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In recent times, the manufacturing processes are faced with many external orinternal (the increase of customized product rescheduling , processreliability,..) changes. Therefore, monitoring and quality managementactivities for these manufacturing processes are difficult. Thus, the managersneed more proactive approaches to deal with this variability. In this study, aproactive quality monitoring and control approach based on classifiers topredict defect occurrences and provide optimal values for factors critical tothe quality processes is proposed. In a previous work (Noyel et al. 2013), theclassification approach had been used in order to improve the quality of alacquering process at a company plant; the results obtained are promising, butthe accuracy of the classification model used needs to be improved. One way toachieve this is to construct a committee of classifiers (referred to as anensemble) to obtain a better predictive model than its constituent models.However, the selection of the best classification methods and the constructionof the final ensemble still poses a challenging issue. In this study, we focusand analyze the impact of the choice of classifier types on the accuracy of theclassifier ensemble; in addition, we explore the effects of the selectioncriterion and fusion process on the ensemble accuracy as well. Several fusionscenarios were tested and compared based on a real-world case. Our results showthat using an ensemble classification leads to an increase in the accuracy ofthe classifier models. Consequently, the monitoring and control of theconsidered real-world case can be improved.

Using a Classifier Ensemble for Proactive Quality Monitoring and  Control: the impact of the choice of classifiers types, selection criterion,  and fusion process