Enhancing Manufacturing Quality Prediction Models Through the Integration of Explainability Methods

Dennis Gross, Helge Spieker, Arnaud Gotlieb, Ricardo Knoblauch

2024

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.

Paper Citation

in Harvard Style

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, SciTePress, pages 898-905. DOI: 10.5220/0012417800003636

in Bibtex Style

@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},
}

in EndNote Style

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
AU - Gross D.
AU - Spieker H.
AU - Gotlieb A.
AU - Knoblauch R.
PY - 2024
SP - 898
EP - 905
DO - 10.5220/0012417800003636
PB - SciTePress