Neural Networks Ensemble for Quality Monitoring

P. Thomas, M. Noyel, M. C. Suhner, P. Charpentier, A. Thomas

2013

Abstract

Product quality level is a key concept for companies' competitiveness. Different tools may be used to improve quality such as the seven basic quality tools or experimental design. In addition, the need of traceability leads companies to collect and store production data. Our paper aims to show that we can ensure the required quality thanks to an "on line quality approach" based on exploitation of collected data by using neural networks tools. A neural networks ensemble is proposed to classify quality results which can be used in order to prevent defects occurrence. This approach is illustrated on an industrial lacquering process. Results of the neural networks ensemble are compared with the ones obtained with the best neural network classifier.

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Paper Citation


in Harvard Style

Thomas P., Noyel M., Suhner M., Charpentier P. and Thomas A. (2013). Neural Networks Ensemble for Quality Monitoring . In Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013) ISBN 978-989-8565-77-8, pages 515-522. DOI: 10.5220/0004556505150522


in Bibtex Style

@conference{ncta13,
author={P. Thomas and M. Noyel and M. C. Suhner and P. Charpentier and A. Thomas},
title={Neural Networks Ensemble for Quality Monitoring},
booktitle={Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013)},
year={2013},
pages={515-522},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004556505150522},
isbn={978-989-8565-77-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013)
TI - Neural Networks Ensemble for Quality Monitoring
SN - 978-989-8565-77-8
AU - Thomas P.
AU - Noyel M.
AU - Suhner M.
AU - Charpentier P.
AU - Thomas A.
PY - 2013
SP - 515
EP - 522
DO - 10.5220/0004556505150522