Using a classifier ensemble for proactive quality monitoring and control: the impact of the choice of classifiers types, selection criterion, and fusion process

Abstract : In recent times, the manufacturing processes are faced with many external or internal (the increase of customized product rescheduling , process reliability,..) changes. Therefore, monitoring and quality management activities for these manufacturing processes are difficult. Thus, the managers need more proactive approaches to deal with this variability. In this study, a proactive quality monitoring and control approach based on classifiers to predict defect occurrences and provide optimal values for factors critical to the quality processes is proposed. In a previous work (Noyel et al. 2013), the classification approach had been used in order to improve the quality of a lacquering process at a company plant; the results obtained are promising, but the accuracy of the classification model used needs to be improved. One way to achieve this is to construct a committee of classifiers (referred to as an ensemble) to obtain a better predictive model than its constituent models. However, the selection of the best classification methods and the construction of the final ensemble still poses a challenging issue. In this study, we focus and analyze the impact of the choice of classifier types on the accuracy of the classifier ensemble; in addition, we explore the effects of the selection criterion and fusion process on the ensemble accuracy as well. Several fusion scenarios were tested and compared based on a real-world case. Our results show that using an ensemble classification leads to an increase in the accuracy of the classifier models. Consequently, the monitoring and control of the considered real-world case can be improved.
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Submitted on : Wednesday, April 4, 2018 - 9:21:39 AM
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Philippe Thomas, Hind Bril El Haouzi, Marie-Christine Suhner, André Thomas, Emmanuel Zimmermann, et al.. Using a classifier ensemble for proactive quality monitoring and control: the impact of the choice of classifiers types, selection criterion, and fusion process. Computers in Industry, Elsevier, 2018, 99, pp.193-204. ⟨10.1016/j.compind.2018.03.038⟩. ⟨hal-01757831⟩

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