Abstract
The integration of complex and innovative technologies into manufacturing processes poses various challenges. This paper comprehensively outlines the challenges that can occur when integrating and using machine learning (ML)-based methods in a production environment to assist in steering the process. The identified problems are considered regarding different categories of problems in process modelling. In addition, potential innovative technologies and methods are shown that can help to mitigate the challenges. Various applications and projects dealing with increasing energy efficiency through ML served as use cases to identify the challenges presented and possible suggestions to mitigate those same challenges.
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Acknowledgement
The authors would like to thank their project partners and the anonymous reviewers for their valuable input. The “Tide2Use" project (19H18004) is funded by the German Federal Ministry of Transport and Digital Infrastructure (BMDV) in the “Innovative Port Technologies" (IHATEC) program. The authors would also like to thank the Federal Ministry for Economic Affairs and Climate Action (BMWK) and the Project Management Juelich (PtJ) for funding the project “Increasing energy efficiency in production through digitization and AI" - ecoKI (03EN2047A).
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Schindler, T.F., Bode, D., Thoben, KD. (2023). Towards Challenges and Proposals for Integrating and Using Machine Learning Methods in Production Environments. In: Valle, M., et al. Advances in System-Integrated Intelligence. SYSINT 2022. Lecture Notes in Networks and Systems, vol 546. Springer, Cham. https://doi.org/10.1007/978-3-031-16281-7_1
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