A neural network applied to pattern recognition in statistical process control

In processes, the causes of variations may be categorized as chance (unassignable) causes and special (assignable) causes. The variations due to chance causes are inevitable, and difficult to detect and identify. On the other hand, the variations due to special causes prevent the process being a stable and predictable. Such variations should be determined effectively and eliminated from the process by taking the necessary corrective actions to maintain the process in control and improve the quality of the products as well. In this study, a multilayered neural network trained with a backpropagation algorithm was applied to pattern recognition on control charts. The neural network was experimented on a set of generated data. A method, histogram representation, was proposed for data preparation. The results obtained from the experiments were compared in terms of recognition accuracy.