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Authors: P. Thomas ; M. Noyel ; M. C. Suhner ; P. Charpentier and A. Thomas

Affiliation: Lorraine-Université and CNRS Faculté des Sciences et Techniques, France

Keyword(s): Neural Network, Product Quality, Neural Network Ensemble, Multivariate Quality Control, Classification, Classifiers Ensemble.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer-Supported Education ; Domain Applications and Case Studies ; Fuzzy Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Industrial, Financial and Medical Applications ; Methodologies and Methods ; Neural Based Data Mining and Complex Information Processing ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

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 several formats:
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 - NCTA, (IJCCI 2013) ISBN 978-989-8565-77-8; ISSN 2184-2825, pages 515-522. DOI: 10.5220/0004556505150522

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 - NCTA, (IJCCI 2013)},


JO - Proceedings of the 5th International Joint Conference on Computational Intelligence - NCTA, (IJCCI 2013)
TI - Neural Networks Ensemble for Quality Monitoring
SN - 978-989-8565-77-8
IS - 2184-2825
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