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A bootstrap method for uncertainty estimation in quality correlation algorithm for risk based tolerance synthesis

Raed S. Batbooti, R.S. Ransing, M.R. Ransing, Rajesh Ransing Orcid Logo

Computers & Industrial Engineering

Swansea University Author: Rajesh Ransing Orcid Logo

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DOI (Published version): 10.1016/j.cie.2016.09.002

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
first_indexed 2016-09-05T12:54:26Z
last_indexed 2018-02-09T05:15:04Z
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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 ACEM 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 31 12 2016 2016-12-31 10.1016/j.cie.2016.09.002 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University 2017-08-03T13:31:39.3910247 2016-09-05T09:21:43.1653675 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical 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
author2 Raed S. Batbooti
R.S. Ransing
M.R. Ransing
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 Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
document_store_str 1
active_str 0
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 2016-12-31T06:56:48Z
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score 11.252118

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