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Enhancing Manufacturing Quality Prediction Models through the Integration of Explainability Methods (2403.18731v1)
Published 27 Mar 2024 in cs.AI, cs.CV, cs.CY, and cs.LG
Abstract: This research presents a method that utilizes explainability techniques to amplify the performance of 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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