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A bootstrap method for uncertainty estimation in quality correlation algorithm for risk based tolerance synthesis / Rajesh, Ransing

Computers & Industrial Engineering

Swansea University Author: Rajesh, Ransing

Abstract

A risk based tolerance synthesis approach is based on ISO9001:2015 quality standard’s risk based thinking. It analyses in-process data to discover correlations among regions of input data scatter and desired or undesired process outputs. Recently, Ransing, Batbooti, Giannetti, and Ransing (2016) pro...

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Published in: Computers & Industrial Engineering
ISSN: 0360-8352
Published: 2016
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URI: https://cronfa.swan.ac.uk/Record/cronfa29741
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first_indexed 2016-09-05T12:54:26Z
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spelling 2017-08-03T13:31:39.3910247 v2 29741 2016-09-05 A bootstrap method for uncertainty estimation in quality correlation algorithm for risk based tolerance synthesis 0136f9a20abec3819b54088d9647c39f 0000-0003-4848-4545 Rajesh Ransing Rajesh Ransing true false 2016-09-05 EEN A risk based tolerance synthesis approach is based on ISO9001:2015 quality standard’s risk based thinking. It analyses in-process data to discover correlations among regions of input data scatter and desired or undesired process outputs. Recently, Ransing, Batbooti, Giannetti, and Ransing (2016) proposed a quality correlation algorithm (QCA) for risk based tolerance synthesis. The quality correlation algorithm is based on the principal component analysis (PCA) and a co-linearity index concept (Ransing, Giannetti, Ransing, & James 2013). The uncertainty in QCA results on mixed data sets is quantified and analysed in this paper.The uncertainty is quantified using a bootstrap sampling method with bias-corrected and accelerated confidence intervals. The co-linearity indices use the length and cosine angles of loading vectors in a p-dimensional space. The uncertainty for all p-loading vectors is shown in a single co-linearity index plot and is used to quantify the uncertainty in predicting optimal tolerance limits. The effects of re-sampling distributions are analysed. The QCA tolerance limits are revised after estimating the uncertainty in limits via bootstrap sampling. The proposed approach has been demonstrated by analysing in-process data from a previously published case study. Journal Article Computers & Industrial Engineering 0360-8352 1 1 2016 2016-01-01 10.1016/j.cie.2016.09.002 COLLEGE NANME Engineering COLLEGE CODE EEN Swansea University 2017-08-03T13:31:39.3910247 2016-09-05T09:21:43.1653675 College of Engineering Engineering Raed S. Batbooti 1 R.S. Ransing 2 M.R. Ransing 3 Rajesh Ransing 0000-0003-4848-4545 4 0029741-05092016092231.pdf batbooti2016.pdf 2016-09-05T09:22:31.6830000 Output 1079998 application/pdf Accepted Manuscript true 2018-03-03T00:00:00.0000000 false
title A bootstrap method for uncertainty estimation in quality correlation algorithm for risk based tolerance synthesis
spellingShingle A bootstrap method for uncertainty estimation in quality correlation algorithm for risk based tolerance synthesis
Rajesh, Ransing
title_short A bootstrap method for uncertainty estimation in quality correlation algorithm for risk based tolerance synthesis
title_full A bootstrap method for uncertainty estimation in quality correlation algorithm for risk based tolerance synthesis
title_fullStr A bootstrap method for uncertainty estimation in quality correlation algorithm for risk based tolerance synthesis
title_full_unstemmed A bootstrap method for uncertainty estimation in quality correlation algorithm for risk based tolerance synthesis
title_sort A bootstrap method for uncertainty estimation in quality correlation algorithm for risk based tolerance synthesis
author_id_str_mv 0136f9a20abec3819b54088d9647c39f
author_id_fullname_str_mv 0136f9a20abec3819b54088d9647c39f_***_Rajesh, Ransing
author Rajesh, Ransing
format Journal article
container_title Computers & Industrial Engineering
publishDate 2016
institution Swansea University
issn 0360-8352
doi_str_mv 10.1016/j.cie.2016.09.002
college_str College of Engineering
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hierarchy_top_id collegeofengineering
hierarchy_top_title College of Engineering
hierarchy_parent_id collegeofengineering
hierarchy_parent_title College of Engineering
department_str Engineering{{{_:::_}}}College of Engineering{{{_:::_}}}Engineering
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description A risk based tolerance synthesis approach is based on ISO9001:2015 quality standard’s risk based thinking. It analyses in-process data to discover correlations among regions of input data scatter and desired or undesired process outputs. Recently, Ransing, Batbooti, Giannetti, and Ransing (2016) proposed a quality correlation algorithm (QCA) for risk based tolerance synthesis. The quality correlation algorithm is based on the principal component analysis (PCA) and a co-linearity index concept (Ransing, Giannetti, Ransing, & James 2013). The uncertainty in QCA results on mixed data sets is quantified and analysed in this paper.The uncertainty is quantified using a bootstrap sampling method with bias-corrected and accelerated confidence intervals. The co-linearity indices use the length and cosine angles of loading vectors in a p-dimensional space. The uncertainty for all p-loading vectors is shown in a single co-linearity index plot and is used to quantify the uncertainty in predicting optimal tolerance limits. The effects of re-sampling distributions are analysed. The QCA tolerance limits are revised after estimating the uncertainty in limits via bootstrap sampling. The proposed approach has been demonstrated by analysing in-process data from a previously published case study.
published_date 2016-01-01T18:46:42Z
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score 10.873183