Sequential monitoring of manufacturing processes: an application of grey forecasting models

被引:7
|
作者
Ku, LL [1 ]
Huang, TC [1 ]
机构
[1] Leader Univ, Dept Ind Management, Tainan 709, Taiwan
关键词
average run length; control chart; control limit; grey predictor;
D O I
10.1007/s00170-004-2198-0
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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 in-control. Sometimes, the process acts as an in-control process for short periods; however, once the data show that the production process is out-of-control, 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 compute the grey predictors is small. The grey predictor is also found to be very sensitive to the number of subgroups.
引用
收藏
页码:543 / 546
页数:4
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