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In statistical process control the quality
characteristics of product may shift in process mean and/or
change in process variability when the out-of-control
conditions were caused by assignable causes. When detecting
the changes of the process variability, people were used to
using R, S, S?charts to monitor the process. But previous
research has shown that these charts are not effective in
detecting a small change of the process variability. In this
research, a control procedure based on artificial
neural networks for monitoring the process variability
was developed. The performance of the proposed artificial
neural network has been evaluated by the average run length
(ARL), and a compression of the performance of artificial
neural network and exponentially weighted moving average(
EWMA) chart which using ln(S? to monitor process is
presented. Simulation results show that the neural
network performance in detecting the changes of the process
variability is superior to traditional EWMA control scheme.
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