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A Resetting Scheme for Process Parameters using the Mahalanobis-Taguchi System

Park, Chang-Soon

  • Received : 2012.05.10
  • Accepted : 2012.07.19
  • Published : 2012.08.31

Abstract

Mahalanobis-Taguchi system(MTS) is a statistical tool for classifying the normal group and abnormal group in multivariate data structures. In addition to the classification itself, the MTS uses a method for selecting variables useful for the classification. This method can be used efficiently especially when the abnormal group data are scattered without a specific directionality. When the feedback adjustment procedure through the measurements of the process output for controlling process input variables is not practically possible, the reset procedure can be an alternative one. This article proposes a reset procedure using the MTS. Moreover, a method for identifying input variables to reset is also proposed by the use of the contribution. The identification of the root-cause parameters using the existing dimension-reduced contribution tends to be difficult due to the variety of correlation relationships of multivariate data structures. However, it became possible to provide an improved decision when used together with the location-centered contribution and the individual-parameter contribution.

Keywords

Classification analysis;reset procedure;contribution;multivariate data;normal group;threshold

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Cited by

  1. DD-plot for Detecting the Out-of-Control State in Multivariate Process vol.26, pp.2, 2013, https://doi.org/10.5351/KJAS.2013.26.2.281
  2. Implementation of Mahalanobis-Taguchi System for the Election of Major League Baseball Hitters to the Hall of Fame vol.26, pp.2, 2013, https://doi.org/10.5351/KJAS.2013.26.2.223

Acknowledgement

Supported by : National Research Foundation of Korea(NRF)