Publikationen des IFF

Hier finden Sie Publikationen, die am IFF entstanden sind.

Publikationsliste (peer-reviewed)

  1. 2025

    1. 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: 10.1007/978-3-031-77429-456.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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, vol. 9, Art. no. 1–2, 2025, doi: 10.1515/teme-2024-0090.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. V. Hofmann et al., “Reintegration into Work after Traumatic Brachial Plexus Injuries: A Selective Literature Review of Experiences from Various Global Regions,” World Neurosurgery, vol. 194, 2025, doi: 10.1016/j.wneu.2024.123632.
    13. 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: 10.1007/978-3-031-75623-835.
    14. 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: 10.1007/978-3-031-77588-841.
    15. 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.
    16. 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.
    17. 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.
    18. 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.

  2. 2024

    1. 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.
    2. 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: 10.1007/978-3-031-71167-14.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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: 10.1007/978-3-031-47394-44.
    15. 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.
    16. 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.
    17. 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.
    18. 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, p. 16, 2024, doi: 10.1007/s00170-024-14055-z.
    19. C. Rietdorf et al., “Leveraging Digital Twins for Real-Time Environmental Monitoring in Battery Manufacturing,” in 57th CIRP Conference on Manufacturing Systems 2024 : Speeding Up Manufacturing. 29.-31.05.2024, Póvoa de Varzim, Portugal, 2024, p. 6.
    20. 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.
    21. 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, p. 25, 2024, doi: 10.1007/s10845-023-02292-0.
    22. 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, 2024, doi: 10.1109/TRO.2024.3420799.
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. 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.
    32. 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.
    33. 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.
    34. 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: 10.1007/978-3-031-71645-49.
    35. 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. 29, Art. no. 11, 2024, doi: 10.1007/s11367-024-02355-1.
    36. 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.
    37. 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.
    38. 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, pp. 2811–2827, 2024, doi: 10.1007/s10845-023-02179-0.
    39. 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.
    40. M. Huber, “Mit KI im Kleinen beginnen, aber groß denken\textquotedbl : WSM im Gespräch,” WSM Nachrichten, Art. no. 3, 2024.
    41. 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.
    42. 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.
    43. 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.
    44. 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.
    45. 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.
    46. 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.
    47. S. Durnagöz and T. Eberhardt, “A Novel Approach for Unsupervised Sensor Error Classification,” Procedia CIRP, vol. 130, pp. 206–213, 2024, doi: 10.1016/j.procir.2024.10.077.
    48. 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.
    49. 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.
    50. 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.
    51. 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.
    52. 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.
    53. 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.
    54. 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.
    55. 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.
    56. 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.
    57. 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.
    58. 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.
    59. 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.
    60. 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.
    61. 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.
    62. 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.
    63. 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.
    64. 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.
    65. 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: 10.1007/978-3-031-44981-938.
    66. 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.
    67. 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.
    68. 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.
    69. 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.
    70. 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.
    71. 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.
    72. 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.
    73. 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.
    74. 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.
    75. 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.
    76. 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.
    77. 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.
    78. 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.
    79. A. Yaman, “Kabelbaummontage mit KI : Flexible Komponenten mithilfe kollaborativer Roboter handhaben,” VDI-Z, vol. 166, Art. no. 3, 2024.
    80. 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.
    81. 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.

  3. 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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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: 10.1007/978-3-031-43662-814.
    8. 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: 10.1007/978-3-031-44137-020.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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, p. 17, 2023, doi: 10.1007/s10845-023-02179-0.
    15. 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.
    16. 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: 10.1007/978-3-031-18326-322.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    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. 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.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. 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.
    32. 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: 10.1007/978-3-031-27933-115.
    33. 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.
    34. 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.
    35. 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.
    36. 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.
    37. 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: 10.1007/978-3-031-43670-357.
    38. 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: 10.1007/978-3-031-18326-321.
    39. 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.
    40. 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.
    41. 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.
    42. 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.
    43. 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: 10.1007/978-3-031-26490-016.
    44. 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.
    45. 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, 2023, pp. 1547–1552. doi: 10.1109/IEEM58616.2023.10406540.
    46. 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.
    47. 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.
    48. 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: 10.1007/978-3-031-43670-347.
    49. 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.
    50. 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.
    51. 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.
    52. 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.
    53. 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.
    54. 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.
    55. 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.
    56. 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: 10.1007/978-3-031-27933-114.
    57. 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.
    58. 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.
    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. 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: 10.1007/978-3-031-43188-36.
    61. 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.
    62. 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.

  4. 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. 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.
    3. 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: 10.1007/978-3-030-90700-670.
    4. 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.
    5. 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.
    6. 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.
    7. 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: 10.37544/1436-4980-2022-7-8-57.
    8. 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: 10.1007/978-3-030-95892-345.
    9. 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.
    10. 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.
    11. A. Lämmle, Z. Xiang, and B. A. Balint, “Extension of Established Modern Physics Simulation for the Training of Robotic Electrical Cabinet Assembly,” Procedia CIRP, vol. 107, pp. 1317–1322, 2022, doi: 10.1016/j.procir.2022.05.151.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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. 1175–1182. doi: 10.1109/ICMLA55696.2022.00190.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. M. Schneider, F. Haag, A. K. Khalil, and D. A. Breunig, “Evaluation of Communication Technologies for Distributed Industrial Control Systems: Concept and Evaluation of 5G and WiFi 6,” Procedia CIRP, vol. 107, pp. 588–593, 2022, doi: 10.1016/j.procir.2022.05.030.
    25. 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: 10.1007/978-3-030-69547-774.
    26. 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.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. 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.
    32. 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.
    33. 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.
    34. 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.
    35. 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.
    36. 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.
    37. 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.
    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. 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.
    40. 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.
    41. 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.
    42. 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.
    43. 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.
    44. 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.
    45. 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.
    46. 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.
    47. 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.
    48. 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.
    49. 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. 1123–1130. doi: 10.1109/IROS47612.2022.9981082.
    50. 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.

  5. 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. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. T. Bauernhansl, “End-to-End komplett neu gedacht : Editorial,” wt Werkstattstechnik online, vol. 111, Art. no. 3, 2021.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. 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.
    32. 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.
    33. 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.
    34. 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.
    35. 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.
    36. 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.
    37. 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.
    38. 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.
    39. 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.
    40. 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.
    41. 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.
    42. 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.
    43. 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.
    44. 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.
    45. 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.
    46. 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.
    47. 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.
    48. 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.
    49. 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.
    50. 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.
    51. 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.
    52. 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.
    53. 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.
    54. 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.

  6. 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. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. T. Bauernhansl, “Soviel KI war selten,” VDI-Z : Integrierte Produktion, vol. 162, Art. no. 4, 2020.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    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. 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.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. 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.
    32. 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.
    33. 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.
    34. M. Huber and U. Eberl, “Schwerpunkt Künstliche Intelligenz (Rezensionen),” Physik Journal, vol. 19, Art. no. 4, 2020.
    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. 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.
    37. 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.
    38. 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.
    39. 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.
    40. 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.
    41. 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.
    42. 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.
    43. 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.
    44. 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.
    45. M. Huber, “Lernen aus der Black Box : Cognitive Deep Learning soll neuronale Netze und Wissensverarbeitung kombinieren,” Physik Journal, vol. 19, Art. no. 4, 2020.
    46. 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.

  7. 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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    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. 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.
    12. 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.
    13. 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.
    14. 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.
    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. 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.
    22. 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.
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. 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.
    32. M. Huber, “Fallstudie: Predictive Maintenance,” in Data Science : Grundlagen, Architekturen und Anwendungen, Heidelberg: dpunkt.verlag, 2019, pp. 225–244.
    33. 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.
    34. 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.
    35. 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.
    36. 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.
    37. 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.

  8. 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. 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.
    5. 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.
    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. 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.
    9. 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.
    10. 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.
    11. 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.
    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. 2025

    1. 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.

  2. 2024

    1. 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.
    2. F. Rapp, D. Kreplin, and M. Roth, “Reinforcement learning-based architecture search for quantum machine learning,” arXiv, 2024, doi: 10.48550/arXiv.2406.02717.
    3. T. Bauernhansl, “Studie zur Biointelligenz: Deutschland auf Platz zwei : InBenBio,” Frankfurter Allgemeine Zeitung, Art. no. Online erschienen am 15.05.2024, 2024.
    4. H.-H. Wiendahl, “Stolpersteine der PPS,” VDI Wissensforum, 2024.
    5. M. Huber, “ChatGPT: So verändert generative KI den beruflichen Alltag : Sprach-KI betrifft vor allem gut bezahlte Jobs,” Automationspraxis, 2024.
    6. M. Hagelüken, M. Huber, and M. Roth, “Data Efficient Prediction of excited-state properties using Quantum Neural Networks,” arXiv, Art. no. Preprint, 2024, doi: 10.48550/arXiv.2412.09423.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. T. Nagel and M. Huber, “Identifying Ordinary Differential Equations for Data-efficient Model-based Reinforcement Learning,” arXiv, Art. no. TPreprint, 2024, doi: 10.48550/arXiv.2406.19817.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. M. Huber, “Kognitive Produktionssysteme: Maschinelles Lernen im industriellen Einsatz,” in GSaME Jahrestagung 2024: Intelligente Produktion : 14.10.2024, Stuttgart, Stuttgart, 2024, p. 27 Folien.
    20. P. Takenaka, J. Maucher, and M. Huber, “ViPro: Enabling and Controlling Video Prediction for Complex Dynamical Scenarios using Procedural Knowledge,” arXiv, Art. no. TPreprint, 2024, doi: 10.48550/arXiv.2407.09537.
    21. M. Huber, “Herausforderungen beim Einsatz generativer KI in der Wirtschaft,” in Bitkom AI Research Network : 15.11.2024, online, Berlin, 2024, p. 50 Folien.
    22. 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.

  3. 2023

    1. T. Bauernhansl, “Die Produktion von Übermorgen,” in Lernreise \textquotedblProduktion der Zukunft\textquotedbl : 13.-14.09.2023, Karlsruhe, München u.a., 2023, p. 39Folien.
    2. 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.
    3. 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.
    4. 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.
    5. T. Bauernhansl, “Nachhaltige Wertschöpfungssysteme - Aktuelle und zukünftige Konzepte,” in Fabrik des Jahres 2022 : Kongress. 22.-23.03.2023, München, München, 2023, p. 10 Folien.
    6. M. Huber, I. Effenberger, H. Eigenbrod, A. Frommknecht, C. Jauch, and J. Denecke, “Qualitätssicherung in der Produktion,” in Handbuch Industrie 4.0 : Band 1: Produktion, Berlin and Heidelberg: Springer Vieweg, 2023, pp. 53–69. doi: 10.1007/978-3-662-58532-0166.
    7. T. Bauernhansl, “Biologische ergänzt Digitale Transformation : Standpunkt,” wt Werkstattstechnik online, vol. 113, Art. no. 3, 2023.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. M. Huber, “Kognitive Produktionssysteme: KI in der Produktion,” in Staufen C-Day 2023 : 26.-27.09.2023, Bad Teinach, 2023, p. 28 Folien.
    15. T. Bauernhansl and R. Miehe, “Was ist Biointelligenz?,” Frankfurter Allgemeine Zeitung, Art. no. Online erschienen am 30.10.2023, 2023.
    16. 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: 10.1007/978-3-662-58532-0167.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. T. Bauernhansl, “Aktuelle Forschungsthemen in den Produktionswissenschaften,” in GSaME Grundprogramm : 20.-30.11.2023, Stuttgart, Stuttgart, 2023, p. 71 Folien.
    24. T. Bauernhansl, “Biointelligenz bringt Umwelt und Wohlstand in Einklang,” atp magazin, Art. no. 4, 2023.
    25. 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.
    26. T. Bauernhansl, “Maschinenhersteller werden zu Dienstleistern,” Frankfurter Allgemeine Zeitung, Art. no. Online erschienen am 12.12.2023, 2023.
    27. M. Huber, “Machine Vision and Dependable AI,” in Industrial-Grade AI Fokustag : 02.02.2023, Stuttgart, Stuttgart, 2023, p. 13 Folien.
    28. M. Huber, “Einsatz von KI in der Produktion,” in Trumpf Data & AI Conference : 18.07.2023, Ditzingen, 2023, p. 26 Folien.
    29. 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.
    30. T. Bauernhansl, “Mit Matrixproduktionssystemen flexibel und produktiv werden,” Frankfurter Allgemeine Zeitung, Art. no. Online erschienen am 02.10.2023, 2023.
    31. 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.

  4. 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. O. Schwarz, “Biomimetics: Learning from Nature for the Bioeconomy,” in 4. Bioökonomiekongress Baden-Württemberg : 26.-28.09.2022, Stuttgart, Stuttgart, 2022, p. 1Poster.
    7. 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.
    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. 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.
    15. 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.
    16. M. Huber, “Roboter lernen zu Montieren : Rob-aKademI,” Magazin des Stuttgarter Maschinenbaus, Art. no. 2, 2022.
    17. M. Huber, “Dependable AI in Mobility and Production,” in AI Quality Summit : 02.11.2022, Frankfurt, Offenbach am Main, 2022, p. 15Folien.
    18. T. Bauernhansl and R. Miehe, “Mit Biointelligenz in einen nachhaltigen Innovationsraum,” Magazin des Stuttgarter Maschinenbaus, Art. no. 2, 2022.
    19. 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.
    20. 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.
    21. M. Huber, “Erklärbare KI,” in Treffen der KI Community - Thema Erklärbare KI : 02.06.2022, Teamsmeeting, Stuttgart, 2022, p. 12Folien.
    22. M. Huber, “Optimizing Processes and Making Them More Efficient with Al,” Metrology.news, Art. no. Online erschienen am 01.02.2022, 2022.
    23. 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.
    24. 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.
    25. 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.
    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.

  5. 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. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    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, “Produktionsdaten sicher und nachvollziehbar nutzen,” Digital Business Cloud : Das Expertenmagazin, Art. no. 1, 2021.
    22. M. Huber, “Zuverlässige KI - Absicherung künstlicher neuronaler Netze,” in Forum Künstliche Intelligenz : 21. April 2021, virtuelle Konferenz, Haar, 2021, p. 24Folien.
    23. 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.
    24. 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.
    25. B. Spaeth, T. Bauernhansl, and M. Heymann, “Biointelligenz,” Magazin des Stuttgarter Maschinenbaus, Art. no. 1, 2021.
    26. 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.
    27. 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.
    28. M. Vogel, M. Huber, and S. Oppold, “Blick in die Black Box,” Forschung Leben : Das Magazin der Universität Stuttgart, Art. no. 1, 2021.
    29. 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.
    30. M. Huber, “Immer noch unterschätzt : Gastbeitrag zum Thema Künstliche Intelligenz,” VDMA Magazin, Art. no. 8, 2021.
    31. 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.
    32. M. Huber, “Dependable AI - Machine Learning in Safety Critical Applications,” in Expertennetzwerk AI Circle : 12.05.2021. Online, Berlin, 2021, p. 26Folien.
    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, “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.
    35. B. Reckter, U. Schneider, and V. Kopp, “Muskelkraftverstärker,” VDI Nachrichten : Technik, Wirtschaft, Gesellschaft, Art. no. 42, 2021.
    36. 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.
    37. M. Huber, “Kognitive Produktionssysteme in der intelligenten Fabrik,” in Maschinenbautag : KI - Künstliche Intelligenz im Maschinenbau. 14.04.2021, Digital, Konstanz, 2021, p. 39Folien.
    38. 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.
    39. 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.
    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.

  6. 2020

    1. M. Huber, “KI in der Produktion und Robotik,” in Digitale Eröffnung von KI-noW : 7. Dezember 2020, online, Stuttgart, 2020, p. 13Folien.
    2. 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.
    3. 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.
    4. M. Huber, “Künstliche Intelligenz in der Produktion,” in Doktoranden-Kolloquium der Graduiertenschule GSaME der Universität Stuttgart : 17. Januar 2020, Stuttgart, Stuttgart, 2020, p. 44Folien.
    5. 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.
    6. 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.
    7. 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.
    8. C. Hennebold, “Künstliche Intelligenz : Einführung und technische Anforderungen,” in Fraunhofertage bei NetApp : 04. März 2020, Kirchheim, Kirchheim, 2020, p. 34Folien.
    9. 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.
    10. 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.
    11. 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.
    12. N. Schaaf, “Open the Black Box - Erklärbare Künstliche Intelligenz,” in Robotic & Automation Wednesday : Webinarreihe. Ab 03. Juni 2020, Stuttgart, 2020, p. 22Folien.
    13. 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.
    14. 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.
    15. 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.
    16. M. Huber, “Kognitive Produktionssysteme : Maschinelles Lernen im industriellen Einsatz,” in Forum Künstliche Intelligenz : 14. Mai 2020, virtuell, Haar, 2020, p. 37Folien.
    17. M. Huber, C. Jauch, and M. Teschner, “Bildverarbeitung für Robotik und Qualitätssicherung : Intelligent sehen,” JavaSPEKTRUM, Art. no. 5, 2020.
    18. 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.
    19. M. Huber, “Erklärbare Künstliche Intelligenz : Eine Einführung,” in Versammlung der Jusos : 25.05.2020, virtuell, Stuttgart, 2020, p. 46Folien.
    20. M. Huber, “KI in der Fertigung und Qualitätssicherung der Zukunft,” in Das intelligente KMU : 24. September 2020. Virtuelle Konferenz, Berlin, 2020, p. 31Folien.
    21. 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.
    22. 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.
    23. G. Reinhart and T. Bauernhansl, “Industrie 4.0 - und was nun? : Editorial,” wt Werkstattstechnik online, vol. 110, Art. no. 3, 2020.
    24. 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.
    25. M. Huber, “Künstliche Intelligenz treibt Innovationen,” Ideen- und Innovationsmanagement, Art. no. 4, 2020.
    26. R. Butscher and M. Huber, “Interview zur Künstlichen Intelligenz: ,,Schluss mit der Black Box!,” Bild der Wissenschaft, Art. no. 6, 2020.
    27. M. Huber, “Maschinelles Lernen besser verstehen : Mehr Transparenz für die Arbeitsweise neuronaler Netze,” AI Spektrum : Das Magazin für Künstliche Intelligenz, pp. 25–27, 2020.
    28. T. Bauernhansl, “Biointelligence - a new Innovation Space for Sustainable Industries,” in Tomorrow : The McKinsey Berlin Conference. November 13, 2020. Remotely, Berlin, 2020, p. 14Folien.
    29. 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.
    30. J. Witte and R. Neuhaus, “Gebäudehaut, die atmet,” Forschung Leben : Das Magazin der Universität Stuttgart, Art. no. 1, 2020.
    31. M. Huber, “Artificial Intelligence,” in Technology Innovation Bootcamp : 30. März, 2020, p. 41Folien.
    32. 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.
    33. M. Huber, “KI in der Produktion,” in FabOS Kick-Off-Event : 11. Februar 2020, Stuttgart, Stuttgart, 2020, p. 40Folien.
    34. 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.
    35. 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.
    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.

  7. 2019

    1. M. Huber, “xAI: Nachvollziehbarkeit maschineller Lernverfahren am Beispiel neuronaler Netze,” in Bitkom AI Research Network : E-Lecture, 19. Juli 2019, Berlin, 2019, p. 42Folien.
    2. M. Huber, “xAI: Erklärbarkeit von KI,” in Fokus: Zukunft : Unser Leben 2050. 28. November 2019, Karlsruhe, Karlsruhe, 2019, p. 10Folien.
    3. 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.
    4. T. Bauernhansl and G. Reinhart, “Innovationssprünge durch Industrie 4.0 ermöglichen eine nachhaltige Produktion : Editorial,” wt Werkstattstechnik online, vol. 109, Art. no. 3, 2019.
    5. 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.
    6. C. Fries et al., “Fluide Fahrzeugproduktion : BMBF-geförderter Forschungscampus für die Mobilität der Zukunft,” VDI-Z : Integrierte Produktion, vol. 161, Art. no. 12, 2019.
    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, “Biologische Transformation - Die nachhaltige Revolution der Wertschöpfung?!,” in Herbstsitzung des Wirtschaftsbeirats der Stadt Ingolstadt : 08. Oktober 2019, Ingolstadt, Ingolstadt, 2019, p. 30Folien.
    9. R. Neuhaus, “Electroactive CNT-Polymer-Actuators : Electroactive CNT-Polymer-Actuators. Pitch,” in NanoCarbon : Annual Conference, February 26 and 27, Würzburg, Würzburg, 2019, p. 26Folien.
    10. 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.
    11. 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.
    12. M. Huber, “Artificial Intelligence : Introduction and Industrial Application,” in 10. Future Lab der Boehringer Ingelheim GmbH : 06. November 2019, Biberach, Biberach, 2019, p. 35Folien.
    13. 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.
    14. 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.
    15. A. Mayer-Grenu and M. Huber, “Selbstoptimierung in der Fabrikhalle,” Forschung Leben : Das Magazin der Universität Stuttgart, Art. no. 12, 2019.
    16. M. Huber, “Artificial Intelligence / Machine Learning Production,” in Entwicklertag der Bosch Packaging Technology GmbH : 26. Februar 2019, Waiblingen, Waiblingen, 2019, p. 37Folien.
    17. 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.
    18. M. Huber, “Explainable AI - Introduction and Application to Neural Networks,” in AI-Monday des Cyber Valley : 18. November 2019, Tübingen, Tübingen, 2019, p. 31Folien.
    19. 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.
    20. K. Kleeberger, M. Huber, and A. Wolf, “Mit Simulationen schneller zur Anwendung,” Fabriksoftware : Die digitale Fabrik realisieren, Art. no. 2, 2019.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. 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.
    28. 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.
    29. 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.
    30. M. Huber, “Machine Learning für Produktion und Robotik,” Automationspraxis, vol. 2019, Art. no. 11, 2019.
    31. 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.
    32. 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.
    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. 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.
    35. 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.
    36. M. Huber, “Zentrum für Cyber Cognitive Intelligence,” in Arena 2036 for You : 17. April 2019, Stuttgart, Stuttgart, 2019, p. 19Folien.
    37. 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.
    38. 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.
    39. T. Bauernhansl, “Die digitale Transformation - Herausforderungen für Baden-Württemberg,” in Transformationskonferenz der IG Metall Baden-Württemberg : 21. Februar 2019, Ludwigsburg, Stuttgart, 2019, p. 42Folien.
    40. 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.
    41. 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.
    42. M. Huber, Ed., “Data are the key,” in Automatica 2020 : The Leading Exhibition for Smart Automation and Robotics, München, 2019, p. 2.
    43. M. Huber, “Cognitive Production Systems,” in Knorr-Bremse Digital Days : 07. November 2019, München, 2019, p. 34Folien.
    44. 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.
    45. 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.
    46. 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.
    47. M. Huber, “Spielerisch besser,” in A&D Kompendium 2019/2020 : Die Macher der Automation, München: Publish-Industry Verlag, 2019, pp. 214–215.
    48. 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.
    49. 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.
    50. M. Huber, “Simulation-driven Reinforcement Learning in Robotics,” in European Robotics Forum : 20-22 March 2019, Bucharest, Romania, Brüssel, 2019, p. 11Folien.
    51. E. Westkämper, “Industrie 4.0 - Vernetzung informationsverarbeitender Prozesse : Editorial,” wt Werkstattstechnik online, vol. 109, Art. no. 6, 2019.
    52. 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.
    53. 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.
    54. K. Pfeiffer and M. Huber, “Mobile Robotik, KI und die Cloud : Eine gemeinsame Betrachtung,” Automationspraxis, Art. no. 8, 2019.
    55. M. Huber, “Künstliche Intelligenz in der Produktion der Zukunft,” in Nokia Innovation Days : 26. und 27. November 2019, Stuttgart, Stuttgart, 2019, p. 33Folien.
    56. 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.
    57. 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.
    58. W. Kraus, B. Winkler, and M. Huber, “Maschinelle Lernverfahren für Roboteranwendungen,” atp magazin : Transforming Automation, Art. no. 1–2, 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. T. Bauernhansl, “Digitalisierung trifft auf Biologisierung : Biologische Transformation der industriellen Wertschöpfung,” Automationspraxis, vol. 14, Art. no. 4, 2019.
    61. 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.
    62. 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.
    63. M. Huber, “Kognitive Produktionssysteme : KI im industriellen Einsatz,” in Vorstandssitzung des VDMA Baden-Württemberg : 16. Juli 2019, Baden-Baden, Stuttgart, 2019, p. 36Folien.
    64. 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.
    65. 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.
    66. 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.
    67. T. Bauernhansl, “Biologische Transformation : Die nachhaltige Revolution der Industrie,” in YPO Gold : 06. April 2019, Stuttgart, Stuttgart, 2019, p. 41Folien.
    68. M. Huber, “Reinforcement Learning,” in Kognitive Produktionssysteme : Künstliche Intelligenz im industriellen Einsatz. 25. und 26. November 2019, Stuttgart, Stuttgart, 2019, p. 36Folien.
    69. T. Bauernhansl, “Mechanical Engineering in Technology-Driven Societies,” in IndustriALL Global Union : Weltkonferenz Maschinenbau. 11.-13. September 2019, Stuttgart, Genf, 2019, p. 47Folien.
    70. 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.
    71. M. Huber, B. Spaeth, and D. Stock, “Neues KI-Zentrum in Stuttgart,” WGP-Newsletter, p. 4, 2019.
    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. 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.
    78. 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.
    79. M. Huber, “Deep Learning und Reinforcement Learning in der Automation,” in Forum Mensch Roboter 2019 : Stuttgart, 23.-24. Oktober 2019, Stuttgart, 2019, p. 43Folien.
    80. 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.
    81. M. Huber, “Artificial Intelligence / Machine Learning in Production,” in DÜRR Senior Management Group Meeting : 14. März 2019, Bietigheim-Bissingen, Bietigheim-Bissingen, 2019, p. 38Folien.
    82. T. Bauernhansl, “Digitale und biologische Transformation der Industrie,” in SensoPart Innovation Day 2019 : 27. Juni 2019, Gottenheim, Wieden, 2019, p. 40Folien.
    83. T. Bauernhansl, “Machine Learning mit CPPS : Use Cases und Potentiale,” in 27. Fabrik des Jahres : 20.-22. März 2019, Ludwigsburg, München, 2019, p. 40Folien.
    84. 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.
    85. 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.

  8. 2018

    1. M. Huber, “Recommendation Engines,” in Smart Systems - Basics of AI : Seminar, 13. November 2018, Stuttgart, Stuttgart, 2018, p. 26Folien.
    2. M. Huber, “AI Technologies,” in Smart Systems - Basics of AI : Seminar, 13. November 2018, Stuttgart, Stuttgart, 2018, p. 58Folien.
    3. 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.
    4. H.-H. Wiendahl and A. Kluth, “Vielfältige Planungs- und Steuerungswerkzeuge : SCM-, MES- und APS-Lösungen,” IT-Matchmaker.guide, Art. no. Industrie4.0-Lösungen, 2018.
    5. 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.
    6. T. Bauernhansl, “Digital Transformation : Perspectives and Changes,” in Bühler Motor Führungskräftetreffen : 05.Juli 2018, Bamberg, 2018, p. 51Folien.
    7. G. Reinhart and T. Bauernhansl, “Von der Digitalen zur Biologischen Transformation,” wt Werkstattstechnik online, p. 107, 2018.
    8. T. Bauernhansl, “Digital Transformation : Status and Future Perspectives,” in Operations and Technology Conference 2018 : 30. Oktober 2018, Mainz, Mainz, 2018, p. 33Folien.
    9. 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.
    10. 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.
    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. M. Huber, “Reinforcement Learning,” in Application of Cyber Physical Systems (CPS) in Production : Schulungsprogramm, 22-31. Oktober 2018, Stuttgart, Stuttgart, 2018, p. 37 Folien.
    18. 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.
    19. 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.
    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, “Artificial Intelligence in the Life Sciences,” in Roche Technology Innovation Bootcamp : 06.November 2018, Basel, Schweiz, 2018, p. 33Folien.
    35. M. Huber, “Robotics and Autonomous Systems,” in Smart Systems - Basics of AI : Seminar, 13. November 2018, Stuttgart, Stuttgart, 2018, p. 55Folien.
    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.
Dieses Bild zeigt Konstantin Hoffmann

Konstantin Hoffmann

M. A.

Bibliothek

Zum Seitenanfang