Enhancing Manufacturing Quality Prediction Models through the Integration of Explainability Methods
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929292171018240 |
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| author | Gross, Dennis Spieker, Helge Gotlieb, Arnaud Knoblauch, Ricardo |
| author_facet | Gross, Dennis Spieker, Helge Gotlieb, Arnaud Knoblauch, Ricardo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_18731 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Manufacturing Quality Prediction Models through the Integration of Explainability Methods Gross, Dennis Spieker, Helge Gotlieb, Arnaud Knoblauch, Ricardo Artificial Intelligence Computer Vision and Pattern Recognition Computers and Society Machine Learning 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. |
| title | Enhancing Manufacturing Quality Prediction Models through the Integration of Explainability Methods |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2403.18731 |