Utilization of neural networks for the recognition of variance shifts in correlated manufacturing process parameters

Traditional statistical process control (SPC) charting techniques were developed for use in discrete industries where independence exists between process parameters over time. Process parameters from many manufacturing industries are not independent, however, but they are serially correlated. Consequently, the power of traditional SPC charts was greatly weakened. The paper discusses the development of neural network models to identify successfully shifts in the variance of correlated process parameters. These neural network models can be used to monitor manufacturing process parameters and signal when process adjustments are needed.