| Summary: | 碩士 === 國立高雄應用科技大學 === 工業工程與管理系碩士班 === 95 === Statistical process control is the method that monitors process quality characteristic. Through control charts, one can detect whether the present process malfunction. However, some annoyances may arise while the engineers need to choose appropriately control chart under different levels of process variation.
The Shewhart, cumulative sum and exponentially weighted moving average control charts have been widely used for monitoring semiconductor manufacturing processes. Generally weighted moving average control chart is a new method of SPC, which was proposed by Sheu and Lin (2003). The main objective of this research is using step-by-step procedures to present a comparative study of the monitoring performance for Shewhart, CUSUM, EWMA and GWMA control charts. According to a specified in-control average run length , we determine the parameters of each control chart based on the Monte Carlo numerical simulation. The setting of the parameters in each control chart was displayed and tabulated. While the process means or the process standard deviations were changing in different levels, the performance of each weighted control chart can be then compared by using ARL. A rule of thumb for selecting better control schemes is provided as a truthfully reference to help engineers in choosing the more appropriate control charts immediately when the assignable causes occurred.
The results show that when the process shift is less than 2.00 standard deviations, the GWMA control chart is better than other weighted control charts. For the process shift is 3.00 standard deviations, the CUSUM chart is proposed to detect the shift quickly. As well as the process standard deviation is changed, the Shewhart chart is superior to other charts in detecting process variations.
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