A Cause-Selecting Control Chart Method for Monitoring and Diagnosing Dependent Manufacturing Process Stages

Abstract

Many industrial products are normally processed through multiple manufacturing process stages before it becomes a final product. Statistical process control techniques often utilize standard Shewhart control charts to monitor these process stages. If the process stages are independent, this is a meaningful procedure. However, they are not independent in many manufacturing scenarios. The standard Shewhart control charts can not provide the information to determine which process stage or group of process stages has caused the problems (i.e., standard Shewhart control charts could not diagnose dependent manufacturing process stages). This study proposes a selective neural network ensemble-based cause-selecting system of control charts to monitor these process stages and distinguish incoming quality problems and problems in the current stage of a manufacturing process. Numerical results show that the proposed method is an improvement over the use of separate Shewhart control chart for each of dependent process stages, and even ordinary quality practitioners who lack of expertise in theoretical analysis can implement regression estimation and neural computing readily.