In recent times, the manufacturing processes are faced with many external or internal (the increase of customized product re-scheduling, 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 , 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. Computers in Industry 99 (2018) 193-204 2 1 IntroductionThe growing need for complex and customized products and services has led to increased complexity of the associated manufacturing processes as well; this complexity may be attributed to several different sources depending on the features of the product/service and the organizational structure of the companies. Consequently, the manufacturing and control tasks become difficult, including their monitoring and quality management. Despite the many methods and operational tools that have been developed in the last few decades, the executive management personnel of these companies are always seeking new approaches and tools to analyze their specific problems and devise potential improvement strategies at several levels.These approaches and tools can thus be regarded as decision-aiding tools to identify not only the root causes of defects but also factors critical to the quality of their products/services; in particular, the aim of the executive managers is to eliminate those causes or limit their impact by setting the factors critical to quality at adequate or optimal levels. Among these approaches, a common method is the Design of Experiments (DoE); however, the primary disadvantage of such an approach is that the improvement process is considered "off-line." Indeed, even if a robust process is established by successfully optimizing the controllable and uncontrol...

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Nowadays complex control systems are rising and especially hybrid control architectures which are developed to face the manufacturing control challenges that occur with the last industrial revolution and the emerging of industry 4.0. This work presents an application, on a testing platform, of a scheduling algorithm, with multicriteria objectives, developed for Acta-Mobilier company suffering from high rework rate. This algorithm will inscribe itself in a hybrid control system based on smart entities. The main objective is to validate the contribution of the proposed algorithm in a disturbed environment. The platform, implemented with a multi-agent's system, allows to measure the reliability of the proposed algorithm used on a complex system in the particular case of high rework rate.

The scheduling problem in manufacturing companies with high rework rates remains a complex research area to date. This paper presents a new approach for manufacturing scheduling that combines a predictive schedule with a proactive multicriteria decision-making method based on smart batches and their quality prediction capability. Each batch embeds an algorithm that allows it to predict its quality out of the next workstation. As soon as a batch determines that its process is too hazardous, a collaborative rescheduling decision, using the analytic hierarchy process (AHP), is initiated with its peer. This article details the proposed approach along with the AHP structure and presents the considered decision problem. A simulation model inspired by a lacquering-robot case study is described to validate this proposition. Then, the results of different scenarios are presented and discussed, highlighting the impact of social myopia on smart batches.