Synergistic artificial neural network scheme for monitoring and diagnosis of multivariate process variation in mean shifts

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, a synergistic artificial neural network scheme was investigated in quality control of process in plastic injection moulding part. This process was selected since it less reported in the literature. In the related point of view, this study should be useful in minimizing the cost of waste materials. The result of this study, suggested this scheme has a superior performance compared to the traditional control chart, namely Multivariate Exponentially Weighted Moving Average (MEWMA). In monitoring, it is effective in rapid detection of out of control without false alarm. In diagnosis, it is able to accurately identify for source of variables. Whereby, diagnosis cannot be performed by traditional control chart. This study is useful for quality control practitioner, particularly in plastic injection moulding industry.

Name dc:type.qualificationname

mphil

Level dc:type.qualificationlevel

masters

Grantor dc:publisher.institution

Universiti Tun Hussein Malaysia

Year dc:date.issued

2014

Author dc:creator

• Marian, Mohd Fairuz

• TS Manufactures
• TS155-194 Production management. Operations management

Language dc:language

en