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Neural Network Inverse Model for Quality Monitoring - Application to a High Quality Lackering Process

Topics: Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World applications, Financial Applications, Neural Prostheses and Medical Applications, Neural based Data Mining and Complex Information Processing, Neural Network Software and Applications, Applications of Deep Neural networks, Robotics and Control Applications

Authors: Philippe Thomas 1 ; Marie-Christine Suhner 1 ; Emmanuel Zimmermann 2 ; Hind Bril El Haouzi 1 ; André Thomas 1 and Mélanie Noyel 3

Affiliations: 1 Université de Lorraine and CNRS, France ; 2 Université de Lorraine, CNRS and Acta-Mobilier, France ; 3 Acta-Mobilier, France

Keyword(s): Neural Network, Product Quality, Inverse Model, Quality Monitoring.

Abstract: The quality requirement is an important issue for modern companies. Many tools and philosophies have been proposed to monitor quality, including the seven basic tools or the experimental design. However, high quality requirement may lead companies to work near their technological limit capabilities. In this case, classical approaches to monitor quality may be insufficient. That is why on line quality monitoring based on the neural network prediction model has been proposed. Within this philosophy, the dataset is used in order to determine the optimal setting considering the operating point and the product routing. An inverse model approach is proposed here in order to determine directly the optimal setting in order to avoid defects production. A comparison between the use of a classical multi-inputs multi-outputs NN model and a sequence of different multi-inputs single-output NN models is performed. The proposed approach is tested on a real application case.

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Paper citation in several formats:
Thomas, P.; Suhner, M.; Zimmermann, E.; Bril El Haouzi, H.; Thomas, A. and Noyel, M. (2017). Neural Network Inverse Model for Quality Monitoring - Application to a High Quality Lackering Process. In Proceedings of the 9th International Joint Conference on Computational Intelligence (IJCCI 2017) - IJCCI; ISBN 978-989-758-274-5; ISSN 2184-3236, SciTePress, pages 186-191. DOI: 10.5220/0006485901860191

@conference{ijcci17,
author={Philippe Thomas. and Marie{-}Christine Suhner. and Emmanuel Zimmermann. and Hind {Bril El Haouzi}. and André Thomas. and Mélanie Noyel.},
title={Neural Network Inverse Model for Quality Monitoring - Application to a High Quality Lackering Process},
booktitle={Proceedings of the 9th International Joint Conference on Computational Intelligence (IJCCI 2017) - IJCCI},
year={2017},
pages={186-191},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006485901860191},
isbn={978-989-758-274-5},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Computational Intelligence (IJCCI 2017) - IJCCI
TI - Neural Network Inverse Model for Quality Monitoring - Application to a High Quality Lackering Process
SN - 978-989-758-274-5
IS - 2184-3236
AU - Thomas, P.
AU - Suhner, M.
AU - Zimmermann, E.
AU - Bril El Haouzi, H.
AU - Thomas, A.
AU - Noyel, M.
PY - 2017
SP - 186
EP - 191
DO - 10.5220/0006485901860191
PB - SciTePress

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