Neural Networks ensemble for quality monitoring

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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Submitted on : Monday, November 4, 2013 - 9:31:36 AM
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Philippe Thomas, Mélanie Noyel, Marie-Christine Suhner, Patrick Charpentier, André Thomas. Neural Networks ensemble for quality monitoring. 5th International Conference on Neural Computation Theory and Application, NCTA'13, Sep 2013, Vilamoura, Portugal. pp.CDROM. ⟨hal-00879477⟩



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