Abstract
In most of the manufacturing processes, we encounter different quality characteristics of a product and process. These characteristics can be categorized into two kinds; study variables (variable of interest) and the supporting/explanatory variables. Sometime, a linear relationship might exist between the study and supporting variable, which is called simple linear profiles. This study focuses on the simple linear profiles under assorted control charting approach to detect the large, moderate and small disturbances in the process parameters. The evaluation of the proposed assorted method is assessed by using numerous performance measures, for instance, average run length, relative average run length, extra and sequential extra quadratic losses. A comparative analysis of the proposal is also carried out with some existing linear profile methods including Shewhart_3, Hotelling's T2, EWMA_3, EWMA/R and CUSUM_3 charts. Finally, a real-life application of the proposed assorted chart is presented to monitor thermal management of diamond-copper composite.
Highlights
Control charts are magnificently applied in many industrial processes and assist the specialists in improving the performance of a process by decreasing the process variation
IMPLEMENTATION OF ASSORTED_3 CHART In this study, we have considered the explanatory variable (PCC) and its values are fixed as (X = 425, 450, 475, 500 and 525) while the densification (Y ) is considered as a predictor variable
SUMMARY AND CONCLUSIONS Monitoring methods based on simple linear profiles is an emerging area within statistical process control (SPC)
Summary
Control charts are magnificently applied in many industrial processes and assist the specialists in improving the performance of a process by decreasing the process variation. The max and sum of square based linear profile monitoring methods were discussed by Mahmood, et al [23], and a progressive approach for simple linear profile was suggested by Saeed, et al [24]. The Shewhart based structures are useful to detect a large amount of shift in the process parameter while for the detection of small to moderate changes, EWMA and CUSUM charts were used (cf Faisal, et al [35]) Instead of these charts, Abbas, et al [36] proposed a method, which is compatible for all type of shifts (i.e., small, moderate and large) and referred to the assorted_3 chart.
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