Bivariate SPC Chart Pattern Recognition Using Modular-Neural Network

Abstract

In statistical process control (SPC), monitoring and identifying unnatural variation in manufacturing process is challenging when dealing with two correlated quality variables (bivariate). The conventional multivariate SPC charts were designed only for triggering unnatural variation but it is not provide information towards diagnosis. In recent years, several SPC chart pattern recognition schemes were proposed to provide information towards diagnosis based on various category of unnatural variation. In this paper, the modular neural-network scheme was developed to identify nine category of bivariate SPC chart patterns. The success factor for the scheme is outlined as a new perspective in realizing accurate monitoring-diagnosis in quality control.