An augmented neural network classification approach to detecting mean shifts in correlated manufacturing process parameters

Statistical process control (SPC) techniques have traditionally been used to identify when the mean of a manufacturing process has shifted out of control. In situations where there is correlation among the observed outputs of the process, however, the underlying assumptions of SPC are violated and alternative approaches such as neural networks become necessary in order to characterize the process behaviour. This paper discusses the development of a neural network technique that provides a significantly improved capability for recognizing these process shifts as compared to the current techniques in the literature. The procedure in question is an augmented neural-network based approach, which incorporates a data processing classification algorithm that provides information to facilitate early detection of out of control operating conditions. This approach is shown to improve significantly upon the performance of previous neural network techniques for identifying process shifts in the presence of correlation.