Enhancing Manufacturing Quality Prediction Models through the Integration of Explainability Methods
<|reference_start|>Enhancing Manufacturing Quality Prediction Models through the Integration of Explainability Methods: 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.<|reference_end|>
@article{gross2024enhancing,
    title={Enhancing Manufacturing Quality Prediction Models through the
  Integration of Explainability Methods},
    author={Dennis Gross, Helge Spieker, Arnaud Gotlieb, Ricardo Knoblauch},
    journal={arXiv preprint arXiv:2403.18731},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.18731},
    primaryClass={cs.AI cs.CV cs.CY cs.LG}
}