Abstract
Early detection of errors in production processes is of crucial importance for manufacturing companies. With the advent of machine learning (ML) methods, the interaction of ML and human expertise offers the opportunity to develop a targeted understanding of error causes and thus to proactively avoid errors. The power of such a model can only be used if relevant domain knowledge is taken into account and applied correctly. When using ML methods, a systematic failure analysis without the need of deeper ML knowledge is crucial for an efficient quality management. Focusing on these two aspects, we develop an updated holistic solution by expanding and detailing our previously proposed approach to support production quality.
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Seiffer, C., Gerling, A., Schreier, U., Ziekow, H. (2021). A Reference Process and Domain Model for Machine Learning Based Production Fault Analysis. In: Filipe, J., Śmiałek, M., Brodsky, A., Hammoudi, S. (eds) Enterprise Information Systems. ICEIS 2020. Lecture Notes in Business Information Processing, vol 417. Springer, Cham. https://doi.org/10.1007/978-3-030-75418-1_8
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