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Authors: Dennis Gross 1 ; Helge Spieker 1 ; Arnaud Gotlieb 1 and Ricardo Knoblauch 2

Affiliations: 1 Simula Research Laboratory, Oslo, Norway ; 2 Ecole Nationale Supérieure d’Arts et Métiers, Aix-en-Provence, France

Keyword(s): Industrial Applications of Machine Learning, Explainable Machine Learning.

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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Paper citation in several formats:
Gross, D.; Spieker, H.; Gotlieb, A. and Knoblauch, R. (2024). Enhancing Manufacturing Quality Prediction Models Through the Integration of Explainability Methods. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4; ISSN 2184-433X, SciTePress, pages 898-905. DOI: 10.5220/0012417800003636

@conference{icaart24,
author={Dennis Gross. and Helge Spieker. and Arnaud Gotlieb. and Ricardo Knoblauch.},
title={Enhancing Manufacturing Quality Prediction Models Through the Integration of Explainability Methods},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={898-905},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012417800003636},
isbn={978-989-758-680-4},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Enhancing Manufacturing Quality Prediction Models Through the Integration of Explainability Methods
SN - 978-989-758-680-4
IS - 2184-433X
AU - Gross, D.
AU - Spieker, H.
AU - Gotlieb, A.
AU - Knoblauch, R.
PY - 2024
SP - 898
EP - 905
DO - 10.5220/0012417800003636
PB - SciTePress

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