Enhancing Manufacturing Quality Prediction Models through the Integration of Explainability Methods

Dennis Gross; Helge Spieker; Arnaud Gotlieb; Ricardo Knoblauch

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arXiv · 2403.18731

Enhancing Manufacturing Quality Prediction Models through the Integration of Explainability Methods

Dennis Gross·Helge Spieker·Arnaud Gotlieb·Ricardo Knoblauch

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Abstract

This research presents a method that utilizes explainability techniques to amplify the performance of machine learning (ML) models in forecasting the quality of milling processes, as demonstrated in this paper through a manufacturing use case. The methodology entails the initial training of ML models, followed by a fine-tuning phase where irrelevant features identified through explainability methods are eliminated. This procedural refinement results in performance enhancements, paving the way for potential reductions in manufacturing costs and a better understanding of the trained ML models. This study highlights the usefulness of explainability techniques in both explaining and optimizing predictive models in the manufacturing realm.

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Dennis Gross, Helge Spieker, Arnaud Gotlieb, Ricardo Knoblauch. 2024-03-27. Enhancing Manufacturing Quality Prediction Models through the Integration of Explainability Methods. https://arxiv.org/abs/2403.18731

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