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Classification of the Mixture Disturbance Patterns for a Manufacturing Process

Yuehjen E. Shao and Po-Yu Chang
Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City, Taiwan, R.O.C.

Abstract—The success of integration of Statistical Process Control (SPC) and Engineering Process Control (EPC) has been reported in recent years. However, the SPC Control Chart Pattern (CCP) has become more difficult to be classified due to the fact that the process disturbances were embedded in the system. Although some studies have focused on the classification tasks for a manufacturing process, they only considered the individual or basic disturbance type in a process. There has been very little research addressed on the classification of mixture of individual disturbance in a SPC-EPC system. The purpose of the present study is therefore to propose an effective way to deal with the classification of mixture CCPs for a SPC-EPC process. Because of its excellent performance on classification tasks, this study employs the Artificial Neural Network (ANN) approach to recognize the mixture patterns of the underlying disturbances. Simulation results revealed that the proposed SVM scheme is able to effectively identify various mixture types of disturbances for an SPC-EPC system.

Index Terms—disturbance, mixture pattern, artificial neural network, SPC, EPC

Cite: Yuehjen E. Shao and Po-Yu Chang, "Classification of the Mixture Disturbance Patterns for a Manufacturing Process," Journal of Industrial and Intelligent Information, Vol. 4, No. 4, pp. 252-256, July 2016. doi: 10.18178/jiii.4.4.252-256
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