INTEGRATING SPC/EPC, ICA AND NEURAL NETWORKS TO DEVELOP AN IDENTIFICATION TECHNIQUE

摘要

There are many studies have been conducted to the integrated use of statistical process control (SPC) and engineering process control (EPC) because using them individually cannot optimally control the manufacturing process. The majority of these studies have reported that the integrated approach has better performance than that by using only SPC or EPC. Among all these studies, most of them have assumed that the assignable causes of process disturbance can be effectively identified and removed by SPC techniques. However, these techniques are typically time-consuming and thus make the search hard to implement in practice. The paper discusses the development of neural network models with independent component analysis (ICA) to identify the disturbance and recognize shifts in the correlated process parameters. Moreover, these designed network models can be used to monitor and eliminate manufacturing process parameters when disturbance happens in the underlying process. As the results reveal, the shift of disturbance can be identified successfully by the proposed approach.