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