024, Venice, Italy, IEEE, 2024. doi: 10.23919/FUSION59988.2024.10706397.
    38. A. Yaman, “Kabelbaummontage mit KI : Flexible Komponenten mithilfe kollaborativer Roboter handhaben,” VDI-Z, vol. 166, Art. no. 3, 2024.
    39. M. Sapounaki et al., “Quantifying human upper limb stiffness responses based on a computationally efficient neuromusculoskeletal arm model,” in IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics : 01.-04.09.2024, Heidelberg, IEEE, 2024, pp. 514–519. doi: 10.1109/BioRob60516.2024.10719776.
    40. M. Schneider, B. Götz, and T. Bauernhansl, “A Framework for Software Deployment in Manufacturing Environments,” in 57th CIRP Conference on Manufacturing Systems 2024 (CMS 2024), G. Putnik, Ed., in Procedia CIRP. Elsevier, 2024, pp. 1043–1048. doi: 10.1016/j.procir.2024.10.204.
    41. B. Götz, M. Schneider, and T. Bauernhansl, “Concept of Event-based AAS Transformation Engine,” Procedia CIRP, vol. 130, pp. 1244–1249, 2024, doi: 10.1016/j.procir.2024.10.234.
    42. F. Mais and T. Bauernhansl, “Decarbonization Drivers and Their Impact on Business Models in the Energy-Intensive Manufacturing Industry (EIMI),” Sustainability, vol. 16, Art. no. 11, 2024, doi: 10.3390/su16114836.
    43. J. Maier, E. Colangelo, T.-F. Hinrichsen, D. K. Tran, H.-H. Wiendahl, and M. Huber, “Simulation Discovery and Semi-Automatic Scenario Generation for Evaluation of Turbulence in Production Systems,” Procedia CIRP, vol. 130, pp. 1623–1631, 2024, doi: 10.1016/j.procir.2024.10.292.
    44. M. Risling, M. Oberle, and T. Bauernhansl, “Analyzing The Purpose And Technologies Of Digital Twins In Distributed Manufacturing: A Systematic Literature Review,” Procedia Computer Science, vol. 232, pp. 368–376, 2024, doi: 10.1016/j.procs.2024.01.036.
    45. J. Maier and H.-H. Wiendahl, “Anticipating VUCA by Utilizing the Potential of Technological and Logistical Degrees of Freedom,” in Advances in Production Management Systems : Production Management Systems for Volatile, Uncertain, Complex, and Ambiguous Environments. 43rd IFIP WG 5.7 International Conference, APMS 2024, Chemnitz, Germany, September 8-12, 2024, Proceedings, Part VI, Cham: Springer Nature, 2024, pp. 123–137. doi: 10.1007/978-3-031-71645-4_9.
    46. M.-A. Berchtold, K. Erlach, and T. Bauernhansl, “A Requirement-Oriented Site Role Concept For Factory Planning - A Systematic Review,” in Proceedings of the Conference on Production Systems and Logistics : 9th - 12th July 2024 College of Engineering - University of Hawai’i at Mânoa Honolulu, Hawaii, USA, publish-Ing, 2024, pp. 165–179. doi: 10.15488/17710.
    47. T. Gramberg, T. Bauernhansl, and A. Eggert, “Disruptive Factors in Product Portfolio Management: An Exploratory Study in B2B Manufacturing for Sustainable Transition,” Sustainability, vol. 16, Art. no. 11, 2024, doi: 10.3390/su16114402.
    48. N. E. Bances Purizaca, U. Schneider, J. Siegert, and T. Bauernhansl, “Enhancing Ergonomics in Construction Industry Environments: A Digital Solution With Scalable Event-Driven Architecture,” in Human Factors and Systems Interaction : Proceedings of the 15th International Conference on Applied Human Factors and Ergonomics (AHFE 2024) and the Affiliated Conferences. 24.-27.07.2024, Nice, France, 2024, pp. 103–113. doi: 10.54941/ahfe1005358.
    49. M.-P. Radtke, M. Huber, and J. Bock, “Encoding Machine Phase Information into Heterogeneous Graphs for Adaptive Fault Diagnosis,” in 29th IEEE International Conference on Emerging Technologies and Factory Automation : 10.-13.09.09.2024, Padova, Italy, IEEE, 2024. doi: 10.1109/ETFA61755.2024.10711048.
    50. S. Durnagöz et al., “A Novel Approach for Unsupervised Sensor Error Classification,” in 57th CIRP Conference on Manufacturing Systems 2024 (CMS 2024), G. Putnik, Ed., Elsevier, 2024, pp. 206–213. doi: 10.1016/j.procir.2024.10.077.
    51. T. Mayr, M. Huber, R. Hensel, and M. Keil, “Process Optimization in Process Planning using a Multidimensional Approach in the Automotive Assembly,” Procedia CIRP, vol. 126, pp. 751–756, 2024, doi: 10.1016/j.procir.2024.08.303.
    52. C. Rietdorf, C. de La Rúa, S. Kiemel, and R. Miehe, “Cradle-to-gate life cycle assessment of cylindrical sulfide-based solid-state batteries,” The International Journal of Life Cycle Assessment, vol. 2024, 2024, doi: 10.1007/s11367-024-02355-1.
    53. D. Brajovic and M. Huber, “Einführung: KI und die wichtigsten damit zusammenhängenden Begriffe und Probleme,” in Praxishandbuch KI und Recht : Rechtliche Aspekte beim Einsatz von Künstlicher Intelligenz - inkl. der neuen KI-Verordnung der EU, Freiburg and München and Stuttgart: Haufe Group, 2024, pp. 17–38.
    54. K. Nordwig, P. Berkhan, T. Bauernhansl, and M. Maurer, “Automatisierte Datenanalyse in der manuellen Montage : Praxisperspektive: Potenziale von automatisierten Datenanalysen für die manuelle Montage,” wt Werkstattstechnik online, vol. 114, Art. no. 11–12, 2024, doi: 10.37544/1436-4980-2024-11-12-24.
    55. E. Gamero, S. Ruppert, R. Miehe, and A. Sauer, “Process Model and Life Cycle Assessment of Biorefinery Concept Using Agricultural and Industrial Residues for Biohydrogen Production,” Energies, vol. 17, Art. no. 17, 2024, doi: 10.3390/en17174282.
    56. B. Leudesdorff, L. Strümpler, T. Dobosz, C. Maufroy, U. Schneider, and T. Bauernhansl, “Sensor System for Real-time Classification of Manual Construction Tasks with Power Tools for Exoskeleton Control,” in 2024 IEEE International Conference on Systems, Man, and Cybernetics : 06.-10.10.2024, Kuching, Sarawak, Malaysia, 2024, pp. 1181–1186. doi: 10.1109/SMC54092.2024.10831877.
    57. D. Ranke, M. Trierweiler, P. Berkhan, M. Münnich, and T. Bauernhansl, “Definition and Description of Matrix Production Systems,” Procedia CIRP, vol. 130, pp. 1875–1880, 2024, doi: 10.1016/j.procir.2024.10.331.
    58. M. Maleki, V. Hofmann, and U. Schneider, “Vergleich der Wirksamkeit von robotergestützter Rehabilitation und konventioneller Therapie zur Verbesserung der oberen Extremitätenfunktion bei Kindern und jugendlichen mit Zerebralparese: eine Literaturübersicht,” Orthopädie Technik, Art. no. 12, 2024.
    59. A. Al Assadi, L. Höltge, M. Brower-Rabinowitsch, F. Nägele, W. Kraus, and M. Huber, “Towards Circular Economy: Process Description, Requirements, and Data Set for Robot-based Disassembly of Small Electrical Appliances,” Procedia CIRP, vol. 130, pp. 903–908, 2024, doi: 10.1016/j.procir.2024.10.183.
    60. V. Hofmann, N. Bölke, C. Maufroy, U. Schneider, and P. Pott, “Development and evaluation of a passive lower body exoskeleton for agriculture,” in IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics : 01.-04.09.2024, Heidelberg, IEEE, 2024, pp. 496–501. doi: 10.1109/BioRob60516.2024.10719886.
    61. J. Wirth, S. Erb, F. Hoffmann, J. Metternich, and T. Bauernhansl, “Framework for the Introduction of Data-driven and Service-oriented Business Models in Mechanical and Plant Engineering Companies,” Procedia CIRP, vol. 130, pp. 1124–1129, 2024, doi: 10.1016/j.procir.2024.10.216.
    62. H. Himmelstoß, R. Hall, B. Vojanec, P. Thieme, and T. Bauernhansl, “Digital Twins and Software Services: Leveraging Product Data for Improved Product Development,” Procedia CIRP, vol. 128, pp. 472–477, 2024, doi: 10.1016/j.procir.2024.03.029.
    63. C. Rietdorf et al., “Leveraging Digital Twins for Real-Time Environmental Monitoring in Battery Manufacturing,” Procedia CIRP, vol. 130, pp. 749–754, 2024, doi: 10.1016/j.procir.2024.10.159.
    64. A. Shoshi et al., “A Flexible Digital Twin Framework for ATMP Production - Towards an efficient CAR T Cell Manufacturing,” Procedia CIRP, vol. 125, pp. 124–129, 2024, doi: 10.1016/j.procir.2024.08.022.
    65. V. Hofmann, C. Maufroy, P. Pott, and U. Schneider, “Results of a quantitative Delphi study investigating needs of people with Traumatic Brachial Plexus Injury,” Current Directions in Biomedical Engineering, vol. 10, Art. no. 4, 2024, doi: 10.1515/cdbme-2024-2077.
    66. J. Wirth, M. Schneider, L. Hanselmann, K. Fink, S. Nebauer, and T. Bauernhansl, “An Exploratory Analysis of the Current Status and Potential of Service-Oriented and Data Driven Business Models within the Sheet Metal Working Sector: Insights from Interview Based Research in Small and Medium-Sized Enterprises,” Sustainability, vol. 16, Art. no. 7, 2024, doi: 10.3390/su16072603.
    67. P. Berkhan, D. Ranke, and T. Bauernhansl, “Method for the Derivation of Flexible Process Modules,” Procedia Computer Science, vol. 232, pp. 1529–1537, 2024, doi: 10.1016/j.procs.2024.01.150.
    68. K. Leiner, T. Bosse, L. Keck, and M. Huber, “Explanation of the Acoustic Features for Detecting a Cut Interruption in the Laser Cutting Process,” Procedia CIRP, vol. 130, pp. 1801–1808, 2024, doi: 10.1016/j.procir.2024.10.319.
    69. B. Fresz, L. Lörcher, and M. Huber, “Classification Metrics for Image Explanations : Towards Building Reliable XAI-Evaluations,” in FAccT ’24: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency : 03.-06.06.2024, Rio de Janeiro, Brazil, New York: Association for Computing Machinery, 2024, p. 19. doi: 10.1145/3630106.3658537.
    70. T. Nagel and M. Huber, “Identifying Ordinary Differential Equations for Data-efficient Model-based Reinforcement Learning,” in 2024 International Joint Conference on Neural Networks (IJCNN) : 30.06.-05.07.2024, Yokohama, Japan, IEEE, 2024. doi: 10.1109/IJCNN60899.2024.10650369.
    71. J. Stahl, S. Zengl, A. Frommknecht, C. Jauch, and M. Huber, “Algorithmic assessment of drag on thermally cut sheet metal edges,” Technisches Messen : Plattform für Methoden, Systeme und Anwendungen in der Messtechnik, p. 15, 2024, doi: 10.1515/teme-2024-0090.
    72. S. Durnagöz, M. Mayer, and M. Huber, “An Approach for Expulsion Predicting in Resistance Spot Welding,” Procedia CIRP, vol. 130, pp. 1732–1738, 2024, doi: 10.1016/j.procir.2024.10.308.
    73. R. Miehe, “On the Concept of Decentralization in Biointelligent Manufacturing,” Procedia CIRP, vol. 125, pp. 296–301, 2024, doi: 10.1016/j.procir.2024.08.050.
    74. N. Silber et al., “Exploring the Potential of Residual Aspergillus Mycelium as a Sustainable Material for Additive Biomanufacturing,” Procedia CIRP, vol. 125, pp. 148–153, 2024, doi: 10.1016/j.procir.2024.08.026.
    75. J. Maier and H.-H. Wiendahl, “Integrierte Arbeitsplanung und Produktionssteuerung : Potenzialanalyse technologischer und logistischer Freiheitsgrade der Produktionssteuerung,” wt Werkstattstechnik online, vol. 114, Art. no. 6, 2024, doi: 10.37544/1436-4980-2024-06-103.
    76. M. Maier, M. Hentsch, C. Schillinger, J. Siegert, and D. Palm, “Echtzeitnahe Dokumentation von Treibhausgasemissionen auf Basis der Verwaltungsschale : Nachhaltigkeit in Produktion und Logistik,” wt Werkstattstechnik online, vol. 114, Art. no. 4, 2024, doi: 10.37544/1436-4980-2024-04-48.
    77. J. Charaja et al., “Generating Realistic Arm Movements in Reinforcement Learning: A Quantitative Comparison of Reward Terms and Task Requirements,” in IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics : 01.-04.09.2024, Heidelberg, IEEE, 2024, pp. 562–568. doi: 10.1109/BioRob60516.2024.10719719.
    78. T. Mayr et al., “Application of Machine Learning for Ergonomic Workplace Assessments in Assembly and Logistics to Identify Potential Use Cases for Exoskeleton,” Procedia CIRP, vol. 130, pp. 625–630, 2024, doi: 10.1016/j.procir.2024.10.139.
    79. A. Shoshi, B. Gündüz, and R. Miehe, “Identifying intelligent data utilization in bioprocesses: overview of current research activities, opportunities and barriers,” Procedia CIRP, vol. 126, pp. 869–874, 2024, doi: 10.1016/j.procir.2024.08.276.
    80. S. Schumacher, M. Hautzinger, R. Hall, and T. Bauernhansl, “Process Model For The Problem-based Identification Of Solutions In Lean Production Systems 4.0 Information In Flexible Production Systems,” in Proceedings of the Conference on Production Systems and Logistics : 9th - 12th July 2024 College of Engineering - University of Hawai’i at Mânoa Honolulu, Hawaii, USA, publish-Ing, 2024, pp. 613–627. doi: 10.15488/17750.
    81. E. Gamero, J. Full, R. Miehe, T. Bauernhansl, and A. Sauer, “Assessment of Novel Biorefinery Concepts for the Production of Biohydrogen and Value-Added Products from Industrial Waste Streams,” Procedia CIRP, vol. 126, pp. 881–886, 2024, doi: 10.1016/j.procir.2024.08.278.
    82. T. Mayr, R. Hensel, and M. Huber, “Integration of a Data-Driven Multi-Criteria Decision Support System for Productivity Enhancement in Manual Assembly Process Planning,” Procedia CIRP, vol. 130, pp. 1653–1658, 2024, doi: 10.1016/j.procir.2024.10.296.
    83. B. Fresz, E. Dubovitskaya, D. Brajovic, M. Huber, and C. Horz, “How should AI decisions be explained? Requirements for Explanations from the Perspective of European Law,” in Proceedings of the Seventh AAAI/ACM Conference on AI, Ethics, and Society : Vol. 7 (2024). October 21-23, 2024, San Jose, California, USA, Washington, DC, USA: AAAI Press, 2024, pp. 438–450.

  4. 2023

    1. F. Falkenau et al., “Catena-X - Online Steuerung und Simulation : Unternehmensübergreifende Supply-Chain-Simulation: Ein Ansatz zur Steigerung der Transparenz und Reaktionsfähigkeit in der Supply Chain,” ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb : Organ des VDI-Kompetenzfeldes Informationstechnik, vol. 118, Art. no. 4, 2023, doi: 10.1515/zwf-2023-1031.
    2. J. Full et al., “Perspectives of Biogas Plants as BECCS Facilities: A Comparative Analysis of Biomethane vs. Biohydrogen Production with Carbon Capture and Storage or Use (CCS/CCU),” Energies, vol. 16, pp. 5066, 16, 2023, doi: 10.3390/en16135066.
    3. T. Ackermann, R. Miehe, P. Reimann, B. Mitschang, R. Takors, and T. Bauernhansl, “A Cross-Disciplinary Training Concept for Future Technologists in the Dawn of Biointelligent Production Systems,” in 13th Conference on Learning Factories (CLF 2023) : 09.-11.05.2023, Reutlingen, Reutlingen, 2023, p. 6. doi: 10.2139/ssrn.4458051.
    4. J. Raible, C. Braun, and M. Huber, “Automatic Path Planning for Robotic Grinding and Polishing Tasks based on Point Cloud Slicing,” in ISR Europe 2023 : 56th International Symposium on Robotics, in cooperation with Fraunhofer IPA September 26 - 27, 2023 in Stuttgart, Berlin and Offenbach: VDE Verlag, 2023, pp. 382–389.
    5. J. L. Schmitt, D. M. Dörr, and T. Bauernhansl, “Collaborative Implementation of Product-Service Systems in Business Ecosystems - Empirical Investigation of Neutral Third Parties as a Success Factor,” in Conference on Production Systems and Logistics : International Conference, CPSL 2023, hosted at the Tecnológico de Monterrey (Tec de Monterrey), Querétaro, Mexico 28th February 2023 - 2nd March 2023 Proceedings, Offenburg: publish-Ing, 2023, pp. 523–532. doi: 10.15488/13470.
    6. D. Ranke and T. Bauernhansl, “Cost-Minimal Selection of Material Supply Strategies in Matrix Production Systems,” in Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus : Proceedings of FAIM 2022, June 19-23, 2022, Detroit, Michigan, USA, Cham, Schweiz: Springer, 2023, pp. 217–226. doi: 10.1007/978-3-031-18326-3_22.
    7. D. Bauer, D. Umgelter, A. Schlereth, T. Bauernhansl, and A. Sauer, “Complex Job Shop Simulation “CoJoSim” - A Reference Model for Simulating Semiconductor Manufacturing,” Applied Sciences, vol. 13, Art. no. 6, 2023, doi: 10.3390/app13063615.
    8. F. Falkenau et al., “Catena-X : Online Control and Simulation Cross-company Supply Chain Simulation,” Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 118, Art. no. 4, 2023, doi: 10.1515/zwf-2023-1031.
    9. P. Berkhan, S. Kärcher, and T. Bauernhansl, “Data Acquisition to Handle Complexity in Matrix Production Systems,” Procedia CIRP, vol. 120, pp. 261–266, 2023, doi: 10.1016/j.procir.2023.08.047.
    10. R. Miehe, Y. Baumgarten, and T. Bauernhansl, “Towards a Common Understanding of the Biointelligence Concept,” Procedia CIRP, vol. 120, pp. 1416–1421, 2023, doi: 10.1016/j.procir.2023.09.186.
    11. J. Schuhmacher and T. Bauernhansl, “Data-driven Prediction of Internal Turbulences in Production Using Synthetic Data,” in Conference on Production Systems and Logistics : International Conference, CPSL 2023, hosted at the Tecnológico de Monterrey (Tec de Monterrey), Querétaro, Mexico 28th February 2023 - 2nd March 2023 Proceedings, Offenburg: publish-Ing, 2023, pp. 189–198. doi: 10.15488/13438.
    12. J. Elstner, R. Schönhof, S. Tauber, and M. Huber, “Optimizing CAD Models with Latent Space Manipulation,” Procedia CIRP, vol. 119, pp. 650–655, 2023, doi: 10.1016/j.procir.2023.03.117.
    13. M. Veerappa, M. Anneken, N. Burkart, and M. Huber, “Chapter 9 - Explaining CNN classifier using association rule mining methods on time-series,” in Explainable Deep Learning AI : Methods and Challenges, Elsevier, 2023, pp. 173–189. doi: 10.1016/B978-0-32-396098-4.00015-6.
    14. M.-A. Zöller, W. Titov, T. Schlegel, and M. Huber, “XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine Learning,” ACM Transactions on Interactive Intelligent Systems, vol. 13, Art. no. 4, 2023, doi: 10.1145/3625240.
    15. J. Lindermayr, C. Odabasi, M. Völk, Y. Chen, R. Bormann, and M. Huber, “SynthRetailProduct3D (SyRePro3D): A Pipeline for Synthesis of 3D Retail Product Models with Domain Specific Details Based on Package Class Templates,” in Computer Vision Systems : 14th International Conference, ICVS 2023, Vienna, Austria, September 27-29, 2023, Proceedings, Cham: Springer Nature, 2023, pp. 230–242. doi: 10.1007/978-3-031-44137-0_20.
    16. J. Maier, J. Gram, M. Weisbarth, C. Hennebold, and M. Huber, “Unsupervised Event Abstraction for Automatic Process Modeling of PLC-controlled Automation Systems,” Procedia CIRP, vol. 120, pp. 631–636, 2023, doi: 10.1016/j.procir.2023.09.050.
    17. X. Wu, P. Wagner, and M. Huber, “Quantification of Uncertainties in Neural Networks,” in New Digital Work : Digital Sovereignty at the Workplace, Cham, Schweiz: Springer, 2023, pp. 276–287. doi: 10.1007/978-3-031-26490-0_16.
    18. J. Schuhmacher, V. Hummel, D. Palm, and T. Bauernhansl, “Method for Determining Material Demands by Combing Deterministic and Probabilistic Information in Flexible and Changeable Production Systems,” in 2023 IEEE International Conference on Industrial Engineering and Engineering Management : 18.-21.12.2023, Marina Bay Sands, Singapore, IEEE, 2023, pp. 1547–1552. doi: 10.1109/IEEM58616.2023.10406540.
    19. N. Silber et al., “Towards an Enzymatic Approach to Valorize Wood Residues for Industrial Production in a Circular Bioeconomy,” Procedia CIRP, vol. 116, pp. 450–455, 2023, doi: 10.1016/j.procir.2023.02.076.
    20. J. Full et al., “Biointelligent Waste-to-X systems: A Novel Concept for Sustainable, Decentralized and Interconnected Value Creation,” Procedia CIRP, vol. 116, pp. 576–581, 2023, doi: 10.1016/j.procir.2023.02.097.
    21. A. Shoshi, R. Miehe, and T. Bauernhansl, “Conceptual Thoughts on Biointelligent Embedded Systems and Operating Systems Architecture,” Procedia Computer Science, vol. 217, pp. 969–978, 2023, doi: 10.1016/j.procs.2022.12.294.
    22. S. Nebauer, P. Schrader, E. Groß, and T. Bauernhansl, “Use of Corporate Venturing Tools in Manufacturing Industry: A Systematic Literature Review,” in Entrepreneurship in the Digital Era : Case Studies, Approaches, and Tools for Ecosystems, Business Models, and Technologies, Cham: Springer, 2023, pp. 95–115. doi: 10.1007/978-3-031-43188-3_6.
    23. R. Hägle, S. Schlögel, K. Klöpfer, and T. Bauernhansl, “A Methodology for the Systematic Selection of Human-Machine Interface Device Types in Production Machinery Development,” Procedia CIRP, vol. 119, pp. 975–980, 2023, doi: 10.1016/j.procir.2023.02.173.
    24. A. Kernbach, K. Hoffmann, O. Sawodny, and S. Eivazi, “Benchmark on deep reinforcement learning-based placing using a robot arm,” in ISR Europe 2023 : 56th International Symposium on Robotics, in cooperation with Fraunhofer IPA September 26 - 27, 2023 in Stuttgart, Berlin and Offenbach: VDE Verlag, 2023, pp. 369–375.
    25. R. Miehe, E. Groß, T. Ackermann, E. Gamero, and Y. Baumgarten, “Learning Factories for Biointelligent Production - Design Aspects and Required Competencies,” in 13th Conference on Learning Factories (CLF 2023) : 09.-11.05.2023, Reutlingen, Reutlingen, 2023, p. 6. doi: 10.2139/ssrn.4458036.
    26. J. Stahl, A. Frommknecht, and M. Huber, “Comprehensive Quantitative Quality Assessment of Thermal Cut Sheet Edges using Convolutional Neural Networks,” in The 34th British Machine Vision Conference : 20.-24.11.2022, Aberdeen, UK, Durham, UK, 2023, p. 11.
    27. M.-A. Zöller, F. Mauthe, P. Zeiler, M. Lindauer, and M. Huber, “Automated Machine Learning for Remaining Useful Life Predictions,” in 2023 IEEE International Conference on Systems, Man, and Cybernetics : 01.-04.10.2023, Hyatt Maui, Hawaii, USA, 2023, pp. 2907–2912. doi: 10.1109/SMC53992.2023.10394031.
    28. H. Himmelstoß, R. Hall, B. Vojanec, P. Thieme, and T. Bauernhansl, “Conceptualizing A Digital Twin Based On The Asset Administration Shell For The Implementation Of Use Case Specific Digital Services,” in Proceedings of the Conference on Production Systems and Logistics : 14th - 17th November 2023, Stellenbosch Institute for Advanced Study (STIAS), Stellenbosch, South Africa, publish-Ing, 2023, pp. 90–99. doi: 10.15488/15285.
    29. T.-F. Hinrichsen, E. Colangelo, and T. Bauernhansl, “Function-based Approach for Supply Chain Resilience,” Procedia CIRP, vol. 120, pp. 219–224, 2023, doi: 10.1016/j.procir.2023.08.039.
    30. L. Rödel, G. Müller, J. Krebs, T. Denner, and T. Bauernhansl, “DesignChain: Process Automation From Recording Of Customer Requirements To Production Release,” in Proceedings of the Conference on Production Systems and Logistics : 14th - 17th November 2023, Stellenbosch Institute for Advanced Study (STIAS), Stellenbosch, South Africa, publish-Ing, 2023, pp. 245–254. doi: 10.15488/15261.
    31. D. Ranke, A. Bruns, R. Fink, A. Lehnert, and T. Bauernhansl, “Evaluation of Indicators for Simulation’s Prediction Quality of Material Demand in Matrix Production Systems,” Procedia Computer Science, vol. 217, pp. 268–277, 2023, doi: 10.1016/j.procs.2022.12.222.
    32. D. Brajovic, V. P. Göbels, J. Kutz, and M. Huber, “Merging (EU)-Regulation and Model Reporting,” in Workshop on Regulatable Machine Learning at the 37th Conference on Neural Information Processing Systems : 16.12.2023, New Orleans, USA, 2023, p. 12.
    33. M.-P. Radtke, M. Huber, and J. Bock, “Increasing Robustness of Data-Driven Fault Diagnostics with Knowledge Graphs,” Proceedings of the Annual Conference of the PHM Society, vol. 15, Art. no. 1, 2023.
    34. S. Stribick, S. Akcara, and E. Dieringer, “Laserstrukturierung von Rührreibschweißwerkzeugen : Einfluss von Laserstrukturen auf das Rührreibschweißen von Kunststoffen,” wt Werkstattstechnik online, vol. 113, Art. no. 1/2, 2023, doi: 10.37544/1436-4980-2023-01-02-63.
    35. S. Schmidt, T. Bauernhansl, T. Schlegel, and J. Siegert, “A New Era of Value Creation - Vertical Value Creation,” Procedia CIRP, vol. 120, pp. 661–666, 2023, doi: 10.1016/j.procir.2023.09.055.
    36. F. Mais, T. Bauernhansl, and L. Schmitt, “Treiber der nachhaltigen Geschäftsmodellinnovation : Expert*innen-Befragungen in der Industrie zu den Treibern der Dekarbonisierung,” wt Werkstattstechnik online, vol. 113, Art. no. 11–12, 2023, doi: 10.37544/1436-4980-2023-11-12-61.
    37. J. Krebs, G. Müller, L. Rödel, J. Prochnau, J. F. Strutz, and T. Bauernhansl, “Precision Assessment of Tactile On-Machine Inspection for Milling Operations,” in Proceedings of the Conference on Production Systems and Logistics : 14th - 17th November 2023, Stellenbosch Institute for Advanced Study (STIAS), Stellenbosch, South Africa, publish-Ing, 2023, pp. 437–446. doi: 10.15488/15263.
    38. X. Wu, C. Nitsche, M. Huber, and E. Wedernikow, “Towards Optimal Energy Management Strategy for Hybrid Electric Vehicle with Reinforcement Learning,” in The 35th IEEE Intelligent Vehicles Symposium (IV 2023) : 04.-07.06.2023, Anchorage, Alaska, USA, 2023, p. 7. doi: 10.1109/IV55152.2023.10186787.
    39. E. Ortlieb et al., “Big Data in der Massivumformung : Carbon Footprint Tool zur Messung und Erfassung von Halbzeug- und Werkzeugtemperaturen,” wt Werkstattstechnik online, vol. 113, Art. no. 10, 2023, doi: 10.37544/1436-4980-2023-10-41.
    40. A. Bozkurt et al., “Cyber-Physical-Systems for Fluid Manufacturing Systems,” in Conference on Production Systems and Logistics : International Conference, CPSL 2023, hosted at the Tecnológico de Monterrey (Tec de Monterrey), Querétaro, Mexico 28th February 2023 - 2nd March 2023 Proceedings, Offenburg: publish-Ing, 2023, pp. 640–653. doi: 10.15488/13484.
    41. H. Himmelstoß, R. Hall, P. Thieme, and T. Bauernhansl, “Designing Smart Services for a Component Manufacturer Based on a Digital Twin,” Procedia CIRP, vol. 119, pp. 950–956, 2023, doi: 10.1016/j.procir.2023.03.139.
    42. C. Jauch, T. Leitritz, and M. Huber, “Self-supervised Optimization of Hand Pose Estimation using Anatomical Features and Iterative Learning,” in 2023 IEEE International Conference on Systems, Man, and Cybernetics : 01.-04.10.2023, Hyatt Maui, Hawaii, USA, 2023, pp. 4519–4524. doi: 10.1109/SMC53992.2023.10394319.
    43. P. Takenaka, J. Maucher, and M. Huber, “Guiding Video Prediction with Explicit Procedural Knowledge,” in IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) : 02.-06.10.2023, Paris, France, 2023, pp. 1076–1084. doi: 10.1109/ICCVW60793.2023.00116.
    44. H. Kim, A. Frommknecht, B. Bieberstein, J. Stahl, and M. F. Huber, “Automated end-of-line quality assurance with visual inspection and convolutional neural networks,” Technisches Messen, vol. 90, Art. no. 3, 2023, doi: 10.1515/teme-2022-0092.
    45. X. Wu, M. El-Shamouty, C. Nitsche, and M. Huber, “Uncertainty-Guided Active Reinforcement Learning with Bayesian Neural Networks : 2023 IEEE International Conference on Robotics and Automation (ICRA 2023), May 29 - June 2, 2023. London, UK,” in 2023 IEEE International Conference on Robotics and Automation (ICRA 2023) : 29.05.2021 - 02.06.2021, London, UK, 2023, pp. 5751–5757. doi: 10.1109/ICRA48891.2023.10160686.
    46. M. Moosmann et al., “Using Meta-Learning to Reduce the Effort of Training New Workpiece Geometries for Entanglement Detection in Bin-Picking Applications,” in Advances in Automotive Production Technology - Towards Software-Defined Manufacturing and Resilient Supply Chains : Stuttgart Conference on Automotive Production (SCAP2022), Wiesbaden: Springer Vieweg, 2023, pp. 149–157. doi: 10.1007/978-3-031-27933-1_14.
    47. K. Leiner, F. Dollmann, M. Huber, M. Geiger, and S. Leinberger, “Cut Interruption Detection in the Laser Cutting Process Using ROCKET on Audio Signals,” in IEEE International Conference on Industrial Informatics, INDIN′23 : 17.-20.07.2023, Lemgo, Deutschland, 2023, p. 6. doi: 10.1109/INDIN51400.2023.10218267.
    48. P. Wagner, X. Wu, and M. Huber, “Kalman Bayesian Neural Networks for Closed-form Online Learning,” in 37th AAAI Conference on Artificial Intelligence : 7. - 14.02.2023, Washington, DC, USA, Washington, DC, USA, 2023, pp. 10069–10077.
    49. A. Al Assadi et al., “Challenges and Prospects of Automated Disassembly of Fuel Cells for a Circular Economy,” Resources, Conservation & Recycling Advances, vol. 19, pp. 200172, 15, 2023, doi: 10.1016/j.rcradv.2023.200172.
    50. M. Anneken, M. Veerappa, M. Huber, C. Kühnert, F. Kronenwett, and G. Maier, “Explainable AI for Sensor-Based Sorting Systems,” Technisches Messen : Plattform für Methoden, Systeme und Anwendungen in der Messtechnik, vol. 90, Art. no. 3, 2023, doi: 10.1515/teme-2022-0097.
    51. H. Himmelstoß and T. Bauernhansl, “Modelling The Digital Twin For Data-Driven Product Development - A Literature Review,” in Proceedings of the Conference on Production Systems and Logistics : 14th - 17th November 2023, Stellenbosch Institute for Advanced Study (STIAS), Stellenbosch, South Africa, publish-Ing, 2023, pp. 634–643. doi: 10.15488/15287.
    52. M. Oberle, D. Schel, M. Risling, and T. Bauernhansl, “Comparing Research Trends And Industrial Adoption Of Manufacturing Operations Management Solutions,” in Proceedings of the Conference on Production Systems and Logistics : 14th - 17th November 2023, Stellenbosch Institute for Advanced Study (STIAS), Stellenbosch, South Africa, publish-Ing, 2023, pp. 427–436. doi: 10.15488/15248.
    53. C. Kaucher, K. Erlach, and T. Bauernhansl, “Planning Method for Future-Proof Factory Buildings,” Procedia CIRP, vol. 120, pp. 15–20, 2023, doi: 10.1016/j.procir.2023.08.004.
    54. B. Sai, J. Gram, and T. Bauernhansl, “Information-based Preprocessing of PLC Data for Automatic Behavior Modeling,” Procedia CIRP, vol. 120, pp. 565–571, 2023, doi: 10.1016/j.procir.2023.09.038.
    55. M. Trierweiler, L. Schermuly, M. Kirchberger, and T. Bauernhansl, “Application of Reconfiguration Process for Matrix Manufacturing System in an Industrial Use Case,” Procedia CIRP, vol. 120, pp. 1528–1533, 2023, doi: 10.1016/j.procir.2023.09.209.
    56. J. Raible et al., “Artificial Neural Network Guided Compensation of Nonlinear Payload and Wear Effects for Industrial Robots,” in 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE) : 26.-30.08.2023, Auckland, New Zealand, 2023, p. 8. doi: 10.1109/CASE56687.2023.10260559.
    57. D. Ranke and T. Bauernhansl, “Modeling of a Matrix Production System for Simulation to Predict Material Demand,” in Advances in Production Management Systems : Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures. IFIP WG 5.7 International Conference, APMS 2023 Trondheim, Norway, September 17-21, 2023 Proceedings, Part III, Cham: Springer Nature, 2023, pp. 676–690. doi: 10.1007/978-3-031-43670-3_47.
    58. O. de Mitri, A. Frommknecht, M. Huber, F. Müller-Graf, and C. Distante, “Synthetic Data Generation for Improvement of Machine Learning based Optical Quality Control: A Practical Approach in the Welding Context,” in Multimodal Sensing and Artificial Intelligence: Technologies and Applications III : Proceedings of SPIE OPTICAL METROLOGY. 26-30 JUNE 2023, Munich, 2023, p. 10. doi: 10.1117/12.2682047.
    59. S. Adam, C. Fries, and B. Kádár, “Financial Aspects of a Trust-Based Resource Sharing Platform,” CIRP Journal of Manufacturing Science and Technology, vol. 43, pp. 88–105, 2023, doi: 10.1016/j.cirpj.2023.03.004.
    60. C. Fries, P. Hölscher, O. Brützel, G. Lanza, and T. Bauernhansl, “Approach for Evaluating Changeable Production Systems in a Battery Module Production Use Case,” in Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus : Proceedings of FAIM 2022, June 19-23, 2022, Detroit, Michigan, USA, Cham, Schweiz: Springer, 2023, pp. 207–216. doi: 10.1007/978-3-031-18326-3_21.
    61. H.-H. Wiendahl, “PPC Layout and Order Net - Visualization for a Rapid PPC Analysis and Design,” in Advances in Production Management Systems : Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures. IFIP WG 5.7 International Conference, APMS 2023 Trondheim, Norway, September 17-21, 2023 Proceedings, Part III, Cham: Springer Nature, 2023, pp. 817–831. doi: 10.1007/978-3-031-43670-3_57.
    62. S. Schumacher, R. Hall, M. Hautzinger, J. Schöllmann, and T. Bauernhansl, “Characterization of Digitally-Advanced Methods in Lean Production Systems 4.0,” in Advances in Production Management Systems : Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures. IFIP WG 5.7 International Conference, APMS 2023 Trondheim, Norway, September 17-21, 2023 Proceedings, Part I, Cham: Springer Nature, 2023, pp. 184–199. doi: 10.1007/978-3-031-43662-8_14.
    63. M. Risling, H. Himmelstoß, A. Brandstetter, D. Shi, and T. Bauernhansl, “Bridging The Gap: A Framework For Structuring The Asset Administration Shell In Digital Twin Implementation For Industry 4.0,” in Proceedings of the Conference on Production Systems and Logistics : 14th - 17th November 2023, Stellenbosch Institute for Advanced Study (STIAS), Stellenbosch, South Africa, publish-Ing, 2023, pp. 760–770. doi: 10.15488/15286.
    64. M. Albus and M. Huber, “Resource Reconfiguration and Optimization in Brownfield Constrained Robotic Assembly Line Balancing Problems,” Journal of Manufacturing Systems, vol. 67, pp. 132–142, 2023, doi: 10.1016/j.jmsy.2023.01.001.
    65. J. Lindermayr et al., “IPA-3D1K: A large Retail 3D Model Dataset for Robot Picking,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems : 01.-05.10.2023, Detroit, Michigan, USA, Piscataway, NJ, USA: IEEE Press, 2023, pp. 11404–11411. doi: 10.1109/IROS55552.2023.10342260.
    66. M. Moosmann et al., “Performance Comparison of Supervised and Reinforcement Learning Approaches for Separating Entanglements in a Bin-Picking Application,” in Advances in Automotive Production Technology - Towards Software-Defined Manufacturing and Resilient Supply Chains : Stuttgart Conference on Automotive Production (SCAP2022), Wiesbaden: Springer Vieweg, 2023, pp. 158–167. doi: 10.1007/978-3-031-27933-1_15.

  5. 2022

    1. I. K. Gauger, T. Nagel, and M. Huber, “Hybrides Maschinelles Lernen im Kontext der Produktion,” in Digitalisierung souverän gestalten II : Handlungsspielräume in digitalen Wertschöpfungsnetzwerken, Berlin u.a.: Springer Vieweg, 2022, pp. 64–79. doi: 10.1007/978-3-662-64408-9_6.
    2. R. Schönhof, J. Elstner, R. Manea, S. Tauber, R. Awad, and M. Huber, “Simplified Learning of CAD Features Leveraging a Deep Residual Autoencoder,” Procedia CIRP, vol. 109, pp. 84–88, 2022, doi: 10.1016/j.procir.2022.05.218.
    3. S. Schumacher, R. Hall, A. Bildstein, and T. Bauernhansl, “Toolbox Lean 4.0 - Development and Implementation of a Database Approach for the Management of Digital Methods and Tools,” Procedia CIRP, vol. 107, pp. 776–781, 2022, doi: 10.1016/j.procir.2022.05.061.
    4. E. Groß, J. Siegert, R. Tenberg, and T. Bauernhansl, “Extension of Assembly System Planning Methods to Include Competence Development in the Value-added Process,” in Proceedings of the 12th Conference on Learning Factories (CLF 2022), Amsterdam: Elsevier, 2022, p. 6.
    5. M. Schalk, I. Schalk, T. Bauernhansl, J. Siegert, A. Esin, and U. Schneider, “Influence of exoskeleton use on welding quality during a simulated welding task,” Wearable Technologies, vol. 3, p. 15, 2022, doi: 10.1017/wtc.2022.13.
    6. L. Rauh, S. Gärtner, D. Brandt, M. Oberle, D. Stock, and T. Bauernhansl, “Towards AI Lifecycle Management in Manufacturing Using the Asset Administration Shell (AAS),” Procedia CIRP, vol. 107, pp. 576–581, 2022, doi: 10.1016/j.procir.2022.05.028.
    7. C. Hennebold, K. Klöpfer, P. Lettenbauer, and M. Huber, “Machine Learning based Cost Prediction for Product Development in Mechanical Engineering,” Procedia CIRP, vol. 107, pp. 264–269, 2022, doi: 10.1016/j.procir.2022.04.043.
    8. A. Lämmle et al., “Simulation-based Learning of the Peg-in-Hole Process Using Robot-Skills,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems : 23.-27.10.2022, Kyoto, Japan, Piscataway, NJ, USA: IEEE Press, 2022, pp. 9340–9346. doi: 10.1109/IROS47612.2022.9982212.
    9. M. El-Shamouty, J. Titze, S. Kortik, W. Kraus, and M. Huber, “GLIR: A Practical Global-local Integrated Reactive Planner towards Safe Human-Robot Collaboration,” in 27th IEEE International Conference on Emerging Technologies and Factory Automation : 06. - 09.09.2022, Stuttgart, Piscataway, NJ, USA: IEEE Press, 2022, p. 8. doi: 10.1109/ETFA52439.2022.9921583.
    10. C. Hennebold, X. Mei, O. Mailahn, M. Huber, and O. Mannuß, “Cooperation of Human and Active Learning based AI for Fast and Precise Complaint Management,” in 2022 IEEE International Conference on Systems, Man, and Cybernetics : 09.10. - 12.10.2022, Virtuell, Piscataway, NJ, USA: IEEE Press, 2022, pp. 282–287. doi: 10.1109/SMC53654.2022.9945445.
    11. S. Hagmeyer, P. Zeiler, and M. Huber, “On the Integration of Fundamental Knowledge about Degradation Processes into Data-Driven Diagnostics and Prognostics Using Theory-Guided Data Science,” in Proceedings of the 7th European Conference of the Prognostics and Health Management Society 2022 : Turin, Italy. July 6th - July 8th, 2022, New York, USA, 2022, pp. 156–165.
    12. J. Schiebl et al., “Model-Based Biomechanical Exoskeleton Concept Optimization for a Representative Lifting Task in Logistics,” International Journal of Environmental Research and Public Health, vol. 19, Art. no. 23, 2022, doi: 10.3390/ijerph192315533.
    13. C. Fries, E. Colangelo, L. Pollmann, T.-F. Hinrichsen, and T. Bauernhansl, “New Data Structures for a Flexible Order Management,” Procedia Computer Science, vol. 200, pp. 267–275, 2022, doi: 10.1016/j.procs.2022.01.225.
    14. M. Moosmann et al., “Transfer Learning for Machine Learning-based Detection and Separation of Entanglements in Bin-Picking Applications,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems : 23.-27.10.2022, Kyoto, Japan, Piscataway, NJ, USA: IEEE Press, 2022, pp. 1123–130. doi: 10.1109/IROS47612.2022.9981082.
    15. M. Veerappa, M. Anneken, N. Burkart, and M. Huber, “Validation of XAI Explanations for Multivariate Time Series Classification in the Maritime Domain,” Journal of Computational Science, vol. 58, p. 10, 2022, doi: 10.1016/j.jocs.2021.101539.
    16. N. E. Bances Purizaca, A. M. A. Karol, and U. Schneider, “LSTM and CNN Based IMU Sensor Fusion Approach for Human Pose Identification in Manual Handling Activities,” in Wearable Robotics: Challenges and Trends : Proceedings of the 5th International Symposium on Wearable Robotics, WeRob2020, and of WearRAcon Europe 2020, October 13-16, 2020, Cham, Schweiz: Springer, 2022, pp. 461–465. doi: 10.1007/978-3-030-69547-7_74.
    17. K. Kleeberger, F. Roth, R. Bormann, and M. Huber, “Automatic Grasp Pose Generation for Parallel Jaw Grippers,” in Intelligent Autonomous Systems 16 : Proceedings of the 16th International Conference IAS-16, Cham, Schweiz: Springer Nature, 2022, pp. 594–607. doi: 10.1007/978-3-030-95892-3_45.
    18. M. Tröster et al., “Biomechanical Analysis of Stoop and Free-Style Squat Lifting and Lowering with a Generic Back-Support Exoskeleton Model,” International Journal of Environmental Research and Public Health, vol. 19, Art. no. 15, 2022, doi: 10.3390/ijerph19159040.
    19. S. Dürr, R. Lamprecht, E. Colangelo, C. Fries, H.-H. Wiendahl, and M. Huber, “A Data-Driven Approach to Generate Planned Order Book Scenarios in Multi-Variant Production,” Procedia CIRP, vol. 107, pp. 71–76, 2022, doi: 10.1016/j.procir.2022.04.012.
    20. P. Berkhan, S. Kärcher, J. Maier, E. Cuk, and T. Bauernhansl, “Sensorbasierte Montageanalyse : Transparenz in manuellen Montageprozessen,” wt Werkstattstechnik online, vol. 112, Art. no. 1/2, 2022, doi: 10.37544/1436-4980-2022-01-02-59.
    21. M. Schalk et al., “Exoskelette reduzieren die subjektive Belastung : Einfluss auf das Belastungsempfinden während standardisierter Arbeitsprozesse,” wt Werkstattstechnik online, vol. 112, Art. no. 9, 2022, doi: 10.37544/1436-4980-2022-09-79.
    22. R. Wang, S. Hoppe, E. Monari, and M. Huber, “Defect Transfer GAN: Diverse Defect Synthesis for Data Augmentation,” in The 33rd British Machine Vision Conference : 21.-24.11.2022, London, UK, Durham, UK, 2022, p. 14.
    23. C. Fries and T. Bauernhansl, “Turbulence Costs within Order Management,” Procedia CIRP, vol. 107, pp. 1269–1274, 2022, doi: 10.1016/j.procir.2022.05.143.
    24. M. Kaufmann, I. Effenberger, and M. Huber, “Study on Algorithms for the Virtual Assembly and Best Combinations of In-line Measured Injection-Molded Parts,” Procedia CIRP, vol. 107, pp. 239–245, 2022, doi: 10.1016/j.procir.2022.04.040.
    25. S. Schmidt, A. J. Martin, and T. Bauernhansl, “DMM - Systematische datenbasierte Geschäftsmodelle : Teil 2: Vorstellung eines Datenmonetarisierungsmodells (DMM) für produzierende Unternehmen,” wt Werkstattstechnik online, vol. 112, Art. no. 9, 2022, doi: 10.37544/1436-4980-2022-09-91.
    26. N. E. Bances Purizaca et al., “Applicability of Exoskeletons in Timber Prefabrication: Actions for Exoskeleton Research,” Procedia CIRP, vol. 107, pp. 1210–1215, 2022, doi: 10.1016/j.procir.2022.05.133.
    27. C. Fries and T. Bauernhansl, “Customer-Induced Planning Deviations within Order Management,” Procedia Computer Science, vol. 200, pp. 71–82, 2022, doi: 10.1016/j.procs.2022.01.206.
    28. M. Schalk, I. Schalk, T. Bauernhansl, J. Siegert, and U. Schneider, “Influence of Exoskeleton Use on Cardiac Index,” Hearts, vol. 3, Art. no. 4, 2022, doi: 10.3390/hearts3040014.
    29. A. Al Assadi, D. Holtz, F. Nägele, C. Nitsche, W. Kraus, and M. Huber, “Machine learning based screw drive state detection for unfastening screw connections,” Journal of Manufacturing Systems, vol. 65, pp. 19–32, 2022, doi: 10.1016/j.jmsy.2022.07.013.
    30. S. Dürr, R. Silbernagel, H. Bartsch, G. L. Steier, M. Huber, and G. Lanza, “A Data-Driven Approach for Option-Specific Order Freeze Points in Mass-Customized Production,” in Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems : Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference (CARV2021) and the 10th World Mass Customization & Personalization Conference (MCPC2021), Aalborg, Denmark, October/November 2021, Cham, Schweiz: Springer Nature, 2022, pp. 620–627. doi: 10.1007/978-3-030-90700-6_70.
    31. T. Bauernhansl, P. Mößner, P. Busch, and T. Hansla, “Methodology for Identifying and Increasing Order-Neutral Components,” Procedia CIRP, vol. 109, pp. 66–71, 2022, doi: 10.1016/j.procir.2022.05.215.
    32. C. Fries, S. Adam, T. Bauernhansl, and G. Schuh, “Impact of Customer Order Change Dimensions on Order Management,” in 2022 IEEE International Conference on Industrial Engineering and Engineering Mangement : 07.-10.12.2022, Kuala Lumpur, Malaysia, Piscataway, NJ, USA: IEEE Press, 2022, pp. 757–761. doi: 10.1109/IEEM55944.2022.9989974.
    33. S. Schmidt, A. J. Martin, and T. Bauernhansl, “Datenkategorisierung für datenbasierte Geschäftsmodelle : Teil 1: Problemstellung, Grundlagen und Datenkategorisierung datenbasierter Geschäftsmodelle,” wt Werkstattstechnik online, vol. 112, Art. no. 7/8, 2022, doi: 10.37544/1436-4980-2022-07-08-55.
    34. V. Kopp et al., “Exoworkathlon: A Prospective Study Approach for the Evaluation of Industrial Exoskeletons,” Wearable Technologies, vol. 3, p. 16, 2022, doi: 10.1017/wtc.2022.17.
    35. S. Dürr, R. Silbernagel, H. Bartsch, G. L. Steier, M. Huber, and G. Lanza, “A Data-Driven Approach for Option-Specific Order Freeze Points in Mass-Customized Production,” in Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems : Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference (CARV2021) and the 10th World Mass Customization & Personalization Conference (MCPC2021), Aalborg, Denmark, October/November 2021, Cham, Schweiz: Springer Nature, 2022, pp. 620–627. doi: 10.1007/978-3-030-90700-6_70.
    36. M. Schalk, I. Schalk, T. Bauernhansl, J. Siegert, and U. Schneider, “Investigation of Possible Effects of Wearing Exoskeletons during Welding on Heart Rate,” Physiologia, vol. 2, Art. no. 3, 2022, doi: 10.3390/physiologia2030009.
    37. F. Aziz, O. Metwally, P. Weller, U. Schneider, and M. Huber, “A MIMO Radar-Based Metric Learning Approach for Activity Recognition,” in 2022 IEEE Radar Conference : 21.03. - 25.03.2022, New York City, USA, Piscataway, NJ, USA: IEEE Press, 2022, p. 6. doi: 10.1109/RADARCONF2248738.2022.9764202.
    38. T. Nagel and M. Huber, “Kalman-Bucy-Informed Neural Network for System Identification,” in 61st IEEE Conference on Decision and Control : 06. - 09.12.2022, Cancún, Mexico, Piscataway, NJ, USA: IEEE Press, 2022, pp. 1503–1508. doi: 10.1109/CDC51059.2022.9993245.
    39. S. Brüggenjürgen, N. Schaaf, P. Kerschke, and M. Huber, “Mixture of Decision Trees for Interpretable Machine Learning,” in 21st International Conference on Machine Learning and Applications : 12. - 14.12.2022, Nassau, Bahamas, Nassau, Bahamas, 2022, pp. 1175–182. doi: 10.1109/ICMLA55696.2022.00190.
    40. T. Teriete, M. Böhm, B. Sai, K. Erlach, and T. Bauernhansl, “Event-based Framework for Digitalization of Value Stream Mapping,” Procedia CIRP, vol. 107, pp. 481–486, 2022, doi: 10.1016/j.procir.2022.05.012.
    41. F. Graf, J. Lindermayr, C. Odabasi, and M. Huber, “Toward Holistic Scene Understanding : A Transfer of Human Scene Perception to Mobile Robots,” IEEE Robotics & Automation Magazine, vol. 29, Art. no. 4, 2022, doi: 10.1109/MRA.2022.3210587.
    42. P. Humbeck, H. Loeffler, and T. Bauernhansl, “Business Ecosystem Management : A Model for the Governance, Auditing and Design of Business Ecosystems,” in PICMET ’22 - Portland International Conference on Management of Engineering and Technology - Proceedings : Technology Management and Leadership in Digital Transformation - Looking Ahead to Post-COVID Era, Piscataway, NJ, USA: IEEE Press, 2022, p. 6.
    43. H.-H. Wiendahl, C. Fries, and E. Colangelo, “Quick-Check Order Management,” in Advances in Production Management Systems : Smart Manufacturing and Logistics Systems: Turning Ideas into Action. IFIP WG 5.7 International Conference, APMS 2022 Gyeongju, South Korea, September 25-29, 2022 Proceedings, Part I, Cham: Springer Nature, 2022, p. 2.
    44. S. Adam, C. Fries, N. Gábor, and B. Kádár, “Economic Aspects of a Resource Sharing Manufacturing Network Under Turbulences,” in Proceedings of the 18th European Conference on Management Leadership and Governance ECMLG 2022 : Hosted by ISCTE - Instituto Universitário de Lisboa Portugal. 10-11 November 2022, Reading, UK, 2022, pp. 389–395. doi: 10.34190/ecmlg.18.1.885.
    45. P. Mindermann et al., “Design of Fiber-Composite/Metal-Hybrid Structures Made by Multi-Stage Coreless Filament Winding,” Applied Sciences, vol. 12, Art. no. 5, 2022, doi: 10.3390/app12052296.
    46. M. Trierweiler and T. Bauernhansl, “Reconfiguration Process for Matrix Manufacturing Systems,” Procedia CIRP, vol. 107, pp. 699–704, 2022, doi: 10.1016/j.procir.2022.05.048.
    47. E. Colangelo, C. Fries, T.-F. Hinrichsen, S. Adam, and N. Gábor, “Maturity Model for AI in Smart Production Planning and Control System,” Procedia CIRP, vol. 107, pp. 493–498, 2022, doi: 10.1016/j.procir.2022.05.014.
    48. R. Miehe, M. Finkbeiner, A. Sauer, and T. Bauernhansl, “A System Thinking Normative Approach towards Integrating the Environment into Value-Added Accounting - Paving the Way from Carbon to Environmental Neutrality,” Sustainability, vol. 14, Art. no. 20, 2022, doi: 10.3390/su142013603.
    49. P. Weller, F. Aziz, S. Abdulatif, U. Schneider, and M. Huber, “A MIMO Radar-based Few-Shot Learning Approach for Human-ID,” in 30th European Signal Processing Conference : 29.08. - 02.09.2022, Belgrade, Serbia, Piscataway, NJ, USA: IEEE Press, 2022, pp. 1796–1800.

  6. 2021

    1. J. Siegert et al., “Model-based Approach for the Automation and Acceleration of the CE-Conformity Process for Modular Production Systems: Future Requirements and Potentials,” in 2nd Conference on Production Systems and Logistics : 10.08.2021 ‐ 11.08.2021. Online Conference, Offenburg: publish-Ing, 2021, pp. 177–190. doi: 10.15488/11273.
    2. D. Bauer, M. Böhm, T. Bauernhansl, and A. Sauer, “Increased resilience for manufacturing systems in supply networks through data-based turbulence mitigation,” Production Engineering: Research and Development, vol. 15, Art. no. 3–4, 2021, doi: 10.1007/s11740-021-01036-4.
    3. E. Groß, J. Siegert, B. Miljanovic, R. Tenberg, and T. Bauernhansl, “Design of multimodal interfaces in human-robot assembly for competence development,” in Proceedings of the Conference on Learning Factories, Amsterdam u.a.: Elsevier, 2021, p. 6. doi: 10.2139/ssrn.3858769.
    4. R. Lamprecht, F. Wurst, and M. Huber, “Reinforcement Learning based Condition-oriented Maintenance Scheduling for Flow Line Systems,” in 2021 IEEE 19th International Conference on Industrial Informatics (INDIN) : 21.07.2021 - 23.07.2021, Virtual, Piscataway, NJ, USA: IEEE Press, 2021, p. 7. doi: 10.1109/INDIN45523.2021.9557373.
    5. K. Kleeberger, J. Schnitzler, M. U. Khalid, R. Bormann, W. Kraus, and M. Huber, “Precise Object Placement with Pose Distance Estimations for Different Objects and Grippers,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems : 27.09.2021 - 01.10.2021. Online, Piscataway, NJ, USA, 2021, pp. 4639–4646. doi: 10.1109/IROS51168.2021.9635926.
    6. K. Kleeberger, M. Völk, R. Bormann, and M. Huber, “Investigations on Output Parameterizations of Neural Networks for Single Shot 6D Object Pose Estimation,” in 2021 IEEE International Conference on Robotics and Automation (ICRA 2021) : 30.05.2021 - 05.06.2021, Virtuell, Piscataway, NJ, USA: IEEE Press, 2021, pp. 13916–13922. doi: 10.1109/ICRA48506.2021.9561712.
    7. M.-A. Zöller, T.-D. Nguyen, and M. Huber, “Incremental Search Space Construction for Machine Learning Pipeline Synthesis,” in Advances in Intelligent Data Analysis XIX : 19th International Symposium on Intelligent Data Analysis, IDA 2021, Porto, Portugal, April 26-28, 2021, Proceedings, Cham: Springer Nature, 2021, pp. 103–115. doi: 10.1007/978-3-030-74251-59.
    8. F. Eiling and M. Huber, “Automatische Programmierung von Produktionsmaschinen,” in Digitalisierung souverän gestalten : Innovative Impulse im Maschinenbau, Berlin u.a.: Springer Vieweg, 2021, pp. 44–58. doi: 10.1007/978-3-662-62377-04.
    9. M. Huber, T. Nagel, R. Lamprecht, and F. Eiling, “Potenziale von Reinforcement Learning für die Produktion,” Industrie 4.0 Management : Gegenwart und Zukunft Industrieller Geschäftsprozesse, vol. 37, Art. no. 2, 2021, doi: 10.30844/I40M21-2S25-29.
    10. T. Bauernhansl, “End-to-End komplett neu gedacht : Editorial,” wt Werkstattstechnik online, vol. 111, Art. no. 3, 2021.
    11. M. Kaufmann, I. Effenberger, and M. Huber, “Measurement uncertainty assessment for virtual assembly,” Journal of Sensors and Sensor Systems, vol. 10, Art. no. 1, 2021, doi: 10.5194/jsss-10-101-2021.
    12. P. Schmidhäuser, M. Link, and D. Berner, “Gestaltung einer arbeitsplatznahen und multifunktionalen Lernumgebung : linc - der kleinste Seminarraum der Zukunft,” ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 116, Art. no. 6, 2021, doi: 10.1515/zwf-2021-0102.
    13. S. Dürr, R. Lamprecht, M. Kauffmann, and M. Huber, “Reinforcement Learning based Optimization of Bayesian Networks for Generating Feasible Vehicle Configuration Suggestions,” in IEEE 17th International Conference on Automation Science and Engineering : 23.08.2021 - 27.08.2021, Lyon, France and Online, Piscataway, NJ, USA: IEEE Press, 2021, pp. 16–22. doi: 10.1109/CASE49439.2021.955142.
    14. D. Bauer, T. Bauernhansl, and A. Sauer, “Improvement of Delivery Reliability by an Intelligent Control Loop between Supply Network and Manufacturing,” Applied Sciences, vol. 11, Art. no. 5, 2021, doi: 10.3390/app11052205.
    15. M. Huber, T. Nagel, R. Lamprecht, and F. Eiling, “Potenziale von Reinforcement Learning für die Produktion,” Industrie 4.0 Management : Gegenwart und Zukunft Industrieller Geschäftsprozesse, vol. 37, Art. no. 2, 2021, doi: 10.30844/I40M21-2S25-29.
    16. D. Görzig and T. Bauernhansl, “Service Capability Ontology,” in Advances in Production Management Systems : Artificial Intelligence for Sustainable and Resilient Production Systems. IFIP WG 5.7 International Conference, APMS 2021, Nantes, France, September 5-9, 2021, Proceedings, Part V, Cham: Springer Nature, 2021, pp. 361–368. doi: 10.1007/978-3-030-85914-538.
    17. E. Westkämper, “Anwendung technischer Intelligenz in der Produktion durch Corona verzögert? : Editorial,” wt Werkstattstechnik online : Forschung und Entwicklung in der Produktion, vol. 111, Art. no. 6, 2021.
    18. N. Burkart and M. Huber, “A Survey on the Explainability of Supervised Machine Learning,” Journal of Artificial Intelligence Research, vol. 70, pp. 245–317, 2021, doi: 10.1613/jair.1.12228.
    19. C. Fries, M. Fechter, N. Gábor, S. Adam, and T. Bauernhansl, “First Results of a Survey on Manufacturing of the Future,” Procedia Computer Science, vol. 180, pp. 142–149, 2021, doi: 10.1016/j.procs.2021.01.137.
    20. M. J. Kratzer, L. Bauch, T. Burkert, B. Szost, and T. Bauernhansl, “Reasons for Engineering Changes Affecting Part-specific Tools : An Investigation in the Automotive Industry,” in 2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Piscataway: IEEE, 2021, pp. 477–481. doi: 10.1109/IEEM50564.2021.9672777.
    21. M. Böhm and T. Bauernhansl, “Data-based turbulence evaluation in production systems,” Procedia CIRP, vol. 99, pp. 686–691, 2021, doi: 10.1016/j.procir.2021.03.119.
    22. T. Eusterwiemann, I. K. Gauger, F. Eiling, and A. Bildstein, “An Integration Approach of Educational Artificial Intelligence (AI) Use Cases into a Demonstration Factory,” in Proceedings of the Conference on Learning Factories, Amsterdam u.a.: Elsevier, 2021, p. 6. doi: 10.2139/ssrn.3862399.
    23. M. Schalk, J. Siegert, U. Schneider, and T. Bauernhansl, “Effektivität industrieller Exoskelette : Auswertung einer Expertenumfrage,” wt Werkstattstechnik online : Forschung und Entwicklung in der Produktion, vol. 111, Art. no. 5, 2021, doi: 10.37544/1436-4980-2021-05-53.
    24. M.-A. Zöller and M. Huber, “Benchmark and Survey of Automated Machine Learning Frameworks,” Journal of Artificial Intelligence Research, vol. 70, pp. 409–472, 2021, doi: 10.1613/jair.1.11854.
    25. S. Schumacher, F. A. Schmid, A. Bildstein, and T. Bauernhansl, “Lean Production Systems 4.0: The Impact of the Digital Transformation on Production System Levels,” Procedia CIRP, vol. 104, pp. 259–264, 2021, doi: 10.1016/j.procir.2021.11.044.
    26. M. Böhm and T. Bauernhansl, “Data-based turbulence evaluation in production systems,” Procedia CIRP, vol. 99, pp. 686–691, 2021, doi: 10.1016/j.procir.2021.03.119.
    27. D. Bargmann, P. Tenbrock, L. Halt, F. Nägele, W. Kraus, and M. F. Huber, “Unobstructed Programming-by-Demonstration for Force-Based Assembly Utilizing External Force-Torque Sensors,” in 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Piscataway: IEEE, 2021, pp. 119–124. doi: 10.1109/SMC52423.2021.9658707.
    28. D. Bauer, M. Böhm, T. Bauernhansl, and A. Sauer, “Increased resilience for manufacturing systems in supply networks through data-based turbulence mitigation,” Production Engineering: Research and Development, vol. 15, Art. no. 3–4, 2021, doi: 10.1007/s11740-021-01036-4.
    29. M.-A. Zöller, T.-D. Nguyen, and M. Huber, “Incremental Search Space Construction for Machine Learning Pipeline Synthesis,” in Advances in Intelligent Data Analysis XIX : 19th International Symposium on Intelligent Data Analysis, IDA 2021, Porto, Portugal, April 26-28, 2021, Proceedings, Cham: Springer Nature, 2021, pp. 103–115. doi: 10.1007/978-3-030-74251-59.
    30. M. Moosmann et al., “Separating Entangled Workpieces in Random Bin Picking using Deep Reinforcement Learning,” Procedia CIRP, vol. 104, pp. 881–886, 2021, doi: 10.1016/j.procir.2021.11.148.
    31. D. Ranke and T. Bauernhansl, “Evaluation of Material Supply Strategies in Matrix Manufacturing Systems,” in Advances in Automotive Production Technology - Theory and Application : Stuttgart Conference on Automotive Production (SCAP 2020), Wiesbaden: Springer Vieweg, 2021, pp. 80–88. doi: 10.1007/978-3-662-62962-810.
    32. E. Groß, J. Siegert, B. Miljanovic, and T. Bauernhansl, “Kompetenzentwicklung in der hybriden Montage : Visuelle, auditive und haptische Gestaltung von Systeminteraktionen,” wt Werkstattstechnik online : Forschung und Entwicklung in der Produktion, vol. 111, Art. no. 3, 2021, doi: 10.37544/1436-4980-2021-03-15.
    33. S. Dürr, R. Lamprecht, M. Kauffmann, J. Winter, H. Alexy, and M. Huber, “Development of an Integrated Data-Driven Process to Handle Uncertainties in Multi-Variant Production and Logistics: A Survey,” in Advances in Automotive Production Technology - Theory and Application : Stuttgart Conference on Automotive Production (SCAP 2020), Wiesbaden: Springer Vieweg, 2021, pp. 486–494. doi: 10.1007/978-3-662-62962-856.
    34. W. Kraus and T. Bauernhansl, “Wie Automatisierung die Zukunft der Produktion verändern wird : Einordnung und Nutzen für produzierende Unternehmen,” ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 116, Art. no. 10, 2021, doi: 10.1515/zwf-2021-0165.
    35. S. Stribick, E. Dieringer, and P. Huber, “Werkzeugeintritt beim Rührreibschweißen : Einfluss des Werkzeuges auf die Prozesskräfte und -temperaturen beim Werkzeugeintritt,” wt Werkstattstechnik online : Forschung und Entwicklung in der Produktion, vol. 111, Art. no. 11–12, 2021, doi: 10.37544/1436-4980-2021-11-12-70.
    36. M. J. Kratzer, C. Buchner, P. Kübler, T. Burkert, B. Szost, and T. Bauernhansl, “Zeitpunktabhängige Prognose von Änderungsaufwänden : Zeitpunktabhängige Prognose von Änderungsaufwänden bei technischen Bauteiländerungen,” wt Werkstattstechnik online : Forschung und Entwicklung in der Produktion, vol. 111, Art. no. 5, 2021, doi: 10.37544/1436-4980-2021-05-63.
    37. K. Kleeberger, F. Roth, R. Bormann, and M. Huber, “Automatic Grasp Pose Generation for Parallel Jaw Grippers,” in 16th International Conference On Intelligent Autonomous Systems : 22.06.2021 - 25.06.2021. Singapore, Singapur, 2021, p. 14.
    38. D. Bauer, C. Kaymakci, T. Bauernhansl, and A. Sauer, “Intelligent Energy Systems as Enabler for Increased Resilience of Manufacturing Systems,” Procedia CIRP, vol. 104, pp. 217–222, 2021, doi: 10.1016/j.procir.2021.11.037.
    39. T. Nagel and M. Huber, “Autoencoder-Inspired Identification of LTI Systems,” in European Control Conference : 29.06.2021 - 02.07.2021, Virtual Conference, Piscataway, NJ, USA: IEEE Press, 2021, pp. 2352–2357.
    40. M. Trierweiler and T. Bauernhansl, “Reconfiguration of Production Equipment of Matrix Manufacturing Systems,” in Advances in Automotive Production Technology - Theory and Application : Stuttgart Conference on Automotive Production (SCAP 2020), Wiesbaden: Springer Vieweg, 2021, pp. 20–27. doi: 10.1007/978-3-662-62962-83.
    41. M. U. Khalid et al., “Automatic Grasp Generation for Vacuum Grippers for Random Bin Picking,” in Advances in Automotive Production Technology - Theory and Application : Stuttgart Conference on Automotive Production (SCAP 2020), Wiesbaden: Springer Vieweg, 2021, pp. 247–255. doi: 10.1007/978-3-662-62962-829.
    42. J. Siegert, T. Schlegel, L. Zarco, and T. Bauernhansl, “Requirements for Human-Centric Informational Complexity Management in Production in the Context of the Matrix Fusion Factory,” in 2nd Conference on Production Systems and Logistics : 10.08.2021 ‐ 11.08.2021. Online Conference, Offenburg: publish-Ing, 2021, pp. 75–86. doi: 10.15488/11287.
    43. C. Landgraf, K. Ernst, G. Schleth, M. Fabritius, and M. Huber, “A Hybrid Neural Network Approach for Increasing the Absolute Accuracy of Industrial Robots,” in IEEE 17th International Conference on Automation Science and Engineering : 23.08.2021 - 27.08.2021, Lyon, France and Online, Piscataway, NJ, USA: IEEE Press, 2021, pp. 468–474. doi: 10.1109/CASE49439.2021.9551684.
    44. N. Schaaf, O. de Mitri, H. B. Kim, A. Windberger, and M. Huber, “Towards Measuring Bias in Image Classification,” in Artificial Neural Networks and Machine Learning - ICANN 2021 : 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14-17, 2021, Proceedings, Part III, Cham, Schweiz: Springer Nature, 2021, pp. 433–445. doi: 10.1007/978-3-030-86365-435.
    45. C. Fries, M. Fechter, N. Gábor, S. Adam, and T. Bauernhansl, “First Results of a Survey on Manufacturing of the Future,” Procedia Computer Science, vol. 180, pp. 142–149, 2021, doi: 10.1016/j.procs.2021.01.137.
    46. E. Groß, J. Siegert, B. Miljanovic, and T. Bauernhansl, “Kompetenzentwicklung in der hybriden Montage : Visuelle, auditive und haptische Gestaltung von Systeminteraktionen,” wt Werkstattstechnik online, vol. 111, Art. no. 3, 2021, doi: 10.37544/1436-4980-2021-03-15.
    47. M.-A. Zöller and M. Huber, “Benchmark and Survey of Automated Machine Learning Frameworks,” Journal of Artificial Intelligence Research, vol. 70, pp. 409–472, 2021, doi: 10.1613/jair.1.11854.
    48. C. Landgraf, B. Meese, M. Pabst, G. Martius, and M. Huber, “A Reinforcement Learning Approach to View Planning for Automated Inspection Tasks,” Sensors, vol. 21, Art. no. 6, 2021, doi: 10.3390/s21062030.
    49. M.-A. Berchtold, M. Böhm, K. Erlach, and T. Bauernhansl, “Nutzung von Standortrollen für die Fabrikkonzeptplanung : Ein Alternativansatz zur Strukturierung des strategischen Fabrikplanungsvorgehens in Produktionsnetzwerken,” ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 116, Art. no. 9, 2021, doi: 10.1515/zwf-2021-0136.
    50. M. Böhm, T. Bauernhansl, and S. Jeschke, “Behavior of Decision Forest Classification in Dynamic Manufacturing Systems,” Procedia CIRP, vol. 104, pp. 524–529, 2021, doi: 10.1016/j.procir.2021.11.088.
    51. L. Zarco, J. Siegert, T. Schlegel, and T. Bauernhansl, “Scope and Delimitation of Game Engine Simulations for Ultra-Flexible Production Environments,” Procedia CIRP, vol. 104, pp. 792–797, 2021, doi: 10.1016/j.procir.2021.11.133.
    52. S. Kärcher and T. Bauernhansl, “Method for Data-Driven Assembly Sequence Planning,” in Advances in Automotive Production Technology - Theory and Application : Stuttgart Conference on Automotive Production (SCAP 2020), Wiesbaden: Springer Vieweg, 2021, pp. 71–79. doi: 10.1007/978-3-662-62962-89.
    53. C. Fries et al., “Fluid Manufacturing Systems (FLMS) : A Novel Approach For Versatility In Production,” in Advances in Automotive Production Technology - Theory and Application : Stuttgart Conference on Automotive Production (SCAP 2020), Wiesbaden: Springer Vieweg, 2021, pp. 37–44. doi: 10.1007/978-3-662-62962-85.
    54. M. Kaufmann, I. Effenberger, and M. Huber, “On the Development of a Surrogate Modelling Toolbox for Virtual Assembly,” Applied Sciences, vol. 11, Art. no. 3, 2021, doi: 10.3390/app11031181.
    55. M. Kaufmann, I. Effenberger, and M. Huber, “Selective Assembly Strategy for Quality Optimization in a Laser Welding Process,” in Advances in Automotive Production Technology - Theory and Application : Stuttgart Conference on Automotive Production (SCAP 2020), Wiesbaden: Springer Vieweg, 2021, pp. 126–134. doi: 10.1007/978-3-662-62962-815.
    56. J. Siegert, L. Zarco, and T. Schlegel, “Adaptive Visual Concept for Controlling Cyber-Physical Production Modules based on Cognitive Associations,” Procedia CIRP, vol. 104, pp. 809–814, 2021, doi: 10.1016/j.procir.2021.11.136.
    57. R. Miehe, L. Buckreus, S. Kiemel, A. Sauer, and T. Bauernhansl, “A Conceptual Framework for Biointelligent Production : Calling for Systemic Life Cycle Thinking in Cellular Units,” Clean technologies, vol. 3, Art. no. 4, 2021, doi: 10.3390/cleantechnol3040049.

  7. 2020

    1. K. Kleeberger and M. Huber, “Single Shot 6D Object Pose Estimation,” in 2020 IEEE International Conference on Robotics and Automation (ICRA 2020) : 31 May - 31 August 2020, Virtuell, Piscataway (NJ), USA, 2020, pp. 6239–6245. doi: 10.1109/ICRA40945.2020.9197207.
    2. J. Siegert, L. Zarco, and T. Schlegel, “Universal Accessibility Concept for Controlling Production Means in Manufacturing Systems,” in 21st IEEE International Conference on Industrial Technology : 26-28 Februar 2020, Buenos Aires, Argentinien, Piscataway, NJ, USA, 2020, pp. 349–354. doi: 10.1109/ICIT45562.2020.9067194.
    3. H.-H. Wiendahl, “Auftragsmanagement,” in Fabrikbetriebslehre 1 : Management in der Produktion, Berlin and Heidelberg: Springer Vieweg, 2020, pp. 193–294. doi: 10.1007/978-3-662-44538-97.
    4. N. Burkart, M. Huber, and M. Anneken, “Supported Decision-Making by Explainable Predictions of Ship Trajectories,” in 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020), Cham: Springer, 2020, pp. 44–54. doi: 10.1007/978-3-030-57802-25.
    5. T. Schlegel, J. Siegert, T. Mahr, L. Zarco, F. Herbrig, and T. Bauernhansl, “Metrological PPC: Determination of added-value driven module process graphs,” Procedia CIRP, vol. 93, pp. 634–639, 2020, doi: 10.1016/j.procir.2020.04.123.
    6. K. Kleeberger, R. Bormann, W. Kraus, and M. Huber, “A Survey on Learning-Based Robotic Grasping,” Current Robotics Reports, p. 11, 2020, doi: 10.1007/s43154-020-00021-6.
    7. M. Trierweiler, P. Foith-Förster, and T. Bauernhansl, “Changeability of Matrix Assembly Systems,” Procedia CIRP, vol. 93, pp. 1127–1132, 2020, doi: 10.1016/j.procir.2020.04.029.
    8. S. Schumacher, A. Bildstein, and T. Bauernhansl, “The Impact of the Digital Transformation on Lean Production Systems,” Procedia CIRP, vol. 93, pp. 783–788, 2020, doi: 10.1016/j.procir.2020.03.066.
    9. A. Al Assadi et al., “User-friendly, requirement based assistance for production workforce using an asset administration shell design,” Procedia CIRP, vol. 91, pp. 402–406, 2020, doi: 10.1016/j.procir.2020.02.192.
    10. J. Siegert, L. Zarco, T. Schlegel, and T. Bauernhansl, “Software Control System Requirements for Ultra-Flexible Learning Factories,” Procedia Manufacturing, vol. 45, pp. 442–447, 2020, doi: 10.1016/j.promfg.2020.04.050.
    11. T. Bauernhansl, “Soviel KI war selten,” VDI-Z : Integrierte Produktion, vol. 162, Art. no. 4, 2020.
    12. J. Siegert, T. Schlegel, and T. Bauernhansl, “Verifiable Competencies for Production Technology,” Procedia Manufacturing, vol. 45, pp. 466–472, 2020, doi: 10.1016/j.promfg.2020.04.054.
    13. M. Huber, “Bayesian Perceptron: Towards fully Bayesian Neural Networks,” in 59th IEEE Conference on Decision and Control : December 14th-18th 2020, Virtuell, Piscataway, NJ, USA, 2020, pp. 3179–3186. doi: 10.1109/CDC42340.2020.
    14. H.-H. Wiendahl and T. Denner, “Arbeitsplanung,” in Fabrikbetriebslehre 1 : Management in der Produktion, Berlin and Heidelberg: Springer Vieweg, 2020, pp. 165–191. doi: 10.1007/978-3-662-44538-96.
    15. S. Schumacher, B. Pokorni, H. Himmelstoß, and T. Bauernhansl, “Conceptualization of a Framework for the Design of Production Systems and Industrial Workplaces,” Procedia CIRP, vol. 91, pp. 176–181, 2020, doi: 10.1016/j.procir.2020.02.165.
    16. P. Kübler, C. Glock, and T. Bauernhansl, “A new iterative method for solving the joint dynamic storage location assignment, order batching and picker routing problem in manual picker-to-parts warehouses,” Computers & Industrial Engineering, vol. 147, p. 20, 2020, doi: 10.1016/j.cie.2020.106645.
    17. R. E. Geitner, O. Schöllhammer, and T. Bauernhansl, “Veränderung der Industrielogik im Maschinenbau - Teil 2 : Wettbewerbsfähige Wertangebote durch Business Ecosystems,” wt Werkstattstechnik online, vol. 110, Art. no. 1–2, 2020.
    18. V. Balzer, T. Schrodi, and H.-H. Wiendahl, “Strategien und Struktur produzierender Unternehmen,” in Fabrikbetriebslehre 1 : Management in der Produktion, Berlin and Heidelberg: Springer Vieweg, 2020, pp. 35–66. doi: 10.1007/978-3-662-44538-92.
    19. P. Humbeck, J. P. Jaeckle, J. Duwe, and T. Bauernhansl, “The Business Ecosystem Management Canvas,” in 2020 IEEE International Conference on Industrial Engineering and Engineering Mangement : 14-17 December, Virtuell, Piscataway, NJ, USA: IEEE Press, 2020, pp. 249–254. doi: 10.1109/IEEM45057.2020.9309731.
    20. M. Kaufmann, I. Effenberger, and M. Huber, “Computed Tomography enabling Virtual Assembly,” The Web’s Largest Open Access Database of Nondestructive Testing (NDT), p. 10, 2020.
    21. M. Link, P. Schmidhäuser, and A. Fehrle, “Konzeption und Gestaltung von Learningstreams : Blended-Learning-Konzept zur anwendungsorientierten Weiterbildung im Innovationslabor Future Work Lab,” Zeitschrift für wirtschaftliche Fertigung ZWF, vol. 115, Art. no. 10, 2020, doi: 10.3139/104.112422.
    22. N. Gábor et al., “Intelligent Production of The Future - First Results of A Survey,” in 17th IMEKO TC 10 and EUROLAB Virtual Conference : Global trends in Testing, Diagnostics & Inspection for 2030. 20 - 22 October 2020, Online, Budapest, Ungarn, 2020, pp. 402–407.
    23. N. Burkart, M. Franz, and M. Huber, “Explanation Framework for Intrusion Detection,” in Machine Learning for Cyber Physical Systems : Selected papers from the International Conference ML4CPS 2020. March 12-13, 2020, Berlin, Berlin u.a.: Springer Vieweg, 2020, pp. 83–91. doi: 10.1007/978-3-662-62746-4.
    24. K. Kleeberger et al., “Transferring Experience from Simulation to the Real World for Precise Pick-And-Place Tasks in Highly Cluttered Scenes,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems : Consumer Robotics and Our Future. October 25 - November 25, 2020. On-Demand Conference, Piscataway, NJ, USA, 2020, pp. 9681–9688.
    25. M. Röhm, H.-H. Wiendahl, T. Denner, and O. Schöllhammer, “Ganzheitliche Produktionssysteme,” in Fabrikbetriebslehre 1 : Management in der Produktion, Berlin and Heidelberg: Springer Vieweg, 2020, pp. 295–343. doi: 10.1007/978-3-662-44538-98.
    26. N. Burkart, P. M. Faller, E. Peinsipp, and M. Huber, “Batch-wise Regularization of Deep Neural Networks for Interpretability,” in 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems : 14-16 September 2020, virtuell, Piscataway (NJ), USA, 2020, p. 7.
    27. T. Pschybilla and H. Alex, “Evaluation of end-to-end process and information flow analyses through digital transformation in mechanical engineering,” Procedia CIRP, vol. 93, pp. 298–303, 2020, doi: 10.1016/j.procir.2020.04.070.
    28. M. El-Shamouty, X. Wu, S. Yang, M. Albus, and M. Huber, “Towards Safe Human-Robot Collaboration Using Deep Reinforcement Learning,” in 2020 IEEE International Conference on Robotics and Automation (ICRA 2020) : 31 May - 31 August 2020, Virtuell, Piscataway (NJ), USA, 2020, pp. 4899–4905. doi: 10.1109/ICRA40945.2020.9196924.
    29. T. Bauernhansl et al., “Semantic Structuring of Elements and Capabilities in Ultra-flexible Factories,” Procedia CIRP, vol. 93, pp. 335–340, 2020, doi: 10.1016/j.procir.2020.04.010.
    30. H. Reinerth and M. Lickefett, “Fabrikplanung,” in Fabrikbetriebslehre 1 : Management in der Produktion, Berlin and Heidelberg: Springer Vieweg, 2020, pp. 103–127. doi: 10.1007/978-3-662-44538-94.
    31. M. Tröster, D. Wagner, F. Müller-Graf, C. Maufroy, U. Schneider, and T. Bauernhansl, “Biomechanical Model-Based Development of an Active Occupational Upper-Limb Exoskeleton to Support Healthcare Workers in the Surgery Waiting Room,” International Journal of Environmental Research and Public Health, vol. 17, Art. no. 14, 2020, doi: 10.3390/ijerph17145140.
    32. E. Groß, S. Finkbeiner, J. Siegert, and T. Bauernhansl, “Sprachsteuerung für die Mensch-Roboter-Kollaboration in der Montage : Gestaltung von Regeln und Implementierung,” wt Werkstattstechnik online, vol. 110, Art. no. 1–2, 2020.
    33. C. Fries, H.-H. Wiendahl, and A. Al Assadi, “Design concept for the intralogistics material supply in matrix productions,” Procedia CIRP, vol. 91, pp. 33–38, 2020, doi: 10.1016/j.procir.2020.02.147.
    34. N. Burkart, S. Robert, and M. Huber, “Are you sure? Prediction revision in automated decisionmaking,” Expert Systems, p. 19, 2020, doi: 10.1111/exsy.12577.
    35. N. E. Bances Purizaca, U. Schneider, J. Siegert, and T. Bauernhansl, “Exoskeletons Towards Industrie 4.0: Benefits and Challenges of the IoT Communication Architecture,” Procedia Manufacturing, vol. 42, pp. 49–56, 2020, doi: 10.1016/j.promfg.2020.02.087.
    36. J. Siegert, T. Schlegel, L. Zarco, B. Miljanovic, A. Meyke, and T. Bauernhansl, “Ultra-flexible Factories: An Approach to Manage Complexity,” Procedia CIRP, vol. 93, pp. 329–334, 2020, doi: 10.1016/j.procir.2020.04.112.
    37. R. Neuhaus et al., “Integrating Ionic Electroactive Polymer Actuators and Sensors Into Adaptive Building Skins - Potentials and Limitations,” Frontiers in Built Environment, vol. 6, p. 22, 2020, doi: 10.3389/fbuil.2020.00095.
    38. J. Trommnau et al., “Limp Component Design for Automatic Assembly - Classification Rating System and Design Rules,” Procedia CIRP, vol. 93, pp. 1139–1144, 2020, doi: 10.1016/j.procir.2020.05.157.
    39. J. Trommnau, A. Frommknecht, J. Siegert, J. Wößner, and T. Bauernhansl, “Design for Automatic Assembly: A new Approach to Classify Limp Components,” Procedia CIRP, vol. 91, pp. 49–54, 2020, doi: 10.1016/j.procir.2020.01.136.
    40. T. Bauernhansl and R. Miehe, “Industrielle Produktion - Historie, Treiber und Ausblick,” in Fabrikbetriebslehre 1 : Management in der Produktion, Berlin and Heidelberg: Springer Vieweg, 2020, pp. 1–33. doi: 10.1007/978-3-662-44538-91.
    41. L. Zarco, J. Siegert, T. Schlegel, and T. Bauernhansl, “Determining and Evaluating Trajectories of a Modular Machine End-Effector in a Manufacturing Environment using a Game Engine,” Procedia CIRP, vol. 93, pp. 1079–1084, 2020, doi: 10.1016/j.procir.2020.04.120.
    42. A. Kluth, M. Schiffer, C. Fries, and J. König, “Influencing factors of the digital transformation on the supply chain complexity dimensions,” Journal of Production Systems and Logistics, vol. 1, pp. 1–11, 2020, doi: 10.15488/9916.
    43. P. Holtewert and H.-H. Wiendahl, “Fertigungs- und Montagesysteme,” in Fabrikbetriebslehre 1 : Management in der Produktion, Berlin and Heidelberg: Springer Vieweg, 2020, pp. 129–164. doi: 10.1007/978-3-662-44538-95.
    44. M. Huber, “Lernen aus der Black Box : Cognitive Deep Learning soll neuronale Netze und Wissensverarbeitung kombinieren,” Physik Journal, vol. 19, Art. no. 4, 2020.
    45. D. Ranke, A. Aichele, D. Görzig, M. Luckert, J. Siegert, and T. Bauernhansl, “Analysis of SMEs as a target group for research institute services,” Procedia Manufacturing, vol. 42, pp. 356–361, 2020, doi: 10.1016/j.promfg.2020.02.065.
    46. J. Siegert, T. Schlegel, L. Zarco, and T. Bauernhansl, “Order-Oriented Learning Factories: Why and How Learning Factories Have to Adapt,” Procedia Manufacturing, vol. 45, pp. 460–465, 2020, doi: 10.1016/j.promfg.2020.04.053.
    47. M. Huber and U. Eberl, “Schwerpunkt Künstliche Intelligenz (Rezensionen),” Physik Journal, vol. 19, Art. no. 4, 2020.

  8. 2019

    1. M. El-Shamouty, K. Kleeberger, A. Lämmle, and M. Huber, “Simulation-driven machine learning for robotics and automation,” TM Technisches Messen : Plattform für Methoden, Systeme und Anwendungen in der Messtechnik, Art. no. TOnline31.08.2019, 2019, doi: 10.1515/teme-2019-0072.
    2. J. Trommnau, J. Kühnle, J. Siegert, R. Inderka, and T. Bauernhansl, “Overview of the State of the Art in the Production Process of Automotive Wire Harnesses, Current Research and Future Trends,” Procedia CIRP, vol. 81, pp. 387–392, 2019, doi: 10.1016/j.procir.2019.03.067.
    3. T. Rossmeissl, E. Groß, L. Zarco, T. Schlegel, J. Siegert, and T. Bauernhansl, “Approach for Extending Evaluation Criteria for Scalable and Modular Industrial Robots,” Procedia CIRP, vol. 81, pp. 1022–1027, 2019, doi: 10.1016/j.procir.2019.03.245.
    4. D. Görzig, M. Luckert, and T. Bauernhansl, “Nutzung von Industrie 4.0-Testumgebungen durch kleine und mittlere Unternehmen : Analyse der Zusammenarbeit zwischen Forschungseinrichtungen und KMU in Digitalisierungsvorhaben,” wt Werkstattstechnik online, vol. 109, Art. no. 6, 2019.
    5. R. Neuhaus, C. Glanz, I. Kolaric, J. Siegert, and T. Bauernhansl, “Manufacturing, optimization and design of electroactive CNT-actuators for adaptive building envelopes,” in Advances in Engineering Materials, Structures and Systems: Innovations, Mechanics and Applications : Proceedings of the 7th International Conference on Structural Engineering, Mechanics and Computation. September September 2-4, 2019, Cape Town, South Africa, London: CRC Press, 2019, p. 6.
    6. E. Groß, L. Solf, T. Rossmeissl, J. Siegert, and T. Bauernhansl, “Konzeption eines Co-Designs für kundenindividuelle Produkte : Gestaltung eines Mockups,” wt Werkstattstechnik online, vol. 109, Art. no. 6, 2019.
    7. A. Bildstein, J. Feng, and T. Bauernhansl, “Combining Channel Theory and Semantic Web Technology to build up a Production Capability Matching Framework,” Procedia CIRP, vol. 81, pp. 139–144, 2019, doi: 10.1016/j.procir.2019.03.025.
    8. U. Schleinkofer, M. Dazer, K. Lucan, O. Mannuß, B. Bertsche, and T. Bauernhansl, “Framework for Robust Design and Reliability Methods to Develop Frugal Manufacturing Systems,” Procedia CIRP, vol. 81, pp. 518–523, 2019, doi: 10.1016/j.procir.2019.03.148.
    9. D. Görzig, S. Kärcher, and T. Bauernhansl, “Capability-based Planning of Digital Innovations in Small- and Medium-sized Enterprises,” in 21st IEEE Conference on Business Informatics : July 15-17, 2019, Mocow, Russia, Piscataway, NJ, USA: IEEE Press, 2019, pp. 495–503. doi: 10.1109/CBI.2019.00064.
    10. E. Colangelo, T. Bauernhansl, S. Hartleif, and T. Kröger, “A Service-Oriented Approach for the Cognitive Factory - A Position Paper,” in The 1st International Conference on Artificial Intelligence in Information and Communication : February 11-13, 2019, Okinawa, Japan, Piscataway, NJ, USA: IEEE Press, 2019, pp. 540–542. doi: 10.1109/ICAIIC.2019.8668990.
    11. U. Schleinkofer, D. Moz, T. Bauernhansl, and A. Lang, “Knowledge Acquisition in Product Planning of Frugal Manufacturing Systems for Emerging Markets,” Procedia CIRP, vol. 81, pp. 246–251, 2019, doi: 10.1016/j.procir.2019.03.043.
    12. N. Schaaf, J. Maucher, and M. Huber, “Enhancing Decision Tree based Interpretation of Deep Neural Networks through L1-Orthogonal Regularization,” in 18th International Conference on Machine Learning and Applications : December 16-19, Boca Raton, Florida, USA, Boca Raton, 2019, pp. 42–49. doi: 10.1109/ICMLA.2019.00016.
    13. A. Bruns, T. Schlegel, M. Lickefett, and J. Siegert, “Echtzeitnahe Simulation mit in-situ Visualisierung : Betriebsparallele Simulation zur Prognose von Störauswirkungen,” wt Werkstattstechnik online, vol. 109, Art. no. 3, 2019.
    14. N. El Bekri, J. Kling, and M. Huber, “A Study on Trust in Black Box Models and Post-Hoc Explanations,” in 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019) : Proceedings. 13-15 May 2019, Seville, Spain, Cham: Springer, 2019, p. 12.
    15. P. Dunau, M. Huber, and J. Beyerer, “Gaussian Process based Dynamic Facial Emotion Tracking,” in 2019 IEEE International Conference on Industrial Cyber Physical Systems : Proceedings. 06-09 May, 2019, Taipei, Taiwan, 2019, pp. 248–253. doi: 10.1109/ICPHYS.2019.8780338.
    16. T. Schlegel, J. Siegert, and T. Bauernhansl, “Metrological Production Control for Ultra-flexible Factories,” Procedia CIRP, vol. 81, pp. 1313–1318, 2019, doi: 10.1016/j.procir.2019.04.019.
    17. D. Görzig, M. Luckert, A. Aichele, and T. Bauernhansl, “Approaches for the Development of Digital Products in Small and Medium-sized Enterprises,” in Advances in Production Research : Proceedings of the 8th Congress of the German Academic Association for Production Technology (WGP), November 19-20, 2018, Cham: Springer Nature, 2019, pp. 574–583.
    18. D. Görzig, S. Kärcher, and T. Bauernhansl, “Capability-Based Implementation of Digital Service Innovation in SMEs,” in Advances in Production Management Systems : Towards Smart Production Management Systems. IFIP WG 5.7 International Conference, APMS 2019, Austin, TX, USA, September 1-5, 2019, Proceedings, Part II, Cham: Springer Nature, 2019, pp. 502–509. doi: 10.1007/978-3-030-30000-562.
    19. S. Kärcher, D. Görzig, and T. Bauernhansl, “Modeling Manual Assembly System to Derive Best Practice from Actual Data,” in Advances in Production Management Systems : Towards Smart Production Management Systems. IFIP WG 5.7 International Conference, APMS 2019, Austin, TX, USA, September 1-5, 2019, Proceedings, Part II, Cham: Springer Nature, 2019, pp. 431–438. doi: 10.1007/978-3-030-29996-550.
    20. R. Geitner and T. Bauernhansl, “Identifikation und Auswahl von Business Ecosystems : Grundsätzliches Vorgehen sowie strategische und operative Einbindung in Industrieunternehmen,” wt Werkstattstechnik online, vol. 109, Art. no. 4, 2019.
    21. R. E. Geitner, O. Schöllhammer, and T. Bauernhansl, “Veränderung der Industrielogik im Maschinenbau - Teil 1 : Wettbewerbsfähige Wertangebote durch Business Ecosystems,” wt Werkstattstechnik online, vol. 109, Art. no. 11–12, 2019.
    22. U. Schleinkofer, T. Herrmann, I. Maier, T. Bauernhansl, D. Roth, and D. Spath, “Development and Evaluation of a Design Thinking Process Adapted to Frugal Production Systems for Emerging Markets,” Procedia Manufacturing, vol. 39, pp. 609–617, 2019, doi: 10.1016/j.promfg.2020.01.429.
    23. K. Daxhammer, M. Luckert, D. M. Dörr, and T. Bauernhansl, “Development of a Strategic Business Model Framework for Multi-Sided Platforms to Ensure Sustainable Innovation in Small and Medium-Sized Enterprises,” Procedia Manufacturing, vol. 39, pp. 1354–1362, 2019, doi: 10.1016/j.promfg.2020.01.322.
    24. S. Kärcher, D. Görzig, P. Foith-Förster, and T. Bauernhansl, “Das Applikationszentrum Industrie 4.0 : Vorgehen, Planung und Erfolgsfaktoren,” wt Werkstattstechnik online, vol. 109, Art. no. 3, 2019.
    25. N. Burkart, P. M. Faller, and M. Huber, “Forcing Interpretability for Deep Neural Networks through Rule-based Regularization,” in 18th International Conference on Machine Learning and Applications : December 16-19, Boca Raton, Florida, USA, Boca Raton, 2019, pp. 700–705. doi: 10.1109/ICMLA.2019.00126.
    26. S. Poeschl, F. Wirth, and T. Bauernhansl, “Strategic Process Planning for Commissioning Processes in Mechanical Engineering,” International Journal of Production Research, vol. 57, Art. no. 21, 2019, doi: 10.1080/00207543.2018.1556408.
    27. L. Zarco, J. Siegert, and T. Bauernhansl, “Software Model Requirements Applied to a Cyber-Physical Modular Robot in a Production Environment,” Procedia CIRP, vol. 81, pp. 352–357, 2019, doi: 10.1016/j.procir.2019.03.061.
    28. P. Foith-Förster and T. Bauernhansl, “Generic Production System Model of Personalized Production,” MATEC Web of Conferences, vol. 301, pp. 1–14, 2019, doi: 10.1051/matecconf/201930100019.
    29. T. Rossmeissl, E. Groß, M. Tzempetonidou, and J. Siegert, “Living Learning Environments,” Procedia Manufacturing, vol. 31, pp. 20–25, 2019, doi: 10.1016/j.promfg.2019.03.004.
    30. R. Neuhaus et al., “Ionic CNT actuators and arrays - towards cost-efficient manufacturing through scalable dispersion and printing processes,” in 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics : July 8-12 2019, Hong Kong, China, Piscataway, NJ: IEEE Operations Center, 2019, pp. 56–61. doi: 10.1109/AIM.2019.8868428.
    31. S. Hesping, V. Jelschow, O. Schöllhammer, and T. Bauernhansl, “Strategisches Programm für Industrie 4.0 : Exemplarische Entwicklung eines angepassten Vorgehensmodells für den mittelständischen Maschinenbau,” ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 114, Art. no. 12, 2019, doi: 10.3139/104.112220.
    32. K. Kleeberger, C. Landgraf, and M. Huber, “Large-scale 6D Object Pose Estimation Dataset for Industrial Bin-Picking,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems. November 4-8, 2019, Macau, China, Piscataway, NJ, USA: IEEE Press, 2019, pp. 2573–2578.
    33. U. Schleinkofer, K. Klöpfer, M. Schneider, and T. Bauernhansl, “Cyber-physical Systems as Part of Frugal Manufacturing Systems,” Procedia CIRP, vol. 81, pp. 264–269, 2019, doi: 10.1016/j.procir.2019.03.046.
    34. E. Groß, L. Gadermann, T. Rossmeissl, and T. Bauernhansl, “Konzeption eines formalisierten und wertschöpfungsintegrierten Lernsystems : Strukturierte Weiterbildung während der Wertschöpfung,” wt Werkstattstechnik online, vol. 109, Art. no. 7/8, 2019.
    35. D. Bauer, T. Bauernhansl, and A. Sauer, “Enhanced Classification of Events for Manufacturing Companies in Supply Networks,” Procedia CIRP, vol. 81, pp. 87–92, 2019, doi: 10.1016/j.procir.2019.03.016.
    36. M. U. Khalid, J. Hager, W. Kraus, M. Huber, and M. Toussaint, “Deep Workpiece Region Segmentation for Bin Picking,” in IEEE CASE 2019 : 15th IEEE International Conference on Automation Science and Engineering. 22 to 26 August 2019, Vancouver, Canada, Piscataway, NJ, USA: IEEE Press, 2019, pp. 1138–1144. doi: 10.1109/COASE.2019.8843050.
    37. M. Huber, “Fallstudie: Predictive Maintenance,” in Data Science : Grundlagen, Architekturen und Anwendungen, Heidelberg: dpunkt.verlag, 2019, pp. 225–244.

  9. 2018

    1. P. Dunau, M. Huber, and J. Beyerer, “Comparison of Angle and Size Features with Deep Learning for Emotion Recognition,” in Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications : 23rd Iberoamerican Congress on Pattern Recognition. November 19-22, 2018, Madrid, Spain, Cham: Springer Nature, 2018, pp. 602–610. doi: 10.1007/978-3-030-13469-370.
    2. J. Siegert, T. Schlegel, and T. Bauernhansl, “Matrix Fusion Factory,” Procedia Manufacturing, vol. 23, pp. 177–182, 2018, doi: 10.1016/j.promfg.2018.04.013.
    3. P. Kübler and T. Bauernhansl, “Was bedeutet Industrie 4.0 für die Kommissionierung? : Herausforderungen der personalisierten Produktion meistern,” wt Werkstattstechnik online, vol. 108, Art. no. 4, 2018.
    4. T. Bauernhansl, M. Tzempetonidou, T. Rossmeissl, E. Groß, and J. Siegert, “Requirements for Designing a Cyber-Physical System for Competence Development,” Procedia Manufacturing, vol. 23, pp. 201–206, 2018, doi: 10.1016/j.promfg.2018.04.017.
    5. D. Görzig and T. Bauernhansl, “Enterprise Architectures for the Digital Transformation in Small and Medium-sized Enterprises,” Procedia CIRP, vol. 67, pp. 540–545, 2018, doi: 10.1016/j.procir.2017.12.257.
    6. M. Luckert, D. M. Dörr, and L. Beer, “Beitrag zur Entwicklung eines Bewertungsmodells für die Erfolgschance plattformorientierter Geschäftsmodelle auf Basis digitaler Plattformen bei kleinen und mittleren Unternehmen,” in Vorausschau und Technologieplanung : 14. Symposium für Vorausschau und Technologieplanung. 08. und 09. November 2018, Berlin, in HNI-Verlagsschriftenreihe; 385. Paderborn, 2018, pp. 143–159.
    7. M. Kraemer, P. Middendorf, and T. Bauernhansl, “Investigation on the Influence of Humidity on the Topography of Surfaces of Polymeric Class A Carbon Fiber Reinforced Plastics,” Journal of composite materials, vol. 52, Art. no. 30, 2018, doi: 10.1177/0021998318778891.
    8. M. Kraemer, T. Dauser, P. Middendorf, and T. Bauernhansl, “Correlation Between Subjective Perception and Objective Parameters for the Characterisation of Fibre Print-through on Surfaces of Class A Carbon Fibre Reinforced Plastics via Multidimensional Scaling,” Composites Part A : Applied science and manufacturing, vol. 115, Art. no. 12, 2018, doi: 10.1016/j.compositesa.2018.09.025.
    9. S. Kärcher et al., “Sensor-driven Analysis of Manual Assembly Systems,” Procedia CIRP, vol. 72, pp. 1142–1147, 2018, doi: 10.1016/j.procir.2018.03.241.
    10. S. Ploypech, M. Metzner, C. B. d. Santos, P. Jearanaisilawong, and Y. Boonyongmaneerat, “Effects of Crack Density on Wettability and Mechanical Properties of Hard Chrome Coatings,” Transactions of the Indian Institute of Metals, vol. 72, Art. no. 4, 2018, doi: 10.1007/s12666-018-01553-4.
    11. A. Issa, S. Schumacher, B. Hatiboglu, E. Groß, and T. Bauernhansl, “Open Innovation in the Workplace : Future Work Lab as a Living Lab,” Procedia CIRP, vol. 72, pp. 629–634, 2018, doi: 10.1016/j.procir.2018.03.149.
    12. A. Bildstein, J. Feng, and T. Bauernhansl, “Information Flow-based Capability Matching Service for Smart Manufacturing,” Procedia CIRP, vol. 72, pp. 1015–1021, 2018, doi: 10.1016/j.procir.2018.03.147.
    13. A. Kluth and P. Kübler, “Transparenzmessung in der Produktionslogistik : Ergebnisse des AiF-geförderten Forschungsprojekts Evidentia,” wt Werkstattstechnik online, vol. 108, Art. no. 3, 2018.
    14. E. Colangelo, T. Kröger, and T. Bauernhansl, “Substitution and Complementation of Production Management Functions with Data Analytics,” Procedia CIRP, vol. 72, pp. 191–196, 2018, doi: 10.1016/j.procir.2018.03.145.
    15. U. Schleinkofer, F. Laufer, M. Zimmermann, D. Roth, and T. Bauernhansl, “Resource-Efficient Manufacturing Systems through Lightweight Construction by Using a Combined Development Approach,” Procedia CIRP, vol. 72, pp. 856–861, 2018, doi: 10.1016/j.procir.2018.03.123.
    16. E. Groß, J. Siegert, and T. Bauernhansl, “Different Competence Areas of Workers in Combination with Technical Assistance as an Enabler for Mass Personalization Products,” Procedia Manufacturing, vol. 23, pp. 195–200, 2018, doi: 10.1016/j.promfg.2018.04.016.

Publikationsliste (nicht peer reviewed)

  1. 2026

    1. R. A. Cooper, J. Candiotti, C. S. Chung, L. Gizzi, and U. Schneider, “Rehabilitation Robotics : Chapter 11,” in An Introduction to Rehabilitation Engineering, Boca Raton, USA: CRC Press, 2026, pp. 179–208. doi: 10.1201/9781003303084-11.
    2. A. Formanek, “Robots, Embodied AI and the Concept of Intelligence,” in Introduction to Ethics of Robotics : 03.06.2026, Wien, Österreich, Wien, 2026, p. 32 Folien.

  2. 2025

    1. M. Tröster, “Exoskelette als präventive Maßnahme zur Reduktion muskuloskelettaler Beschwerden,” in TREFFpunkt Gesundheitsindustrie : Präventivmedizin - grenzenlos gesund?! 22.05.2025, Konstanz, Stuttgart, 2025, p. 21 Folien.
    2. M. Huber, “KI-Anwendungsbeispiele aus der Praxis,” in KI-Unternehmerabend 2025: Impulse für Effizienz, Innovation & Service (Teil 2) : 13.05.2025, Online, 2025, p. 33 Folien.
    3. M. Huber, “Dependable AI and qualification of AI-based quality inspection systems,” in MAITRE: Metrics for Measurement of Artificial Intelligence for Trustworthy, Reliable and Explainable Applications : 14.-16.07.2025, Berlin, New Delhi, India, 2025, p. 16 Folien.
    4. M. Tröster, “Age-Appropriate and Ergonomic Work Design with Digital Planning and Exoskeletons,” in ema Online Summit : Research & Innovation in Digital Production Planning. 28.03.2025, Online, Chemnitz, 2025, p. 36 Folien.
    5. M. Willmann, M. Albus, J. Schnabel, and M. Roth, “Application of quantum annealing for scalable robotic assembly line optimization: a case study,” in An International Network on Quantum Annealing (INQA) : 18.02.2025, Online, London, UK, 2025, p. 20 Folien.
    6. H. Amirkhanian Namagerdi and M. Huber, “From Overfitting to Reliability: Introducing the Hierarchical Approximate Bayesian Neural Network,” arXiv, p. 26, 2025, doi: 10.48550/arXiv.2512.13111.
    7. M. Huber, “Trends und Entwicklungen der KI in der Produktion,” in AI-Matters - KI-Anwendungen in der Produktion : Wie Künstliche Intelligenz in der Industrie Realität wird. 04.12.2025, Reutlingen, Reutlingen, 2025, p. 26 Folien.
    8. M. Huber, “Kognitive Produktionssysteme: KI im industriellen Einsatz,” in KI-Tag by HHG : 06.02.2025, Düsseldorf, Düsseldorf, 2025, p. 31 Folien.
    9. U. Schneider, J. Wege, and K. Michel, “Forschungsprojekt zur Kriminalprävention von Vergewaltigung mit technischen Hilfen,” in Kunst gegen Missbrauch, Oppenheim am Rhein: Nünnerich-Asmus Verlag & Media GmbH, 2025, pp. 168–175.
    10. M. Huber, “Grundlagen und Umgang mit Künstlicher Intelligenz,” in KI-Unternehmerabend 2025: Impulse für Effizienz, Innovation & Service (Teil 1) : 29.04.2025, Online, 2025, p. 36 Folien.
    11. M. Huber, “KI im Produktionsumfeld erfolgreich und zuverlässig einsetzen,” in KI Palooza : 30.09.-01.10.2025, Esslingen, Berlin, 2025, p. 28 Folien.
    12. M. Huber, “Gemeinsam schneller: Fraunhofer als Partner im Technologie-Ökosystem,” in next Startup Day : 17.09.2025, Hannover, Hannover, 2025, p. 12 Folien.
    13. T. Bauernhansl, “Geleitwort,” in Fluide Fahrzeugproduktion für die Mobilität der Zukunft : Entwicklung und Implementierung eines menschzentrierten cyberphysischen Produktionskonzeptes, Berlin and Heidelberg: Springer Vieweg, 2025.
    14. M. Huber and I. Effenberger, “Fake it till you make it: Synthetische Bilddatenerzeugung für industrielle Anwendungen,” in 4. VDI-Fachkonferenz KI in der Produktion : Vom Hype zur Realität? 25.-26.02.2025, Leinfelden-Echterdingen, Düsseldorf, 2025, p. 31 Folien.
    15. M. Mayer and M. Huber, “KI in der Produktion - WPS Analytics,” in Hannover Messe 2025 : Shaping the future with technology. 31.03-04.04.2025, Hannover, Hannover, 2025, p. 25 Folien.
    16. M. Holl, A. Benkiser, L. Kloppenburg, U. Frey, and U. Schneider, “Benefits of a novel bimanual urethral catheterization technique for female patients: a nursing school experiment on skills, hygiene and duration,” medRxiv, Art. no. Preprint, 2025, doi: 10.1101/2025.04.24.25326249.
    17. M. Huber, “Potenziale von KI für die Produktion : Hemmnisse, Einsatzmöglichkeiten undein szenariobasierter Ausblick auf 2030,” in KI und Arbeitswelt 2030 : KI im produzierenden Gewerbe. WebTalk. 15.04.2025, Online, Potsdam, 2025, p. 14 Folien.
    18. M. Huber, “KI und Bildverarbeitung: Potentiale für die Qualitätssicherung,” in 4. Süddeutscher Qualitätstag am Fraunhofer IPA : 27.06.2025, Stuttgart, Stuttgart, 2025, p. 28 Folien.
    19. C. Braun and M. Huber, “Hybrid AI-Driven Advances in Prognostics and Health Management Within Manufacturing Environments,” in New Digital Work II : Digital Sovereignty of Companies and Organizations, Cham: Springer, 2025, pp. 109–123. doi: 10.1007/978-3-031-69994-87.
    20. H. Monke, B. Fresz, M. Bernreuther, Y. Chen, and M. Huber, “Efficiently Transforming Neural Networks into Decision Trees: A Path to Ground Truth Explanations with RENTT,” arXiv, Art. no. Preprint, 2025, doi: 10.48550/arXiv.2511.09299.
    21. A. Al Assadi and C. Pollmann, “Zusammenfassung,” in Fluide Fahrzeugproduktion für die Mobilität der Zukunft : Entwicklung und Implementierung eines menschzentrierten cyberphysischen Produktionskonzeptes, Berlin and Heidelberg: Springer Vieweg, 2025, pp. 289–290. doi: 10.1007/978-3-662-70847-716.
    22. M. Huber, “AutoML for Times Series Data,” in Industrial AI Conference : Business, Engineering, Deep Tech. 22.01.2025, Frankfurt am Main, Hannover, 2025, p. 18 Folien.
    23. M. Huber, “Simulation-based AI-Training for Robotics and Machine Vision,” in Machine Vision, Robotics, and AI combined : 04.06.2025, Online, Hoboken, New Jersey, USA, 2025, p. 26 Folien.
    24. M. Huber, “Virtuelle Bildverarbeitung: Von synthetischen Daten zu automatisierten Inspektionslösungen,” in Machine Vision - Von der Inspektion zur smarten Revolution! : 01.-02.07.2025, Baden-Baden, Düsseldorf, 2025, p. 25 Folien.

  3. 2024

    1. H.-H. Wiendahl, “Stolpersteine der PPS,” VDI Wissensforum, 2024.
    2. M. Huber, “ChatGPT: So verändert generative KI den beruflichen Alltag : Sprach-KI betrifft vor allem gut bezahlte Jobs,” Automationspraxis, 2024.
    3. T. Bauernhansl, “Studie zur Biointelligenz: Deutschland auf Platz zwei : InBenBio,” Frankfurter Allgemeine Zeitung, Art. no. Online erschienen am 15.05.2024, 2024.
    4. J. Wirth, M. Schneider, L. Hanselmann, K. Fink, S. Nebauer, and T. Bauernhansl, “Empirical Analysis of the Current Status and Potential of Service-Oriented and Data-Driven Business Models within the Sheet Metal Working Sector: Insights from Interview-Based Research in SMEs,” Preprints.org, 2024, doi: 10.20944/preprints202402.1556.v1.
    5. M. Huber, “Kognitive Produktionssysteme: KI im industriellen Einsatz,” in Effiziente Möbelfertigung in der Praxis 2024 : 19.-20.11.2024, Köln, Hamburg, 2024, p. 29 Folien.
    6. F. Mauthe, C. Braun, J. Raible, P. Zeiler, and M. Huber, “Overview of Publicly Available Degradation Data Sets for Tasks within Prognostics and Health Management,” arXiv, 2024, doi: 10.48550/arXiv.2403.13694.
    7. M. Huber, “Mit KI im Kleinen beginnen, aber groß denken : WSM im Gespräch,” WSM Nachrichten, Art. no. 3, 2024.
    8. M. Holl, V. Kopp, U. Daub, and U. Schneider, “Exoworkathlon - a systematic study approach to understanding effectiveness of exoskeletons,” in BMT 2024 : Abstracts of the 58th Annual Meeting of the German Society of Biomedical Engineering. 18.-20.09.2024, Stuttgart, Stuttgart, 2024, p. 1.
    9. W. Kraus and M. Huber, “KI und Robotik: Zwei Seiten einer Medaille,” in \#KRoX - das Konradin RobotX Forum : 20.06.2024, Leinfelden-Echterdingen, Leinfelden-Echterdingen, 2024, p. 27 Folien.
    10. M. Huber, “Generative KI in der industriellen Qualitätsprüfung: Erzeugung synthetischer Bilddaten,” in Data Science Forum Rhein-Main : 25.04.2024, Frankfurt am Main, München, 2024, p. 23 Folien.
    11. M. Huber, “KI-Systeme in der industriellen Qualitätsprüfung : Das FQS-Forschungsprojekt AIQualify,” QZ Qualität und Zuverlässigkeit : Die Zeitschrift für Qualitätsmanagement und Qualitätssicherung, vol. 69, Art. no. 1, 2024.
    12. M. Willmann, M. Albus, J. Schnabel, and M. Roth, “Application of quantum annealing for scalable robotic assembly line optimization: a case study,” arXiv, Art. no. Preprint, 2024, doi: 10.48550/arXiv.2412.09239.
    13. M. Huber, “Kognitive Produktionssysteme: Maschinelles Lernen im industriellen Einsatz,” in GSaME Jahrestagung 2024: Intelligente Produktion : 14.10.2024, Stuttgart, Stuttgart, 2024, p. 27 Folien.
    14. T. Bauernhansl and T. Denner, “E2E - durchgängig automatisieren in der Design Chain : Digitale Transformation,” Frankfurter Allgemeine Zeitung, Art. no. Online erschienen am 06.03.2024, 2024.
    15. M. Huber, “Erzeugung synthetische Bilddaten,” in X-KIT Workshop zu den aktuellen Entwicklungen generativer KI und zum Technologietransfer in die Landwirtschaft, den ländlichen Raum und die Lebensmittelkette : 20.-21.02.2024, Berlin, Berlin, 2024, p. 20 Folien.
    16. S. Eigner et al., “Evaluating the combination of transglutaminase and soy protein isolate as wood binder for sustainable additive manufacturing materials,” Procedia CIRP, Art. no. Preprint, 2024.
    17. B. Fresz et al., “The Contribution of XAI for the Safe Development and Certification of AI: An Expert-Based Analysis,” arXiv, 2024, doi: 10.48550/arXiv.2408.02379.
    18. M. Huber, “Herausforderungen beim Einsatz generativer KI in der Wirtschaft,” in Bitkom AI Research Network : 15.11.2024, online, Berlin, 2024, p. 50 Folien.
    19. G. Müller and M. Tröster, “Wie Exoskelette, Daten und digitale Menschmodelle im betrieblichen Gesundheitsmanagement helfen,” in 40. Münchner Gefahrstoff- und Sicherheitstage : 28.-29.11.2024, München, München, 2024, p. 38 Folien.

  4. 2023

    1. M. Huber, “Kognitive Produktionssysteme: KI im industriellen Einsatz,” in Forming Technology Network of Sheet Metal Forming : 22.-23.05.2023, Stuttgart, Stuttgart, 2023, p. 39 Folien.
    2. M. Huber, “Industrielle Bildverarbeitung und KI: Zwei Seiten einer Medaille,” in 35. Control - Internationale Fachmesse für Qualitätssicherung : 09.-12.05.2023, Stuttgart, Frickenhausen, 2023, p. 13 Folien.
    3. T. Bauernhansl, “Nachhaltige Wertschöpfungssysteme - Aktuelle und zukünftige Konzepte,” in Fabrik des Jahres 2022 : Kongress. 22.-23.03.2023, München, München, 2023, p. 10 Folien.
    4. T. Bauernhansl, “Die Produktion von Übermorgen,” in Lernreise \textquotedblProduktion der Zukunft\textquotedbl : 13.-14.09.2023, Karlsruhe, München u.a., 2023, p. 39Folien.
    5. M. Huber, “Interview zum FQS-Forschungsprojekt AIQualify: Framework zur Qualifizierung von KI-Systemen in der industriellen Qualitätsprüfung,” DGQ - Wissen & Forschung, Art. no. Online erschienen am 04.09.2023, 2023.
    6. T. Bauernhansl, “Biologische ergänzt Digitale Transformation : Standpunkt,” wt Werkstattstechnik online, vol. 113, Art. no. 3, 2023.
    7. S. Kärcher and T. Bauernhansl, “Kommt das Cyber-physische Matrixproduktionssystem? : Produktivität und Flexibilität in Zielharmonie,” IT & Production : Zeitschrift für erfolgreiche Produktion. Das Industrie 4.0 Magazin, Art. no. 1, 2023.
    8. L. Masia, U. Schneider, C. Maufroy, D. Häufle, S. Wischnewski, and D. Remy, “Exoskelette am Arbeitsplatz und ihr Potenzial zur Prävention von arbeitsbedingten Muskel-Skeletterkrankungen,” Infobrief BVOU, Art. no. 1, 2023.
    9. M. Huber, “Künstliche Intelligenz: gestern, heute, morgen,” in Cologne Futures : Connecting the Dots - Medien als Infrastrukturen. 05.12.2023, Köln, Köln, 2023, p. 36 Folien.
    10. M. Huber, “Zuverlässige KI: Wie gelingt der sichere Einsatz von KI in kritischen Anwendungen,” in Petersberger Strategiedialog : 02.-03.05.2023, Petersberg, Düsseldorf, 2023, p. 25 Folien.
    11. M. Huber, “KI und Robotik in der Produktion,” in Verpackertage 2023 - Der Zuschnitt im Fokus : 19.-21.09.2023, Illertissen, Illertissen, 2023, p. 36 Folien.
    12. M. Huber, “Kognitive Produktionssysteme: KI in der Produktion,” in Staufen C-Day 2023 : 26.-27.09.2023, Bad Teinach, 2023, p. 28 Folien.
    13. M.-L. Schumacher and M. Huber, “Probabilistic Global Robustness Verification of Arbitrary Supervised Machine Learning Models,” in 2nd Workshop on Formal Verification of Machine Learning (WFVML 2023) : 28.07.2023, Honolulu, Hawaii, United States, 2023, p. 11.
    14. M. Huber, “Daten für KI,” in Datenräume in der Industrie 4.0 : Paradigmenwechsel für KI? 26.01.2023, Online, Frankfurt am Main, 2023, p. 30 Folien.
    15. M. Huber, “Dependable AI: Machine Learning in Regulated and Safety-critical Applications,” in Productronica - KI in der Elektronikfertigung : 16.11.2023, München, Frankfurt am Main, 2023, p. 26 Folien.
    16. T. Bauernhansl, “Die biologische Transformation und ihre Potentiale,” in XI. Turnaroundkongress 2023 : Durch multiple Krisen kreativ und flexibel führen - Wie Unternehmen digital und nachhaltig erfolgreich bleiben. 22.-23.06.2023, Bonn-Bad Godesberg, Erfurt, 2023, p. 20 Folien.
    17. M. Huber, “Wird der Mensch morgen noch gebraucht? Künstliche Intelligenz und Robotik in der Produktion,” in TMA Stammtisch : 04.07.2023, Stuttgart, Essen, 2023, p. 44 Folien.
    18. M. Huber, “Happy Birthday ChatGPT: Ein Jahr generative KI im Einsatz,” in 4. KI-Kongress: Smarte Maschinen im Einsatz - KI als Produktivitätsbooster : 30.11.2023, Stuttgart, Stuttgart, 2023, p. 39 Folien.
    19. C. Braun and L. Lörcher, “Towards Leveraging the Full Potential of Artificial Intelligence in Medicine: Challenges Related to Medical Image Data : Introducing the Checklist on Challenges of Radiological Image Data (CORID-Checklist),” in AI Health Summit : 23.-24.11.2023, Singapur, Singapur, 2023, p. 1 Poster.
    20. T. Bauernhansl, “Aktuelle Forschungsthemen in den Produktionswissenschaften,” in GSaME Grundprogramm : 20.-30.11.2023, Stuttgart, Stuttgart, 2023, p. 71 Folien.
    21. T. Bauernhansl and R. Miehe, “Was ist Biointelligenz?,” Frankfurter Allgemeine Zeitung, Art. no. Online erschienen am 30.10.2023, 2023.
    22. D. Brajovic, P. Wagner, M. I. Kläb, B. Fresz, M. Huber, and u.a., “Model Reporting for Certifiable AI: A Proposal from Merging EU Regulation into AI Development,” arXiv, Art. no. TPreprint, 2023, doi: 10.48550/arXiv.2307.11525.
    23. G. Schuh, M. Kuhn, T. Bauernhansl, and A. Sauer, “Konzepte zur Gestaltung und Steuerung von Kooperationsmodellen im Maschinen- und Anlagenbau,” in Handbuch Unternehmensorganisation : Strategien, Planung, Umsetzung, Berlin, Heidelberg: Springer, 2023, pp. 1–15. doi: 10.1007/978-3-642-45370-0_29-3.
    24. P. Wagner, T. Nagel, C. Hennebold, M. Huber, and W. Kraus, “KI-Anwendungsfälle in der Produktion,” in Handbuch Industrie 4.0 : Band 1: Produktion, Berlin and Heidelberg: Springer Vieweg, 2023, pp. 71–94. doi: 10.1007/978-3-662-58532-0_167.
    25. M. Huber, “Machine Vision and Dependable AI,” in Industrial-Grade AI Fokustag : 02.02.2023, Stuttgart, Stuttgart, 2023, p. 13 Folien.
    26. M. Huber, “Einsatz von KI in der Produktion,” in Trumpf Data & AI Conference : 18.07.2023, Ditzingen, 2023, p. 26 Folien.
    27. M. Huber, “Industrie 4.0 war gestern - Maschinelles Lernen als Innovationstreiber in der industriellen Produktion,” in Data Science Darmstadt, 11.09.2023, 2023, p. 77 Folien.
    28. T. Bauernhansl, “Biointelligenz bringt Umwelt und Wohlstand in Einklang,” atp magazin, Art. no. 4, 2023.
    29. T. Bauernhansl, “Maschinenhersteller werden zu Dienstleistern,” Frankfurter Allgemeine Zeitung, Art. no. Online erschienen am 12.12.2023, 2023.
    30. I. Effenberger, H. Eigenbrod, A. Frommknecht, C. Jauch, J. Denecke, and M. Huber, “Qualitätssicherung in der Produktion,” in Handbuch Industrie 4.0 : Band 1: Produktion, Berlin and Heidelberg: Springer Vieweg, 2023, pp. 53–69. doi: 10.1007/978-3-662-58532-0_166.
    31. T. Bauernhansl, “Mit Matrixproduktionssystemen flexibel und produktiv werden,” Frankfurter Allgemeine Zeitung, Art. no. Online erschienen am 02.10.2023, 2023.
    32. U. Schneider, U. Daub, V. Kopp, and M. Holl, “Exoworkathlon: a Modular Prospective Study on Occupational Exoskeletons,” in State of the Science : 15.06.2023, Bethesda, Maryland, USA, Washington, D.C., 2023, p. 31 Folien.

  5. 2022

    1. M. Huber, “From AI Research to Practice: Technology Transfer at the KI-Fortschrittszentrum,” in Sciencepreneur : AI Symposium and Start-up Fair. 30.09.2022, Stuttgart, Tübingen, 2022, p. 15Folien.
    2. M. Huber, “Künstliche Intelligenz in produzierenden Unternehmen: Möglichkeiten und Grenzen,” in WAGO Stiftungskolloquium : 10.06.2022, Minden, Minden, 2022, p. 40Folien.
    3. M. Huber, “Manufacturing meets AI: Technology Transfer in the Cyber Valley,” in 20th EMVA Business Conference : 12.-14.05.2022, Brüssel, Belgien, Barcelona, Spanien, 2022, p. 22Folien.
    4. M. Huber, “Zuverlässige KI: Absicherung künstlicher neuronaler Netze,” in Minds Mastering Machines : 01.- 03.06.2022, Karlsruhe, Heidelberg, 2022, p. 30Folien.
    5. T. Bauernhansl, “FUTURESPACE Satelliten: Technologien für die Serienproduktion,” in Raumfahrtkonferenz : Wir sind FUTURESPACE. 24.10.2022, Stuttgart, Stuttgart, 2022, p. 14Folien.
    6. P.-A. Matt, R. Ziegler, D. Brajovic, M. Roth, and M. Huber, “A Nested Genetic Algorithm for Explaining Classification Data Sets with Decision Rules,” arXiv, Art. no. TPreprint, 2022, doi: 10.48550/arXiv.2209.07575.
    7. O. Schwarz, “Biomimetics: Learning from Nature for the Bioeconomy,” in 4. Bioökonomiekongress Baden-Württemberg : 26.-28.09.2022, Stuttgart, Stuttgart, 2022, p. 1Poster.
    8. M. Huber, “Dependable AI: Machine Learning in Safety-critical Applications,” in ZF Data and AI Conference 2022 : 15.11.2022, Online, Friedrichshafen, 2022, p. 26Folien.
    9. M. Huber, “Zuverlässige und vertrauenswürdige Künstliche Intelligenz,” in 81. Heidelberger Bildverarbeitungsforum : Intelligente Vision Systeme. 05.07.2022, Mannheim, Mannheim, 2022, p. 31Folien.
    10. T. Bauernhansl and W. Kraus, “40 Jahre Motek - 40 Jahre Technologieentwicklung in der Produktionsautomatisierung,” in 40. Motek : Internationale Fachmesse für Produktions- und Montageautomatisierung. 04.-07.10.2022, Stuttgart, Frickenhausen, 2022, p. 19Folien.
    11. T. Bauernhansl, “Matrixproduktion : Aktueller Umsetzungsstand und Ausblick,” in 14. Montage-Tagung : 04.05.-05.05.2022, Saarbrücken, Saarbrücken, 2022, p. 28Folien.
    12. M. Huber, “Wie macht man künstliche Intelligenz robust, transparent und erklärbar?,” in Smarte Maschinen im Einsatz - Künstliche Intelligenz in der Produktion : Effizient, sicher und nachhaltig mit KI. 10.05.2022, Stuttgart und Online, Stuttgart, 2022, p. 26Folien.
    13. T. Bauernhansl, “Circular Ecnomy in verschiedenen Branchen : was können wir lernen, was klappt, was nicht?,” in La Biosthétique Alumni Meeting : 12.09.2022, Pforzheim, Pforzheim, 2022, p. 23Folien.
    14. B. Spaeth and M. Huber, “Mit hybrider KI Bauteile aufbereiten : Forschungsprojekt RoboGrind,” IT & Production : Zeitschrift für erfolgreiche Produktion. Das Industrie 4.0 Magazin, Art. no. 7, 2022.
    15. M. Huber, “Explainable AI: Introducing Trust and Comprehensibility to AI Engineering,” in Engineering AI Workshop : Research and Practice of AI Systems Engineering. 23.09.2022, Karlsruhe, Karlsruhe, 2022, p. 12Folien.
    16. M. Huber, “KI-Technologien in der Produktion,” in Künstliche Intelligenz im Produktionsprozess : Digitalisierung souverän gestalten. 18.10.2022, Online, Frankfurt am Main, 2022, p. 28Folien.
    17. M. Huber, “Roboter lernen zu Montieren : Rob-aKademI,” Magazin des Stuttgarter Maschinenbaus, Art. no. 2, 2022.
    18. M. Huber, “Dependable AI in Mobility and Production,” in AI Quality Summit : 02.11.2022, Frankfurt, Offenbach am Main, 2022, p. 15Folien.
    19. T. Bauernhansl and R. Miehe, “Mit Biointelligenz in einen nachhaltigen Innovationsraum,” Magazin des Stuttgarter Maschinenbaus, Art. no. 2, 2022.
    20. M. Huber, “Künstliche Intelligenz: Lernen Maschinen wie Menschen?,” in Künstliche Intelligenz - Antagonismen in der Digitalen Revolution : Vortragsreihe. Ab 06.05.2022, Online und in Düsseldorf, Düsseldorf, 2022, p. 34Folien.
    21. T. Bauernhansl, “Biologische Transformation,” in enter the future : Biologische Transformation - wofür? Und was haben Pilze damit zu tun? 13.10.2022, Weikersheim, Igersheim, 2022, p. 31Folien.
    22. M. Huber, “Erklärbare KI,” in Treffen der KI Community - Thema Erklärbare KI : 02.06.2022, Teamsmeeting, Stuttgart, 2022, p. 12Folien.
    23. M. Huber, “Optimizing Processes and Making Them More Efficient with Al,” Metrology.news, Art. no. Online erschienen am 01.02.2022, 2022.
    24. M. Huber, “Cognitive Production Systems: Machine Learning in Industrial Manufacturing,” in Upper Rhine Artificial Intelligence Symposium 2022 : AI Applications in Medicine and Manufacturing. 19.10.2022, Furtwangen, Furtwangen, 2022, p. 29Folien.
    25. O. Schwarz and K. Protte, “Enzyme assisted Circular Additive Manufacturing (ENCAM) using wood as an example,” in 4. Bioökonomiekongress Baden-Württemberg : 26.-28.09.2022, Stuttgart, Stuttgart, 2022, p. 1Poster.
    26. M. Huber, “AI in Manufacturing: Introduction, Applications and Dependable Use,” in Radar Forum : 09.09.2022, Ulm, Ulm, 2022, p. 46Folien.
    27. M. Huber, “KI für den Mittelstand : low hanging fruits,” in KI-Erfahrungsaustausch : 23.03.2022, Freudenstadt, Freudenstadt, 2022, p. 19Folien.

  6. 2021

    1. M. Huber, “Einsatzpotential Künstlicher Intelligenz in der Pharmazeutischen Industrie,” in Einsatzpotenzial Künstlicher Intelligenz in der Pharmazeutischen Industrie : KI im Einsatz & Matchmaking. 22. April - Kongress/Symposium. Online, Stuttgart, 2021, p. 29Folien.
    2. T. Bauernhansl, “Biointelligenz : Eine neue Perspektive für nachhaltige Wertschöpfung,” Biointelligenz : Gemeinsame Vortragsreihe 2021/22 der Württembergischen Landesbibliothek und der Fraunhofer-Gesellschaft, p. 33Folien, 2021.
    3. I. K. Gauger, “Hybrides maschinelles Lernen für die Automatisierung in der Produktion,” in Vernetzungstreffen des Graduiertennetzwerks des VDI/VDE : Digitale Souveränität in der Wirtschaft. 27. Oktober, Webex-Meeting, Berlin, 2021, p. 11Folien.
    4. M. Huber, W. Kraus, M. Peissner, and T. Renner, “KI-Fortschrittszentrum Lernende Systeme und Kognitive Robotik,” in Virtuelles S-TEC Spitzentreffen: KI - Made im Ländle : 25.02.2021, Digital, Stuttgart, 2021, p. 21Folien.
    5. M. Huber, “Maschinelles Lernen besser verstehen : Mehr Transparenz,” Art. no. 3, 2021.
    6. M. Huber, “Künstliche Intelligenz: Verstehen, Anwenden, Gestalten,” in Berufliche Bildung für eine zukunftsfähige Gesellschaft : Entwicklung von zukunftsrelevanten überfachlichen Kompetenzen. 02. -04.08.2021, Virtuell, Stuttgart, 2021, p. 41Folien.
    7. M. Huber, “KI und Technik,” in Impulse kontrovers : PODIUMSGESPRÄCHE 2021. Künstliche Intelligenz. 26. April 2021. Virtuell, Stuttgart, 2021, p. 46Folien.
    8. M. Huber, “Zuverlässige KI - Absicherung künstlicher neuronaler Netze,” in Forum Künstliche Intelligenz : 21. April 2021, virtuelle Konferenz, Haar, 2021, p. 24Folien.
    9. T. Bauernhansl, “Nachhaltige technologiebasierte Ressourcennutzung : Ressourcen, Daten und Dienste in zyklischen Wertschöpfungssystemen,” in InnoPuls : Vernetzung.Innovation.Wertschöpfung. 10. März 2021. Digitaler Kongress. Bundesministerium für Bildung und Forschung, Berlin u.a., 2021, p. 8Folien.
    10. M. Huber, “Kognitive Produktionssysteme : Maschinelles Lernen im industriellen Einsatz,” in Lernreise Industrie 4.0 live : Abschlussveranstaltung.18. und 19. Mai, Virtuell, Stuttgart, 2021, p. 18 Folien.
    11. M. Huber, “Produktionsdaten sicher und nachvollziehbar nutzen,” Digital Business Cloud : Das Expertenmagazin, Art. no. 1, 2021.
    12. M. Huber, “Zuverlässige KI - Absicherung künstlicher neuronaler Netze,” in Forum Künstliche Intelligenz : 21. April 2021, virtuelle Konferenz, Haar, 2021, p. 24Folien.
    13. M. Huber, “Wie Künstliche Intelligenz und Machine Learning die Wertschöpfung in der Produktion steigern,” in INDUSTRY.forward EXPO 2021 : Technik, Wandel, Zukunft - Smarte Lösungen für die Industrie. 23. Februar - 16. März 2020. Virtuell, Haiger u.a., 2021, p. 25Folien.
    14. M. Huber, “Dependable Al - Machine Learning in Safety-Critical Applications,” in Cyber Valley Entrepreneurship Series : AI in Production & Logistics. 06 May 2021. Virtual Event, Tübingen, 2021, p. 24Folien.
    15. M. Huber, “Künstliche Intelligenz: Der Treiber in der Industrie 4.0,” in Industrie 4.0 in Baden-Württemberg : Themenreihe im März 2021. Thementag: 04.03.2021 - Künstliche Intelligenz. Webinar. IHK Region Stuttgart, Stuttgart, 2021, p. 32Folien.
    16. A. Sauer, “Energieeffizienz und -flexibilität: Ausgewählte Industrienahe Forschungsaktivitäten : in Kooperation mit den Instituten EEP und IFF der Universität Stuttgart,” in Sitzung des BDI-Arbeitskreises Energieforschung und -technologien : 07.05.2021. Digital, Berlin, 2021, p. 21Folien.
    17. N. Burkart and M. Huber, “A Survey on the Explainability of Supervised Machine Learning,” in QuantUniversity Winter School Speaker Series : Session 5. February 16, 2020. Online, Boston, Massachusetts, USA, 2021, p. 35 Folien.
    18. M. Huber, “Potenziale und Grenzen des maschinellen Lernens in produzierenden Unternehmen,” in KI zwischen Mythos und Realität : Gestaltungshorizonte und Gestaltungspotenziale für algorithmische Entscheidungssysteme in Unternehmen und betrieblichen Arbeitswelten - Workshop für betriebliche Anwenderinnen und Anwender. 19.10.2021, Stuttgart, Stuttgart u.a., 2021, p. 29Folien.
    19. M. Huber, “Uncertainty Quantification in Neural Networks: A novel Approach,” in Trustworthy Robotics: Safety, Credibility, Explainability : Workshop. 13.04.2021. European Robotics Forum, Stuttgart u.a., 2021, p. 10Folien.
    20. M. Huber, “Künstliche Intelligenz: Der Treiber in der Industrie 4.0,” in Industrie 4.0 in Baden-Württemberg : Themenreihe im März 2021. Thementag: 04.03.2021 - Künstliche Intelligenz. Webinar. IHK Region Stuttgart, Stuttgart, 2021, p. 32Folien.
    21. M. Huber, “AI Innovation Center \textquotedblLearning Systems and Cognitive Robotics,” in Vortrag bei der EU-Kommission : 11.03.2021. Online, Brüssel, Belgien, 2021, p. 9Folien.
    22. M. Huber, “Einsatzpotential Künstlicher Intelligenz in der Pharmazeutischen Industrie,” in Einsatzpotenzial Künstlicher Intelligenz in der Pharmazeutischen Industrie : KI im Einsatz & Matchmaking. 22.04.2021 - Kongress/Symposium. Online, Stuttgart, 2021, p. 29Folien.
    23. B. Spaeth, T. Bauernhansl, and J. Full, “Industrie 4.0: Die biologische Transformation kommt,” Forschung Leben : Das Magazin der Universität Stuttgart, Art. no. 2, 2021.
    24. B. Spaeth, T. Bauernhansl, and M. Heymann, “Biointelligenz,” Magazin des Stuttgarter Maschinenbaus, Art. no. 1, 2021.
    25. A. Sauer, “Fraunhofer-Institut für Produktionstechnik und Automatisierung IPA : in Kooperation mit den Instituten EEP und IFF der Universität Stuttgart,” in Fahrerlose Transportsysteme (FTS) und mobile Roboter : Chancen, Technologien, Wirtschaftlichkeit. Fraunhofer IPA Seminar F 362. 22.09.2021, Stuttgart, Stuttgart, 2021, p. 13Folien.
    26. M. Huber, “Kognitive Produktionssysteme: Maschinelles Lernen in der Produktion,” in 27. DFO Automobil-Tagung European Automotive Coating : 15.06.2021 - 16.06.2021, Online, Neuss, 2021, p. 24Folien.
    27. M. Vogel, M. Huber, and S. Oppold, “Blick in die Black Box,” Forschung Leben : Das Magazin der Universität Stuttgart, Art. no. 1, 2021.
    28. T. Bauernhansl, “Nachhaltige technologiebasierte Ressourcennutzung : Ressourcen, Daten und Dienste in zyklischen Wertschöpfungssystemen,” in InnoPuls : Vernetzung.Innovation.Wertschöpfung. 10.03.2021. Digitaler Kongress. Bundesministerium für Bildung und Forschung, Berlin u.a., 2021, p. 8Folien.
    29. M. Huber, “Immer noch unterschätzt : Gastbeitrag zum Thema Künstliche Intelligenz,” VDMA Magazin, Art. no. 8, 2021.
    30. T. Bauernhansl, “Gründen im Bereich der Personalisierten Medizin - Chancen und Herausforderungen,” in Wie kommt das Neue in die Medizin? : Den Start-up-Hindernislauf zum ersten Nutzer bewältigen. 4. Februar 2021. Online-Eventreihe, Tübingen, 2021, p. 13Folien.
    31. M. Huber, “Wie Künstliche Intelligenz und Machine Learning die Wertschöpfung in der Produktion steigern,” in INDUSTRY.forward EXPO 2021 : Technik, Wandel, Zukunft - Smarte Lösungen für die Industrie. 23.02.2021 - 16.03.2021. Virtuell, Haiger u.a., 2021, p. 25Folien.
    32. B. Reckter, U. Schneider, and V. Kopp, “Muskelkraftverstärker,” VDI Nachrichten : Technik, Wirtschaft, Gesellschaft, Art. no. 42, 2021.
    33. N. Burkart and M. Huber, “A Survey on the Explainability of Supervised Machine Learning : Vorstellung des Überblickspapiers,” in Meeting bei Aggregate Intellect : 03.02.2021, Virtuell, Toronto, Kanada, 2021, p. 35Folien.
    34. M. Huber, “Kognitive Produktionssysteme in der intelligenten Fabrik,” in Maschinenbautag : KI - Künstliche Intelligenz im Maschinenbau. 14.04.2021, Digital, Konstanz, 2021, p. 39Folien.
    35. M. Huber, “KI für die Produktion? Aber sicher!,” in 63. Treffen des Arbeitskreises \textquotedblTechnik\textquotedbl des FV Holzbearbeitungsmaschinen : 20.10.2021, MS Teams-Besprechung, Frankfurt am Main, 2021, p. 18Folien.
    36. M. Huber, “Manufacturing meets AI : Technology Transfer in the Cyber Valley,” in Big-Data.AI Summit : 21.04.2021 und 22.04.2021, Virtuell, Berlin, 2021, p. 21Folien.
    37. M. Huber, “Dependable AI - Machine Learning in Safety Critical Applications,” in Expertennetzwerk AI Circle : 12.05.2021. Online, Berlin, 2021, p. 26Folien.
    38. M. Huber, “AI Innovation Center Learning Systems and Cognitive Robotics,” in Vortrag bei der EU-Kommission : 11 March 2021. Online, Brüssel, Belgien, 2021, p. 9Folien.
    39. N. Burkart and M. Huber, “A Survey on the Explainability of Supervised Machine Learning : Vorstellung des Überblickspapiers,” in Meeting bei Aggregate Intellect : 03.02.2021, Virtuell, Toronto, Kanada, 2021, p. 35Folien.
    40. T. Bauernhansl, “Gründen im Bereich der Personalisierten Medizin - Chancen und Herausforderungen,” in Wie kommt das Neue in die Medizin? : Den Start-up-Hindernislauf zum ersten Nutzer bewältigen. 4. Februar 2021. Online-Eventreihe, Tübingen, 2021, p. 13Folien.
    41. R. Neuhaus, “CNT-actuators for adaptive building skins - Update on potentials and limitations,” in NanoCarbon Annual Conference 2021 : Presentations - Short Pitches - Networking. March 02 and 03, 2021 Online event, Würzburg, 2021, p. 39Folien.
    42. B. Spaeth, “Künstliche Intelligenz zu vermieten : Eine neue Fraunhofer-Studie zeigt, wie kleine und mittlere Unternehmen KI nutzen können,” Industrie 4.0 Management : Gegenwart und Zukunft Industrieller Geschäftsprozesse, vol. 37, Art. no. 5, 2021.
    43. M. Huber, “How Artificial Intelligence Improves Robotics,” in Dresden Robotics Festival : \#inmotion. 16.-22. September. Dresden und Virtuell, Dresden, 2021, p. 25Folien.
    44. M. Huber, “Machine Learning: From Patterns in Data to Bayes and Beyond,” in Forschungskolloquium der TU Darmstadt : Fachbereich Elektrotechnik und Informationstechnik. 09.11.2021, Online, Darmstadt, 2021, p. 39Folien.
    45. M. Huber and M. El-Shamouty, “Dependable AI - Machine Learning in Safety Critical Applications,” in Expertennetzwerk AI Circle : 12.05.2021. Online, Berlin, 2021, p. 26Folien.
    46. M. Huber, “Künstliche Intelligenz für die Produktion: Grundlagen, Anwendungen und Herausforderungen,” in Fragen an KollegIn KI : Einführung in die Wissenschaftskommunikation. FÜSQ-Seminarreihe. Programmtag: 22. November 2021, Stuttgart, Stuttgart, 2021, p. 39Folien.
    47. M. Huber, “Künstliche Intelligenz: Lernen Maschinen wie Menschen?,” Biointelligenz : Gemeinsame Vortragsreihe 2021/22 der Württembergischen Landesbibliothek und der Fraunhofer-Gesellschaft, p. 37Folien, 2021.

  7. 2020

    1. T. Bauernhansl, “Wirtschaft 2025 : Profitiert die regionale Wirtschaft von den globalen Megatrends,” in Neujahrsempfang 2020 der IHK Bezirkskammer Esslingen-Nürtingen : 06.01.2020, Wernau, Stuttgart, 2020, p. 38Folien.
    2. T. Bauernhansl, “Herausforderungen in der technologiegetriebenen Welt : Digitale und biologische Transformation als Chance für Baden-Württemberg,” in Club-Veranstaltung im Lions Club Remstal : 21.09.2020, Kernen-Stetten, Remstal, 2020, p. 23Folien.
    3. F. Eiling, “Explainable AI: Unlock the black-box : Vortrag am 11.12.2020: KI in der Produktion,” in Future Work Talks : Online-Seminar. 16.10.2020, 11.11.2020 und 11.12.2020, Stuttgart, 2020, p. 19Folien.
    4. C. Hennebold, “Künstliche Intelligenz : Einführung und technische Anforderungen,” in Fraunhofertage bei NetApp : 04. März 2020, Kirchheim, Kirchheim, 2020, p. 34Folien.
    5. T. Bauernhansl and V. Kübler, “Transformation der Wirtschaft : Wie Technologien Industriesektoren radikal verändern,” in 34. Stuttgarter Controlling & Management Forum : Transformation und Integration von Führungsfunktionen und Unternehmenssteuerung - Gestärkt aus der Corona-Krise 22./23. September 2020, Kostenfreie Webkonferenz, Stuttgart, 2020, p. 20 Folien.
    6. M. Huber and B. Voß, “Künstliche Intelligenz und Biointelligenz : Abgrenzung, Integration und Perspektive,” in 3. Internationaler Bioökonomiekongress Baden-Württemberg : 21. - 22. September 2020, Online, Stuttgart, 2020, p. 14Folien.
    7. M. Huber, “KI in der Produktion und Robotik,” in Digitale Eröffnung von KI-noW : 7. Dezember 2020, online, Stuttgart, 2020, p. 13Folien.
    8. M. Huber, “Künstliche Intelligenz und Maschinelles Lernen in der Produktion,” in 9. Ressourceneffizienz- und Kreislaufwirtschaftskongress Baden-Württemberg : 7. und 8. Oktober 2020, Virtuell, Stuttgart, 2020, p. 13Folien.
    9. M. Huber, “Optimierungspotentiale durch Anwendung der Künstlichen Intelligenz speziell in der Pharma-Branche,” in 14. Sitzung der AG Pharmadialog Baden-Württemberg : Dienstag, 29. September 2020. Videokonferenz, Stuttgart, 2020, p. 17Folien.
    10. M. Huber, “Künstliche Intelligenz in der Produktion,” in Doktoranden-Kolloquium der Graduiertenschule GSaME der Universität Stuttgart : 17. Januar 2020, Stuttgart, Stuttgart, 2020, p. 44Folien.
    11. M. Huber, “Cognitive Production Systems : Machine Learning in Production,” in Cognitive Manufacturing : Machine Learning - Big Data Analytics - Smart Manufacturing. June 29 - 30, 2020. Online, 2020, p. 32Folien.
    12. T. Bauernhansl, “Das neue Normal\textquotedbl- Was bleibt, was kommt, was verändert sich?,” in Das neue Normal\textquotedbl - Wirtschaftsrat Vortragsreihe : 29. Mai 2020, Virtuell, Stuttgart, 2020, p. 55Folien.
    13. N. Schaaf, “Open the Black Box - Erklärbare Künstliche Intelligenz,” in Robotic & Automation Wednesday : Webinarreihe. Ab 03. Juni 2020, Stuttgart, 2020, p. 22Folien.
    14. T. Bauernhansl, “Die technologiegetriebene Welt 2020 : Wie die digitale und biologische Transformation in Krisenzeiten die industrielle Wertschöpfungskette verändert,” in Strategiemeeting Dürr : 03.07.2020, Stuttgart, Stuttgart, 2020, p. 61Folien.
    15. M. Huber, “Kognitive Produktionssysteme : Maschinelles Lernen im industriellen Einsatz,” in Forum Künstliche Intelligenz : 14. Mai 2020, virtuell, Haar, 2020, p. 37Folien.
    16. M. Huber, “Quality & Material - KI als Enabler für eine ressourceneffiziente Produktion,” in 9. Ressourceneffizienz- und Kreislaufwirtschaftskongress Baden-Württemberg : 7. und 8. Oktober 2020, Virtuell, Stuttgart, 2020, p. 13 Folien.
    17. M. Huber, C. Jauch, and M. Teschner, “Bildverarbeitung für Robotik und Qualitätssicherung : Intelligent sehen,” JavaSPEKTRUM, Art. no. 5, 2020.
    18. S. Hintermayr, J. Siegert, T. Schlegel, and S. Bogenrieder, “Corona-Schutz, made im Ländle : Face-Shields der Uni Stuttgart,” Stuttgarter Nachrichten : süddeutsche Tageszeitung, Art. no. TErschienenam28.05.2020, 2020.
    19. M. Huber, “Mehr Autonomie wagen: Deep Learning und Reinforcement Learning in der Robotik,” in 5. Fachkonferenz Maschinelles Lernen in der Produktion : Machine Learning for Cyber Physical Systems. 12.-13. März 2020, Berlin, Berlin, 2020.
    20. G. Reinhart and T. Bauernhansl, “Industrie 4.0 - und was nun? : Editorial,” wt Werkstattstechnik online, vol. 110, Art. no. 3, 2020.
    21. M. Huber, “Wie wird die KI transparent? : Einführung und Methoden,” in Erklärbarkeit und Transparenz von KI-Methoden : KI-Innovationswettbewerb. 10. Juni 2020, online, Berlin, 2020, p. 12Folien.
    22. M. Huber, “Erklärbare Künstliche Intelligenz : Eine Einführung,” in Versammlung der Jusos : 25.05.2020, virtuell, Stuttgart, 2020, p. 46Folien.
    23. M. Huber, “KI in der Fertigung und Qualitätssicherung der Zukunft,” in Das intelligente KMU : 24. September 2020. Virtuelle Konferenz, Berlin, 2020, p. 31Folien.
    24. M. Huber, “KI-Fortschrittszentrum \textquotedblLernende Systeme,” in Open Innovation Kongress Baden-Württemberg 2020 : Open Innovation - So spielen Sie mit. 02. März 2020, Stuttgart, Stuttgart, 2020, p. 3Folien.
    25. T. Bauernhansl, “Biointelligence - a new Innovation Space for Sustainable Industries,” in Tomorrow : The McKinsey Berlin Conference. November 13, 2020. Remotely, Berlin, 2020, p. 14Folien.
    26. T. Bauernhansl, “Automating the Automation : Consistent end-to-end processes in the context of manufacturing,” in Advances in Automotive Production Technology - Theory and Application : Stuttgart Conference on Automotive Production (SCAP 2020), Wiesbaden: Springer Vieweg, 2020, p. 22Folien.
    27. J. Witte and R. Neuhaus, “Gebäudehaut, die atmet,” Forschung Leben : Das Magazin der Universität Stuttgart, Art. no. 1, 2020.
    28. M. Huber, “Artificial Intelligence,” in Technology Innovation Bootcamp : 30. März, 2020, p. 41Folien.
    29. M. Huber, “KI in der Produktion,” in FabOS Kick-Off-Event : 11. Februar 2020, Stuttgart, Stuttgart, 2020, p. 40Folien.
    30. N. Schaaf, “Open the Black Box : Erklärbare Künstliche Intelligenz,” in Besuch von Rudolf Scharping / RSBK AG : 11. März 2020, Stuttgart, Stuttgart, 2020, p. 22 Folien.
    31. M. Huber, “Künstliche Intelligenz treibt Innovationen,” Ideen- und Innovationsmanagement, Art. no. 4, 2020.
    32. R. Butscher and M. Huber, “Interview zur Künstlichen Intelligenz: ,,Schluss mit der Black Box!,” Bild der Wissenschaft, Art. no. 6, 2020.
    33. M. Huber, “Maschinelles Lernen besser verstehen : Mehr Transparenz für die Arbeitsweise neuronaler Netze,” AI Spektrum : Das Magazin für Künstliche Intelligenz, pp. 25–27, 2020.
    34. M. Huber, “Maschinelles Lernen und Bildverarbeitung in der Fertigung und Qualitätssicherung,” in Forum Bildverarbeitung 2020 : 26.-27. November 2020. Virtuell, Karlsruhe: KIT Scientific Publishing, 2020, p. 37Folien.
    35. M. Huber, “Innovative Arbeitswelten: KI und die Zukunft der Arbeit,” in Jahreskongress Berufliche Bildung : Lern- und Arbeitswelten zukunftsfähig gestalten. 07. / 08. Dezember 2020 Virtuell, Stuttgart, 2020, p. 29Folien.
    36. M. Huber, “What’s in forEL? Anwendungstransfer Cyber Valley - Industrie,” in Robotic & Automation Wednesday : Webinarreihe. Ab 03. Juni 2020, Stuttgart, 2020, p. 34Folien.
    37. H. Weik and E. Colangelo, “Produktionsdaten ganz einfach auswerten : Industrie 4.0,” IndustryArena : eMagazine. Fachmagazin für Fertigungstechnik und Automatisierung, Art. no. 3, 2020.
    38. T. Bauernhansl, “Digital Transformation in Production-Core and fields of action,” in International Production Meeting : 29.07.2020, Günzburg, Weißenhorn, 2020, p. 48Folien.
    39. M. Kaufmann, I. Effenberger, and M. Huber, “Measurement Uncertainty Assessment for Virtual Assembly,” in Sensor and Measurement Science International 2020 : 22-25 June 2020, 2020, pp. 339–340. doi: 10.5162/SMSI2020/P4.1.

  8. 2019

    1. T. Bauernhansl and G. Reinhart, “Innovationssprünge durch Industrie 4.0 ermöglichen eine nachhaltige Produktion : Editorial,” wt Werkstattstechnik online, vol. 109, Art. no. 3, 2019.
    2. T. Nagel, “Grundlagen des Maschinellen Lernens: Vorgehensmodell,” in Kognitive Produktionssysteme : Künstliche Intelligenz im industriellen Einsatz. 25. und 26. November 2019, Stuttgart, Stuttgart, 2019, p. 39Folien.
    3. M. Huber, “Artificial Intelligence Perspective on Mobile Robotics,” in Autonomous Machines World 2019 : Cognition and intelligence in industrial machines. July 1-2, 2019, Berlin, Berlin, 2019, p. 35Folien.
    4. T. Bauernhansl, E. Groß, and J. Siegert, “Learning Factory AIE at IFF, University of Stuttgart, Germany,” in Learning Factories : Concepts, Guidelines, Best-Practice Examples, Cham, Schweiz: Springer Nature, 2019, pp. 383–388.
    5. C. Fries et al., “Fluide Fahrzeugproduktion : BMBF-geförderter Forschungscampus für die Mobilität der Zukunft,” VDI-Z : Integrierte Produktion, vol. 161, Art. no. 12, 2019.
    6. M. Huber, “Artificial Intelligence : Introduction and Industrial Application,” in 10. Future Lab der Boehringer Ingelheim GmbH : 06. November 2019, Biberach, Biberach, 2019, p. 35Folien.
    7. R. Neuhaus, T. Bauernhansl, I. Kolaric, and C. Glanz, “Ionic EAP actuators and arrays - towards cost-efficient manufacturing & integration,” in Advances and Challenges in Transferring EAP Technology Into Industry : 2nd International EAP-Seminar. 04 July 2019, Stuttgart, Stuttgart, 2019, p. 74Folien.
    8. T. Bauernhansl, “The Future of Industrie 4.0,” in The 3rd COMAC International Science and Technology Innovation Week : September 2-6, 2019, Shanghai, China, Shanghai, 2019, p. 23Folien.
    9. T. Bauernhansl, “Die digitale Transformation der Fabriken - Wunsch und Wirklichkeit,” in Smarte Maschinen im Einsatz : Künstliche Intelligenz im Unternehmen. 15. Oktober 2019, Stuttgart, Leinfelden-Echterdingen: Konradin Verlag R. Kohlhammer, 2019, p. 35Folien.
    10. A. Mayer-Grenu and M. Huber, “Selbstoptimierung in der Fabrikhalle,” Forschung Leben : Das Magazin der Universität Stuttgart, Art. no. 12, 2019.
    11. T. Bauernhansl, “Biologische Transformation - Die nachhaltige Revolution der Wertschöpfung?!,” in Herbstsitzung des Wirtschaftsbeirats der Stadt Ingolstadt : 08. Oktober 2019, Ingolstadt, Ingolstadt, 2019, p. 30Folien.
    12. M. Huber, “Artificial Intelligence / Machine Learning Production,” in Entwicklertag der Bosch Packaging Technology GmbH : 26. Februar 2019, Waiblingen, Waiblingen, 2019, p. 37Folien.
    13. R. Neuhaus, “Electroactive CNT-Polymer-Actuators : Electroactive CNT-Polymer-Actuators. Pitch,” in NanoCarbon : Annual Conference, February 26 and 27, Würzburg, Würzburg, 2019, p. 26Folien.
    14. M. Huber, “xAI: Nachvollziehbarkeit maschineller Lernverfahren am Beispiel neuronaler Netze,” in Bitkom AI Research Network : E-Lecture, 19. Juli 2019, Berlin, 2019, p. 42Folien.
    15. M. Huber, “xAI: Erklärbarkeit von KI,” in Fokus: Zukunft : Unser Leben 2050. 28. November 2019, Karlsruhe, Karlsruhe, 2019, p. 10Folien.
    16. M. Huber, “Daten sind der Schlüssel für maschinelles Lernen : Künstliche Intelligenz ermöglicht vorausschauende Wartung und neue datenbasierte Dienstleistungen,” mav : Innovation in der spanenden Fertigung, 2019.
    17. T. Bauernhansl and M. Wolperdinger, “Producing Sustainability with Biointelligent Systems : The Biological Transformation of Value Adding in the Context of the Bioeconomy,” in Biointelligente Produkte und Produktion : Die nachhaltige Revolution der Industrie. 15.-19. Mai 2019, Stuttgart u.a., 2019, p. 19Folien.
    18. T. Bauernhansl, “Industrie 4.0 - Key Enabling Technologies,” in ABB Distribution Solutions Innovation Day 2019 : June 18-19, 2019, Frankfurt, Germany, Zürich, 2019, p. 40Folien.
    19. R. Miehe, J. Full, T. Bauernhansl, and A. Sauer, “Biointelligenz : Neue Chancen für eine nachhaltige industrielle Wertschöpfung,” Industrie 4.0 Management : Gegenwart und Zukunft Industrieller Geschäftsprozesse, vol. 35, Art. no. 1, 2019.
    20. T. Bauernhansl, “Mass Personalization - Mit kognitiven Produktionssystemen die Stückzahl 1 entwickeln und produzieren,” in Fastems Open House : Losgrößenunabhängige Fertigung - so wird’s gemacht!. 05. und 06. Juni 2019, St. Ingbert, Göppingen, 2019, p. 40Folien.
    21. T. Bauernhansl, “Die digitale Transformation - Wohin geht die Reise?,” in Network of Excellence der ebm-papst Mulfingen GmbH & Co. KG : 06. Juni 2019, Mulfingen, Mulfingen, 2019, p. 46Folien.
    22. M. Huber, “Grundlagen des Maschinellen Lernens: Algorithmen,” in Kognitive Produktionssysteme : Künstliche Intelligenz im industriellen Einsatz. 25. und 26. November 2019, Stuttgart, Stuttgart, 2019, p. 53Folien.
    23. M. Huber, “Explainable AI - Introduction and Application to Neural Networks,” in AI-Monday des Cyber Valley : 18. November 2019, Tübingen, Tübingen, 2019, p. 31Folien.
    24. T. Bauernhansl, “Cognitive Production Systems - Technologies and Business Impacts,” in NEXCON : First International Virtual Congress on Smart Manufacturing. February 28th, 2019, online only, Stuttgart, 2019, p. 42Folien.
    25. K. Kleeberger, M. Huber, and A. Wolf, “Mit Simulationen schneller zur Anwendung,” Fabriksoftware : Die digitale Fabrik realisieren, Art. no. 2, 2019.
    26. T. Bauernhansl, “Wandlungsfähige Automobilproduktion der Zukunft : Digitale Wertschöpfungssysteme und Gestaltungsrichtlinien auf dem Weg zu einer smarten Automobilproduktion,” in 1. Stuttgarter Tagung zur Zukunft der Automobilproduktion : Der Weg zur wandlungsfähigen Produktion. 26. September 2019, Stuttgart, Stuttgart, 2019, p. 36 Folien.
    27. R. Neuhaus et al., “Ionic CNT Actuators and Arrays - towards Cost-Efficient Manufacturing through Scalable Dispersions and Printing Processes,” in 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics : July 8-12 2019, Hong Kong, China, Piscataway, NJ: IEEE Operations Center, 2019, p. 47 Folien.
    28. P. Dunau, M. Huber, and J. Beyerer, “Gaussian Process based Dynamic Facial Emotion Tracking.,” in 2019 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2019) : May 6-9, 2019, Taipei, Taiwan, Piscataway, NJ, USA: IEEE Press, 2019, p. 14 Folien.
    29. M. Huber, “Machine Learning für Produktion und Robotik,” Automationspraxis, vol. 2019, Art. no. 11, 2019.
    30. M. Huber and N. Schaaf, “Extraktion von Erklärungen zu Produktionsprozessen aus künstlichen Neuronalen Netzen,” in Blick in die Blackbox : Nachvollziehbarkeit von KI-Algorithmen in der Praxis, Berlin, 2019, pp. 62–72.
    31. M. Huber, “Einführung in die Künstliche Intelligenz und das Maschinelle Lernen in der Produktion,” in 2. Open Lab Day am Zentrum für Cyber Cognitive Intelligence (ZCCI) : 17. Januar 2019, Stuttgart, Stuttgart, 2019, p. 31Folien.
    32. M. Schleef and M. Huber, “Künstliche Intelligenz und Maschinelles Lernen in der Produktion,” in ZVEI Mitgliederversammlung : Mitgliederversammlung des Zentralverband Elektrotechnik und Elektronikindustrie e.V.. 25. September 2019, Weimar, Frankfurt am Main, 2019, p. 31Folien.
    33. M. Huber, M. El-Shamouty, K. Kleeberger, and A. Lämmle, “Simulationsbasiertes maschinelles Lernen in der Automatisierung,” in Trends in der industriellen Mess- und Automatisierungstechnik : Von der Messung zur Information. VDI-Expertenforum. 28.-29. November 2019, Karlsruhe, Düsseldorf, 2019, p. 41Folien.
    34. T. Bauernhansl, “Die digitale Transformation - Herausforderungen für Baden-Württemberg,” in Transformationskonferenz der IG Metall Baden-Württemberg : 21. Februar 2019, Ludwigsburg, Stuttgart, 2019, p. 42Folien.
    35. M. Huber, Ed., “Data are the key,” in Automatica 2020 : The Leading Exhibition for Smart Automation and Robotics, München, 2019, p. 2.
    36. M. Huber, “Cognitive Production Systems,” in Knorr-Bremse Digital Days : 07. November 2019, München, 2019, p. 34Folien.
    37. T. Bauernhansl, “Produktion der Zukunft : Was geht davon heute schon?,” in Fast Ramp-Up Challenge und MindSphere Ecosystem : 21. März 2019, Stuttgart, Stuttgart, 2019, p. 38Folien.
    38. M. Huber, “Cognitive Production Systems : AI in Production,” in Seminar am Max-Planck-Institut für Intelligente Systeme : 09. Juli 2019, Stuttgart, Stuttgart, 2019, p. 49Folien.
    39. T. Bauernhansl, “Digital Transformation in Automotive Industry : Consequences for Body in White Production,” in 6th International Automotive Conference : Joining Smart Technologies. 08.-09. Mai 2019, Sattledt, Austria, Neuhof-Dorfborn, 2019, p. 42Folien.
    40. M. Huber, “Spielerisch besser,” in A&D Kompendium 2019/2020 : Die Macher der Automation, München: Publish-Industry Verlag, 2019, pp. 214–215.
    41. M. Huber, “Center for Cyber Cognitive Intelligence,” in Global Challenges Science Week : International interdisciplinary days of Grenoble Alpes. 3 to 6 June 2019, Grenoble, France, Grenoble, 2019, p. 43Folien.
    42. M. Huber and N. Schaaf, “Extracting Explanations from Deep Neural Networks,” in European Robotics Forum : 20-22 March 2019, Bucharest, Romania, Brüssel, 2019, p. 9Folien.
    43. M. Huber, “Simulation-driven Reinforcement Learning in Robotics,” in European Robotics Forum : 20-22 March 2019, Bucharest, Romania, Brüssel, 2019, p. 11Folien.
    44. E. Westkämper, “Industrie 4.0 - Vernetzung informationsverarbeitender Prozesse : Editorial,” wt Werkstattstechnik online, vol. 109, Art. no. 6, 2019.
    45. N. Schaaf, “KI in der Produktion einsetzen? Quick Checks geben die Antwort!,” in S-TEC Spitzentreffen Künstliche Intelligenz : Inklusive der Fachforen \textquotedblKognitive Produktionssysteme für eine intelligente, wettbewerbsfähige Fabrik\textquotedbl und \textquotedblQuantensprung in der Robotik und Automatisierungstechnik durch KI\textquotedbl. 07. November 2019, Stuttgart, Stuttgart, 2019, p. 22Folien.
    46. T. Nagel, “Kognitive Produktionssysteme : Künstliche Intelligenz in der Produktion,” in GSaME Kernprogramm : Grundprogramm \textquotedblKI in der Produktion\textquotedbl. 26. März 2019, Stuttgart, Stuttgart, 2019, p. 27Folien.
    47. M. Huber, “Kognitive Produktionssysteme - Maschinelles Lernen im industriellen Einsatz,” in Künstliche Intelligenz im Produktionsumfeld : ERFA-Veranstaltung des VDMA Baden-Württemberg, 12. März 2019, Kornwestheim, Frankfurt am Main, 2019, p. 30Folien.
    48. K. Pfeiffer and M. Huber, “Mobile Robotik, KI und die Cloud : Eine gemeinsame Betrachtung,” Automationspraxis, Art. no. 8, 2019.
    49. T. Bauernhansl, “Künstliche Intelligenz als nächster Hype nach Industrie 4.0? Was bedeutet das für zukünftige Montage-Konzepte?,” in Montage 2019 : 18. Management Circle Jahrestagung. 19. und 20. Februar 2019, München, Eschborn, 2019, p. 49 Folien.
    50. M. Huber, “Daten als Schlüssel für maschinelles Lernen : Zentrum für Cyber Cognitive Intelligence (CCI) am Fraunhofer IPA hilft beim Einstieg,” Automationspraxis, Art. no. 1–2, 2019.
    51. M. Huber, “Zentrum für Cyber Cognitive Intelligence,” in Arena 2036 for You : 17. April 2019, Stuttgart, Stuttgart, 2019, p. 19Folien.
    52. M. Huber, “Kognitive Produktionssysteme : Künstliche Intelligenz im praktischen Einsatz,” in Smarte Maschinen im Einsatz : Künstliche Intelligenz im Unternehmen. 15. Oktober 2019, Stuttgart, Leinfelden-Echterdingen: Konradin Verlag R. Kohlhammer, 2019, p. 41Folien.
    53. M. Huber, “Künstliche Intelligenz - Was ist das eigentlich?,” in Netzwerktreffen Allianz Industrie 4.0 \textquotedblKünstliche Intelligenz in der Produktion\textquotedbl : 12. Dezember 2019, Stuttgart, Stuttgart, 2019, p. 49Folien.
    54. M. Huber, “Künstliche Intelligenz: Einführung und Anwendungen,” in Jahrestagung VDMA Batterieproduktion 2019 : Batterieproduktion: Wettbewerbsfähig und nachhaltig. 02. und 03. Dezember 2019, Ditzingen, 2019, p. 40Folien.
    55. W. Kraus, B. Winkler, and M. Huber, “Maschinelle Lernverfahren für Roboteranwendungen,” atp magazin : Transforming Automation, Art. no. 1–2, 2019.
    56. M. Huber, “Explainable Artificial Intelligence : Introduction and Application to Neural Networks,” in State of Technology and Research in Industrial AI : KEX AG Workshop. 01. Oktober 2019, Aachen, Aachen, 2019, p. 39Folien.
    57. M. Huber, “Kognitive Produktionssysteme : KI im industriellen Einsatz,” in Vorstandssitzung des VDMA Baden-Württemberg : 16. Juli 2019, Baden-Baden, Stuttgart, 2019, p. 36Folien.
    58. A. Schlicht and T. Bauernhansl, “Industrie 4.0: Die Zeit des Herumspielens ist vorbei,” finanzen.de : Einfach gut beraten, Art. no. TOnline08.07.2019, 2019.
    59. R. Neuhaus, C. Glanz, I. Kolaric, and T. Bauernhansl, “Electroactive CNT-Polymer-Actuators : State of Science and Technology and their slow Approach into Architectural Applications,” in NanoCarbon : Annual Conference, February 26 and 27, Würzburg, Würzburg, 2019, p. 1Poster.
    60. M. Huber, “Welcome to the Center for Cyber Cognitive Intelligence (CCI),” in Visit of a Delegation of AI-Experts : 16.-17. Mai 2019, Karlsruhe / Stuttgart, Karlsruhe / Stuttgart, 2019, p. 6Folien.
    61. T. Bauernhansl, “Digitalisierung trifft auf Biologisierung : Biologische Transformation der industriellen Wertschöpfung,” Automationspraxis, vol. 14, Art. no. 4, 2019.
    62. R. Neuhaus, “Polymeraktoren : Integration künstlicher Muskeln in Textilfassaden,” in Symposium SFB 1244 : Adaptive Hüllen und Strukturen für die gebaute Umwelt von morgen. 20. September 2019, Stuttgart, Stuttgart, 2019, p. 74Folien.
    63. T. Bauernhansl, “Biologische Transformation : Die nachhaltige Revolution der Industrie,” in YPO Gold : 06. April 2019, Stuttgart, Stuttgart, 2019, p. 41Folien.
    64. M. Huber, “Reinforcement Learning,” in Kognitive Produktionssysteme : Künstliche Intelligenz im industriellen Einsatz. 25. und 26. November 2019, Stuttgart, Stuttgart, 2019, p. 36Folien.
    65. T. Bauernhansl, “Mechanical Engineering in Technology-Driven Societies,” in IndustriALL Global Union : Weltkonferenz Maschinenbau. 11.-13. September 2019, Stuttgart, Genf, 2019, p. 47Folien.
    66. M. Huber, B. Spaeth, and D. Stock, “Neues KI-Zentrum in Stuttgart,” WGP-Newsletter, p. 4, 2019.
    67. M. Huber, “Künstliche Intelligenz : Einführung und industrielle Nutzung,” in S-TEC Spitzentreffen Künstliche Intelligenz : Inklusive der Fachforen \textquotedblKognitive Produktionssysteme für eine intelligente, wettbewerbsfähige Fabrikund \textquotedblQuantensprung in der Robotik und Automatisierungstechnik durch KI\textquotedbl. 07. November 2019, Stuttgart, Stuttgart, 2019, p. 23 Folien.
    68. M. Huber, “Innovationstreiber Künstliche Intelligenz : Möglichkeiten und Grenzen,” in Forschung als Innovationstreiber : Chancen und Grenzen Künstlicher Intelligenz. 30. April 2019, Stuttgart, Stuttgart, 2019, p. 33 Folien.
    69. M. Huber, “Künstliche Intelligenz in der Produktion der Zukunft,” in Nokia Innovation Days : 26. und 27. November 2019, Stuttgart, Stuttgart, 2019, p. 33Folien.
    70. M. Huber, “Einführung und Überblick: Was ist Künstliche Intelligenz im industriellen Einsatz?,” in Kognitive Produktionssysteme : Künstliche Intelligenz im industriellen Einsatz. 25. und 26. November 2019, Stuttgart, Stuttgart, 2019, p. 51Folien.
    71. M. Huber, “Künstliche Intelligenz und Maschinelles Lernen in der Produktion,” in Technologietag 2019 : Datenbasierte Produktion - Mehrwerte smarter Algorithmen & Services. 05. und 06. Februar 2019, Stuttgart, Stuttgart, 2019, p. 28Folien.
    72. M. Huber, “Einführung in die Künstliche Intelligenz und Nutzung in der Produktion,” in IZS Open Campus Day : Jubiläumsfest 70 Jahre Fraunhofer auf dem Fraunhofer IZS-Gelände. 16.Juli 2019, Stuttgart, Stuttgart, 2019, p. 40Folien.
    73. M. Huber, “Von den Daten zum Geschäftsmodell,” in KI & Plattformökonomie: Potenzial erkennen und nutzen : 17. Oktober 2019, Ludwigsburg, Stuttgart, 2019, p. 29Folien.
    74. M. Huber, “Künstliche Intelligenz : Einführung und industrielle Nutzung,” in 8. Technologieforum Fahrerlose Transportsysteme und mobile Roboter des Fraunhofer IPA : Chancen, Technologien, Wirtschaftlichkeit. 18. September 2019, Leinfelden-Echterdingen, Stuttgart, 2019, p. 30Folien.
    75. M. Huber, “Chancen und Grenzen der Künstlichen Intelligenz,” in Exkursion zum Future Work Lab des Fraunhofer IPA : Besuch der Exkursionsgruppe des Jahreskongress Berufliche Bildung jakobb. 06. Dezember 2019, Stuttgart, Stuttgart, 2019, p. 36Folien.
    76. M. Huber, “Open the Black Box : Erklärbarkeit maschineller Lernverfahren,” in Kundenveranstaltung der Frankfurter Inkasso GmbH : 24. Oktober 2019, Frankfurt am Main, Frankfurt am Main, 2019, p. 37Folien.
    77. M. Huber, “Deep Learning und Reinforcement Learning in der Automation,” in Forum Mensch Roboter 2019 : Stuttgart, 23.-24. Oktober 2019, Stuttgart, 2019, p. 43Folien.
    78. M. Huber, “Cyber Cognitive Intelligence : Von der Forschung in die Anwendung,” in AI in Production : Internationale Fachkonferenz für KI-Lösungen in der Produktion. 24.-25. September 2019, Hannover, München, 2019, p. 43Folien.
    79. M. Huber, “Artificial Intelligence / Machine Learning in Production,” in DÜRR Senior Management Group Meeting : 14. März 2019, Bietigheim-Bissingen, Bietigheim-Bissingen, 2019, p. 38Folien.
    80. T. Bauernhansl, “Digitale und biologische Transformation der Industrie,” in SensoPart Innovation Day 2019 : 27. Juni 2019, Gottenheim, Wieden, 2019, p. 40Folien.
    81. T. Bauernhansl, “Machine Learning mit CPPS : Use Cases und Potentiale,” in 27. Fabrik des Jahres : 20.-22. März 2019, Ludwigsburg, München, 2019, p. 40Folien.
    82. T. Bauernhansl, D. Görzig, G. Hoßfeld, and J. Siegert, “Industrie 4.0-Testumgebungen in Deutschland : Fördermaßnahme des BMBF für die Digitalisierung von KMU - eine Bestandsaufnahme,” VDI-Z : Integrierte Produktion, vol. 161, Art. no. 7–8, 2019.
    83. S. Kölle, C. Mock, K. Schmid, and C. B. d. Santos, “Von der Industrie 4.0 zu Galvanik 4.1 - Elektrolytführung neu gedacht,” WOMag : Kompetenz in Werkstoff und funktioneller Oberfläche, vol. 7, Art. no. 4, 2019.
    84. M. Schleef and M. Huber, “Artificial Intelligence and Machine Learning in Production,” in Productronica 2019 : Accelerating Innovation. Weltleitmesse für Entwicklung und Fertigung von Elektronik. 12.-15. November 2019, München, München, 2019, p. 35Folien.
    85. T. Bauernhansl, “Cognitive Production Systems : Technologies and Solutions for Body in White Production,” in Materialien des Karosseriebaus 2019 : Automotive-Circle-Fachkonferenz, 14.-15. Mai 2019, Bad Nauheim, Hannover, 2019, p. 32Folien.

  9. 2018

    1. H.-H. Wiendahl and A. Kluth, “Vielfältige Planungs- und Steuerungswerkzeuge : SCM-, MES- und APS-Lösungen,” IT-Matchmaker.guide, Art. no. Industrie4.0-Lösungen, 2018.
    2. T. Bauernhansl, “Maschinenbau zwischen Digitalisierung und Protektionismus : Wie kann die Industriepolitik gegensteuern?,” in Transformationskongress der IG Metall 2018 : Miteinander für Morgen. 29. und 30. Oktober 2018, Bonn, Frankfurt am Main, 2018, p. 40Folien.
    3. T. Bauernhansl, “Digital Transformation : Perspectives and Changes,” in Bühler Motor Führungskräftetreffen : 05.Juli 2018, Bamberg, 2018, p. 51Folien.
    4. G. Reinhart and T. Bauernhansl, “Von der Digitalen zur Biologischen Transformation,” wt Werkstattstechnik online, p. 107, 2018.
    5. T. Bauernhansl, “Digital Transformation : Status and Future Perspectives,” in Operations and Technology Conference 2018 : 30. Oktober 2018, Mainz, Mainz, 2018, p. 33Folien.
    6. S. Kölle and A. Leiden, “Neue Ansätze zur Prozessüberwachung und -steuerung durch Verknüpfung von Material-/Energieflussdaten mit chemischen Analyseverfahren in der \textquotedblGalvanik 4.0\textquotedbl : Teil 1: Galvanotechnische Betrachtung,” in eiffo:tag : Innovation und Effizienz - Industrie 4.0 und Energietechnik in der Praxis der Oberflächentechnik. 25. Oktober 2018, Karlsruhe, Ostfildern, 2018, p. 10Folien.
    7. L. Boonen, P. Kitzler, C. Glanz, and I. Kolaric, “Digitalization in Electrode Manufacturing : Towards More Efficiency in Joined Research and Small Lot Size Production,” in NanoCarbon : Annual Conference, February 27th and 28th, Würzburg, Würzburg, 2018, p. 24 Folien.
    8. M. Huber, “Recommendation Engines,” in Smart Systems - Basics of AI : Seminar, 13. November 2018, Stuttgart, Stuttgart, 2018, p. 26Folien.
    9. M. Huber, “AI Technologies,” in Smart Systems - Basics of AI : Seminar, 13. November 2018, Stuttgart, Stuttgart, 2018, p. 58Folien.
    10. M. Huber, “Kognitive Produktionssysteme : Maschinelles Lernen im industriellen Einsatz,” in Roboter in der Automobilindustrie : 4. Fachkonferenz, 14. und 15. November 2018, Dresden, Landsberg am Lech, 2018, p. 28Folien.
    11. S. Schumacher, “Future Work Lab : Innovation Lab for Work, People and Technology,” in Capturing Value from Digitalization of Logistics : Learn from Innovation Leaders how to Apply Best Practises of Logistics 4.0 in your Business. Seminar SPA 437, 10. April 2018, Stuttgart, Stuttgart, 2018, p. 16Folien.
    12. P. Schwanzer, K. Schmid, and M. Metzner, “Selektive Verchromung von rotationssymmetrischen Bauteilen mit automatisierter Brushtechnik,” WOMag : Kompetenz in Werkstoff und funktioneller Oberfläche, vol. 6, Art. no. 3, 2018.
    13. T. Bauernhansl, “Automobilindustrie 4.0 - personalisiert und smart,” in 4. Automotive Photonics : 7. und 8. Februar 2018, Ditzingen, Ditzingen, 2018, p. 38Folien.
    14. R. Neuhaus, T. Bauernhansl, I. Kolaric, and C. Glanz, “Smarte Materialien und Oberflächen : Raumkonditionierung der Zukunft,” in Leichtbau im urbanen System : Architektur, Engineering, Forschung. Symposium, 18. Juli 2018, Stuttgart, Stuttgart, 2018, p. 43Folien.
    15. T. Bauernhansl, “Hochleistungsnetzwerke,” in 3. Spitzentreffen \textquotedblIndustrie 4.0 Live\textquotedbl : 22. November 2018, Stuttgart, Stuttgart, 2018, p. 9Folien.
    16. M. S. Dillmann, “Automation assessment for intralogistics : A Fast Selection Fethod for Finding the First-choice Automation Projects,” in Capturing Value from Digitalization of Logistics : Learn from innovation leaders how to apply best practices of Logistics 4.0 in your business. Seminar, 24.-25. Januar, 2018, Stuttgart, Stuttgart, 2018, p. 29Folien.
    17. C. Dierolf, “Smart Service durch maschinelles Lernen : Wie aus Energiedaten Wissen entsteht,” in Lernreise Industrie 4.0 live : Ansätze für ein Produktionssystem der Zukunft. Forschung für die Industrie 4.0. Abschlussveranstaltung. 27. und 28. Juni 2018, Stuttgart, 2018, p. 6Folien.
    18. M. Huber, “Reinforcement Learning,” in Application of Cyber Physical Systems (CPS) in Production : Schulungsprogramm, 22-31. Oktober 2018, Stuttgart, Stuttgart, 2018, p. 37 Folien.
    19. T. Bauernhansl, “The next Generation of Technologies for Industrie 4.0,” in La prossima generazione di tecnologie per le strategie 4.0 : 31 gennaio 2018, Bologna, Bologna, 2018, p. 90 Folien.
    20. Y. Boonyongmaneerat, M. Metzner, and K. Schmid, “TEPNET: An Industry Cluster Initiative for Advancement of Electroplating Activities in Thailand,” WOMag : Kompetenz in Werkstoff und funktioneller Oberfläche, Art. no. 5, 2018.
    21. T. Bauernhansl, “Digitale Transformation der Produktion : Von der Plattform zum Betriebssystem,” in Secure Exchange Fachtagung : Wie Mittelstand und IoT SICHER zueinander finden. 11. September 2018, Offenbach am Main, Frankfurt am Main, 2018, p. 42Folien.
    22. U. Schneider, O. Röhrle, E. Ramasamy, and J. Eckstein, “Forschungspotentiale für aktive prothetische und orthetische Systeme für die obere Extremität,” in 49. Jahrestagung der Deutschen Gesellschaft der Plastischen, Rekonstruktiven und Ästhetischen Chirurgen (DGPRÄC) : 50 Jahre Plastische Chirurgie - Tradition und Moderne. 13.-15. September 2018, Bochum, Berlin, 2018, p. 25Folien.
    23. T. Bauernhansl, “Industrie 4.0 - Wie die digitale Transformation die Wandlungsfähigkeit der Produktion verbessert,” in IDEEN-FORUM+ : Impulse für Technik, Wirtschaft, Wissenschaft. 26. April 2018, Besigheim, 2018, p. 31Folien.
    24. T. Bauernhansl, “Industrie 4.0 - Es muss nicht immer teuer sein!,” in Sinfosy Jahresveranstaltung 2018 : 13.-14. September 2018, Wildau, Wildau, 2018, p. 39Folien.
    25. T. Bauernhansl, “Arbeitswelt der Zukunft : Technik treibt den Fortschritt - der Mensch macht den Unterschied,” in Erfolgsfaktoren für eine wirksame Führung im Zeitalter der Digitalisierung : ARENA 2036, 8. März 2018, Stuttgart, Stuttgart, 2018, p. 39 Folien.
    26. T. Bauernhansl, “Cyber-physische Architekturen - Paradigmenwechsel auf allen Ebenen?!,” in VDE Tec Summit 2018 : 13.-14. November 2018, Berlin, Frankfurt am Main, 2018, p. 25 Folien.
    27. M. Huber, “AI Basics,” in Application of Cyber Physical Systems (CPS) in Production : Schulungsprogramm, 22-31. Oktober 2018, Stuttgart, Stuttgart, 2018, p. 52Folien.
    28. T. Bauernhansl, “Neue Geschäftsmodelle - Industrie 4.0 : Aktueller Stand und Ausblick,” in Lernreise Industrie 4.0 live : Auftaktveranstaltung. 18. und 19. Oktober 2018, Stuttgart, Stuttgart, 2018, p. 37Folien.
    29. M. Tzempetonidou, “Mit Fördermaßnahmen zur Industrie-4.0-Anwendung,” MM MaschinenMarkt : Das IndustrieMagazin, vol. 124, Art. no. 21, 2018.
    30. H.-H. Wiendahl, A. Kluth, and R. Kipp, “MES-Auswahl - sicher und nachvollziehbar,” Plastverarbeiter : Neue Technologien, Kosteneffizienz, Erhöhte Marktchancen, vol. 69, Art. no. 2, 2018.
    31. U. Schneider, F. Blab, E. Ramasamy, B. Dorow, O. Avci, and O. Röhrle, “Personalisierung und 3D-Druck in der Orthopädietechnik,” in Auditorenfortbildung der mdc : Fortbildungskongress, 19.09.2018, Stuttgart, Berlin / Stuttgart / Wien, 2018, p. 58Folien.
    32. C. Glanz, M. Entenmann, R. Neuhaus, L. Boonen, and I. Kolaric, “Smart Surfaces with Nanocarbon Materials,” in Future World with Nano-Carbon Materials : Seminar SPA, 05. Juni 2018, Stuttgart, Stuttgart, 2018, p. 32Folien.
    33. T. Bauernhansl, “Biologische Transformation : Die nachhaltige Revolution der Industrie,” in AmCham Germany Business After Hours : 18. September 2018, Stuttgart, Frankfurt / Berlin, 2018, p. 39Folien.
    34. M. Huber, “Robotics and Autonomous Systems,” in Smart Systems - Basics of AI : Seminar, 13. November 2018, Stuttgart, Stuttgart, 2018, p. 55Folien.
    35. M. Huber, “Artificial Intelligence in the Life Sciences,” in Roche Technology Innovation Bootcamp : 06.November 2018, Basel, Schweiz, 2018, p. 33Folien.
    36. P. Kübler, “Warehousing and Order Picking 4.0,” in Capturing Value from Digitalization of Logistics : Learn from innovation leaders how to apply best practices of Logistics 4.0 in your business. Seminar, 24.-25. Januar, 2018, Stuttgart, Stuttgart, 2018, p. 62Folien.
    37. T. Bauernhansl, “Personalisierung von (Luxus-)Produkten und Anforderungen an die Infrastruktur,” in Disruptive Technologien und die Luxusgüterindustrie : 19. Oktober 2018, Wimsheim, Wimsheim, 2018, p. 107Folien.
    38. T. Bauernhansl, “Wertschöpfung der Zukunft : Chancen & Risiken,” in NEXCON : Der erste voll-digitale Kongress zum Thema Sm@rt Factory im deutschsprachigen Raum. 02. März 2018, online only, Stuttgart, 2018, p. 37 Folien.
    39. M. S. Dillmann and P. Kübler, “Automation Assessment for Intralogistics : A Fast Felection Method for Finding the First-choice Automation Projects,” in Capturing Value from Digitalization of Logistics : Learn from Innovation Leaders how to Apply Best Practises of Logistics 4.0 in your Business. Seminar SPA 437, 10. April 2018, Stuttgart, Stuttgart, 2018, p. 29 Folien.
    40. T. Bauernhansl, “Future Enabling Technologies and Digital Transformation,” in INDEX TRAUB Open House 2018 : 24.-27. April 2018, Reichenbach, 2018.
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