| Summary: | 碩士 === 國立交通大學 === 工業工程與管理系所 === 93 === Statistical process control (SPC) is a widely employed quality control method in industry. SPC is mainly designed for monitoring single quality characteristic. However, as the design of a product / process becomes complex, a process usually has multiple quality characteristics related to it. These characteristics must be monitored by multivariate SPC. When the autocorrelation is present in the process data, the traditional SPC may mislead the results. Hence, the autocorrelated data must be treated to eliminate the autocorrelation effect before employing SPC to detect the assignable causes. Besides, chance causes also have impact on the processes. When the process is out of control but no assignable cause is found, it can be adjusted by employing engineering process control (EPC). However, EPC may result in unnecessary adjustments. This study presents an integrated SPC and EPC procedure for multivariate autocorrelated process. The SPC procedure constructs a predicting model by group method of data handling (GMDH), which can transfer the autocorrelated data into uncorrelated data. Then, the Hotelling T2 and multivariate cumulative sum control charts are constructed to monitor the process. The EPC procedure constructs a controller utilizing GMDH to adjust the multiple quality characteristics to their target values. Finally, this study uses a set of simulated chemical mechanical polishing (CMP) data to verify the effectiveness of the proposed procedure.
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