Enhancing Manufacturing Quality Prediction Models through the Integration of Explainability Methods

• URL: http://arxiv.org/abs/2403.18731v1
• Date: Wed, 27 Mar 2024 16:21:24 GMT
• Title: Enhancing Manufacturing Quality Prediction Models through the Integration of Explainability Methods
• Authors: Dennis Gross, Helge Spieker, Arnaud Gotlieb, Ricardo Knoblauch,
• Abstract summary: 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. 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 study highlights the usefulness of explainability techniques in both explaining and optimizing predictive models in the manufacturing realm.
• Score: 10.32461766065764
• License: http://creativecommons.org/licenses/by/4.0/
• 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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