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

TL;DR

This paper presents a methodology for building accurate machine learning quality prediction models in manufacturing by integrating expert knowledge, especially effective for small datasets, leading to interpretable and accepted models.

Contribution

The study introduces a novel approach that incorporates shape expert knowledge into machine learning models for small datasets, ensuring model compliance and interpretability.

Findings

Models strictly adhere to expert knowledge

Method reduces need for hyperparameter tuning

Applicable to small datasets in manufacturing

Abstract

Increasing digitalization enables the use of machine learning methods for analyzing and optimizing manufacturing processes. A main application of machine learning 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 machine learning 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 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 machine learning 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 machine learning 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. Another merit of the proposed methodology is that, in contrast to most conventional machine learning methods, it involves no time-consuming and often heuristic hyperparameter tuning or model selection step.