Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing

Patrick Link (), Miltiadis Poursanidis, Jochen Schmid, Rebekka Zache, Martin Kurnatowski, Uwe Teicher and Steffen Ihlenfeldt
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Patrick Link: Fraunhofer Institute for Machine Tools and Forming Technology IWU
Miltiadis Poursanidis: Fraunhofer Institute for Industrial Mathematics ITWM
Jochen Schmid: Fraunhofer Institute for Industrial Mathematics ITWM
Rebekka Zache: Fraunhofer Institute for Machine Tools and Forming Technology IWU
Martin Kurnatowski: Fraunhofer Institute for Industrial Mathematics ITWM
Uwe Teicher: Fraunhofer Institute for Machine Tools and Forming Technology IWU
Steffen Ihlenfeldt: Fraunhofer Institute for Machine Tools and Forming Technology IWU

Journal of Intelligent Manufacturing, 2022, vol. 33, issue 7, No 14, 2129-2142

Abstract: Abstract Increasing digitalization enables the use of machine learning (ML) methods for analyzing and optimizing manufacturing processes. A main application of ML is the construction of quality prediction models, which can be used, among other things, for documentation purposes, as assistance systems for process operators, or for adaptive process control. The quality of such ML models typically strongly depends on the amount and the quality of data used for training. In manufacturing, the size of available datasets before start of production (SOP) is often limited. In contrast to data, expert knowledge commonly is available in manufacturing. Therefore, this study introduces a general methodology for building quality prediction models with ML methods on small datasets by integrating shape expert knowledge, that is, prior knowledge about the shape of the input–output relationship to be learned. The proposed methodology is applied to a brushing process with 125 data points for predicting the surface roughness as a function of five process variables. As opposed to conventional ML methods for small datasets, the proposed methodology produces prediction models that strictly comply with all the expert knowledge specified by the involved process specialists. In particular, the direct involvement of process experts in the training of the models leads to a very clear interpretation and, by extension, to a high acceptance of the models. While working out the shape knowledge requires some iterations in general, another clear merit of the proposed methodology is that, in contrast to most conventional ML, it involves no time-consuming and often heuristic hyperparameter tuning or model selection step.

Keywords: Informed machine learning; Small datasets; Expert knowledge; Shape constraints; Quality prediction; Surface finishing (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10845-022-01975-4

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