The Performance of Triple Sampling X Control Chart with Measurement Errors. Issue 5 (3rd September 2022)

• Record Type: Journal Article
• Authors: Maleki, Mohammad Reza; Salmasnia, Ali; Yarmohammadi Saber, Shayesteh
• Is Part Of: Quality technology & quantitative management. Volume 19:Issue 5(2022)
• Page Start: 587
• Page End: 604
• Publication Date: 2022-09-03
• Subjects: Multiple measurement based remedial approach -- additive covariate model -- measurement errors -- triple sampling Xˉ chart -- Run length; Quality control -- Periodicals; Quality control -- Statistical methods -- Periodicals; Industrial management -- Periodicals; Industrial management; Management -- Research -- Methodology -- Periodicals; Qualitative research -- Periodicals; Management; Quality control; Quality control -- Statistical methods; Periodicals; 658.00721
• DOI: 10.1080/16843703.2022.2040702
• Languages: English
• ISSNs: 1684-3703

Abstract

ABSTRACT: The presence of measurement errors can seriously alter the statistical performance of Phase II control charts. Up today, no research on designing the triple sampling control charts taking into account the gauge measurement errors is reported in the existing literature. In this paper, we study the adverse effect of measurement errors on detecting performance of triple sampling (TS)- X ˉ control chart based on an additive covariate model. Three multiple measurement based triple sampling (MMBTS) schemes are developed to reduce the undesired impact of gauge inability on detecting performance of TS- X ˉ chart. Through simulation studies in terms of average run length ( ARL ) and standard deviation of run length ( SDRL ), it is indicated that the run length characteristics of the TS- X ˉ is significantly affected by the measurement errors. The results also confirm that all proposed remedial approaches can effectively reduce the undesired impact of imprecise measurements on performance of TS- X ˉ chart. A sensitivity analysis is also carried out to evaluate how the covariate model parameters affect the detection performance of the TS- X ˉ chart. Finally, using a real industrial data obtained from the spring production system, we demonstrate the performance of TS- X ˉ chart when the measurement errors exist.