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Statistical process control with autocorrelated data using neural networks
Author(s)
Abrahams, Rachel
Date Issued
October 24, 2011
Type
Conference Paper
Abstract
Statistical Process Control (SPC) is widely used for monitoring the performance of processes in manufacturing. Traditional SPC methods require trained individuals to read data which results in slow and limited detection. Much research has been devoted into developing an online automated system for SPC, so that the abnormality can be detected quickly and corrected by the process operation. To build a system as such, artificial neural networks (ANN) are widely used as tools where complex patterns can be difficult to recognize. Many research projects involve using random data patterns for training and recognition of patterns for ANN/SPC applications. However, many manufacturing processes involve autocorrelated data, to determine the effect of autocorrelated data, green sand data was analyzed and a neural network was built and trained to analyze a number of out of control patterns. Overall, the network performed best for detecting larger mean shifts. © 2011 IEEE.
Citation
2011 IEEE International Conference on Quality and Reliability Icqr 2011, 283-287, 2011