(X)OVER-BAR CONTROL CHART PATTERN IDENTIFICATION THROUGH EFFICIENT OFF-LINE NEURAL-NETWORK TRAINING

Back-propagation pattern recognizers (BPPR) are proposed to identify unnatural patterns exhibited on Shewhart control charts. These unnatural patterns, such as cycles and trends, can provide valuable information for real-time process control. In a computer-integrated manufacturing environment, the operator need not routinely monitor the control chart but, rather, can be alerted to patterns by a computer signal generated by the propose algorithm. In this paper, an off-line analysis is performed to investigate the training and learning speed of these BPPRs on simulated xBAR data. The best configuration of the network is further tested to demonstrate the classification capability of the proposed BPPR.