  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.
