Design optimization of ANN-based pattern recognizer for multivariate quality control

Abdul Jamil, Muhamad Faizal (2013) Design optimization of ANN-based pattern recognizer for multivariate quality control. Masters thesis, Universiti Tun Hussein Onn Malaysia.

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Abstract

In manufacturing industries, process variation is known to be major source of poor quality. As such, process monitoring and diagnosis is critical towards continuous quality improvement. This becomes more challenging when involving two or more correlated variables or known as multivariate. Process monitoring refers to the identification of process status either it is running within a statistically in-control or out-of-control condition, while process diagnosis refers to the identification of the source variables of out-of-control process. The traditional statistical process control (SPC) charting scheme are known to be effective in monitoring aspects, but they are lack of diagnosis. In recent years, the artificial neural network (ANN) based pattern recognition schemes has been developed for solving this issue. The existing ANN model recognizers are mainly utilize raw data as input representation, which resulted in limited performance. In order to improve the monitoring-diagnosis capability, in this research, the feature based input representation shall be investigated using empirical method in designing the ANN model recognizer.

Item Type:Thesis (Masters)
Subjects:T Technology > TS Manufactures > TS155-194 Production management. Operations management
ID Code:4720
Deposited By:Normajihan Abd. Rahman
Deposited On:21 Sep 2014 16:23
Last Modified:21 Sep 2014 16:23

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