A New Diagnosis System Based on Fuzzy Reasoning to Detect Mean And/or Variance Shifts in a Process

Statistical process control is a very useful method to improve the product quality and reduce reworks and scraps. In a production environment, control charts are the most important tool to determine whether a process is in-control or out-of-control. Control charts are to detect the occurrence of the shifts in a process rapidly so that their causes can be found and the necessary corrective action can be taken before a large quantity of nonconforming products are manufactured. The determination of variability affects the cost and the quality in a process. Considering the cost that is caused by delay in defining the variability, it is important to determine the variation correctly and quickly in a production process. This paper presents a new method based on a fuzzy inference system for determining shifts in the process. The Fuzzy Inference Control System includes four stages to detect and distinguish mean and/or variance shifts in the quality characteristic. Furthermore, the performance of the proposed method is examined and compared with that of Shewhart Control Charts by evaluating Type II error. In addition, the proposed model is evaluated by comparing performances of the joint X-bar and R charts, and X-bar and s charts for different sample sizes.