Pattern Recognition for Manufacturing Process Variation using Ensembled Artificial Neural Network

Abstract

In quality control, monitoring and diagnosis of multivariate out of control condition is essential in today’s manufacturing industries. The simplest case involves two correlated variables; for instance, monitoring value of temperature and pressure in our environment. Monitoring refers to the identification of process condition either it is running in control or out of control. Diagnosis refers to the identification of source variables (X1 and X2) for out of control. In this study, an ensemble artificial neural network scheme was investigated in quality control of process in Hard Disk Drive manufacturing. This process was selected since it less reported in the literature. In the related point of view, this study should be useful in monitoring and diagnose the bivariate process pattern in Hard Disk Drive manufacturing process. The result of this study, suggested this scheme has a superior performance compared to the traditional artificial intelligence, namely single isolated Artificial Neural Network. In monitoring, ANN expected to be effective in rapid detection of out of control without false alarm. In diagnosis, this scheme was effective to be applied in identifying various types of process variation such as loading error, offsetting tool, and inconsistent pressure in clamping fixture. Whereby, diagnosis cannot be performed by traditional control chart. This study is useful for quality control practitioner, particularly in manufacturing industry.