生產(chǎn)過程的動態(tài)監(jiān)測：灰色預(yù)測模型的一種應(yīng)用畢業(yè)論文外文翻譯-展示頁
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【正文】 production process is outofcontrol, a lot of defective products will have already been produced, especially when the process exhibits an apparent normal trend behavior or if the change is only slight. In this paper, we explore the application of grey forecasting models for predicting and monitoring production processes. The performance of control charts based on grey predictors for detecting process changes is investigated. The average run length(ARL) is used to measure the effectiveness when a mean shift exists. When a mean shift occurs, the grey predictors are found to be superior to the sample mean, especially if the number of subgroups used to pute the grey predictors is small. The grey predictor is also found to be very sensitive to the number of subgroups. Keywords Average run length 中文 3300字 出處： The International Journal of Advanced Manufacturing Technology, 2020, 27(56): 543546 Sequential monitoring of manufacturing processes: an application of grey forecasting models LiLin Ku TungChen Huang Abstract This study used statistical control charts as an efficient tool for improving and monitoring the quality of manufacturing processes. Under the normality assumption, when a process variable is within control limits, the process is treated as being incontrol. Sometimes, the process acts as an incontrol process for short periods。 Control chart Grey predictor 1 Introduction Statistical control charts have long been used as an efficient tool for improving and monitoring the quality of manufacturing processes. Traditional statistical process control (SPC) methods assume that the process variable is distributed normally, and that the observed data are independent. Under the normality assumption, when the process variable is within the control limits, the process is treated as being incontrol。 however, once the data show that the production process is outofcontrol, a lot of defective products have already been produced, especially when the process exhibits an apparent normal trend behavior[2] or if the change is only slight. Though these kinds of shifts in the process are not easy to detect, the process is nevertheless predictable. If the process failure costs are very large, then detecting these shifts as soon as possible bees very important. In this paper, we explore the application of grey forecasting models for predicting and monitoring production processes. The performance of control charts based on grey predictors for detecting process changes is studied. The average run length(ARL) is used to measure the effectiveness when a mean shift exists. The ARL means that an average number of observations is required before an outofcontrol signal is created indicating special circumstances. Small ARL values are desired. The performance of grey predictors is pared with sample means x . All procedures are studied via simulations. When a mean shift occurs, the grey predictors are found to be superior to the sample mean if the number of subgroups that are used to pute the grey predictors is small. The grey predictor is also found to be very sensitive to the number of subgroups. The advantage of the grey methods is that the grey predictor only needs a few samples in order to detect the process changes even when the process shifts are slight. The number of subgroups(samples) can be adjusted so that the performance of grey predictors can be changed according to the desired criteria. In the next section, the grey forecasting models are introduced and an overview of the proposed monitoring procedure will be given. The details of the numerical analytical results and conclusions are then given in Sect. 3, in which the results of the grey predictors are pared with sample means. The Type I error based on Xbar control charts for sample means and the grey predictors are also described. Finally, remendations and suggestions based on the results are then discussed in Sect. 4. 2 Grey forecasting models and procedural steps The grey s
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