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.