loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock
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.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.144.212.145

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

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

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic