An Approach for Adaptive Parameter Setting in Manufacturing Processes

Sonja Strasser, Shailesh Tripathi, Richard Kerschbaumer

Abstract

In traditional manufacturing processes the selection of appropriate process parameters can be a difficult task which relies on rule-based schemes, expertise and domain knowledge of highly skilled workers. Usually the parameter settings remain the same for one production lot, if an acceptable quality is reached. However, each part processed has its own history and slightly different properties. Individual parameter settings for each part can further increase the quality and reduce scrap. Machine learning methods offer the opportunity to generate models based on experimental data, which predict optimal parameters depending on the state of the produced part and its manufacturing conditions. In this paper, we present an approach for selecting variables, building and evaluating models for adaptive parameter settings in manufacturing processes and the application to a real-world use case.

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Paper Citation


in Harvard Style

Strasser S. and Kerschbaumer R. (2018). An Approach for Adaptive Parameter Setting in Manufacturing Processes.In Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-318-6, pages 24-32. DOI: 10.5220/0006894600240032


in Bibtex Style

@conference{data18,
author={Sonja Strasser and Richard Kerschbaumer},
title={An Approach for Adaptive Parameter Setting in Manufacturing Processes},
booktitle={Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2018},
pages={24-32},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006894600240032},
isbn={978-989-758-318-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - An Approach for Adaptive Parameter Setting in Manufacturing Processes
SN - 978-989-758-318-6
AU - Strasser S.
AU - Kerschbaumer R.
PY - 2018
SP - 24
EP - 32
DO - 10.5220/0006894600240032