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A quality correlation algorithm for tolerance synthesis in manufacturing operations / Rajesh, Ransing; Cinzia, Giannetti

Computers & Industrial Engineering, Volume: 93, Pages: 1 - 11

Swansesa University Authors: Rajesh, Ransing, Cinzia, Giannetti

Abstract

The clause 6.1 of the ISO9001:2015 quality standard requires organisations to take specific actions to determine and address risks and opportunities in order to minimize undesired effects in the process and achieve process improvement. This paper proposes a new quality correlation algorithm to optim...

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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/cronfa25053
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spelling 2019-09-08T17:45:58.5465730 v2 25053 2015-12-14 A quality correlation algorithm for tolerance synthesis in manufacturing operations 0136f9a20abec3819b54088d9647c39f 0000-0003-4848-4545 Rajesh Ransing Rajesh Ransing true false a8d947a38cb58a8d2dfe6f50cb7eb1c6 0000-0003-0339-5872 Cinzia Giannetti Cinzia Giannetti true false 2015-12-14 EEN The clause 6.1 of the ISO9001:2015 quality standard requires organisations to take specific actions to determine and address risks and opportunities in order to minimize undesired effects in the process and achieve process improvement. This paper proposes a new quality correlation algorithm to optimise tolerance limits of process variables across multiple processes. The algorithm uses reduced p-dimensional principal component scores to determine optimal tolerance limits and also embeds ISO9001:2015’s risk based thinking approach. The corresponding factor and response variable pairs are chosen by analysing the mixed data set formulation proposed by Giannetti etl al. (2014) and co-linearity index algorithm proposed by Ransing et al. (2013). The goal of this tolerance limit optimisation problem is to make several small changes to the process in order to reduce undesired process variation. The optimal and avoid ranges of multiple process parameters are determined by analysing in-process data on categorical as well as continuous variables and process responses being transformed using the risk based thinking approach. The proposed approach has been illustrated by analysing in-process chemistry data for a nickel based alloy for manufacturing cast components for an aerospace foundry. It is also demonstrated how the approach embeds the risk based thinking into the in-process quality improvement process as required by the ISO9001:2015 standard. Journal Article Computers & Industrial Engineering 93 1 11 0360-8352 7Epsilon, Six Sigma, No-Fault-Found product failures, in-tolerance faults, in-process quality improvement, and cause and effect analysis. 1 3 2016 2016-03-01 10.1016/j.cie.2015.12.008 COLLEGE NANME Engineering COLLEGE CODE EEN Swansea University 2019-09-08T17:45:58.5465730 2015-12-14T17:15:27.2495688 College of Engineering Engineering R.S. Ransing 1 R.S. Batbooti 2 C. Giannetti 3 M.R. Ransing 4 Rajesh Ransing 0000-0003-4848-4545 5 Cinzia Giannetti 0000-0003-0339-5872 6 0025053-22022016143355.pdf RansingAQualityCorrelationAlgorithm2015AM.pdf 2016-02-22T14:33:55.3830000 Output 1364143 application/pdf Accepted Manuscript true 2017-06-18T00:00:00.0000000 true
title A quality correlation algorithm for tolerance synthesis in manufacturing operations
spellingShingle A quality correlation algorithm for tolerance synthesis in manufacturing operations
Rajesh, Ransing
Cinzia, Giannetti
title_short A quality correlation algorithm for tolerance synthesis in manufacturing operations
title_full A quality correlation algorithm for tolerance synthesis in manufacturing operations
title_fullStr A quality correlation algorithm for tolerance synthesis in manufacturing operations
title_full_unstemmed A quality correlation algorithm for tolerance synthesis in manufacturing operations
title_sort A quality correlation algorithm for tolerance synthesis in manufacturing operations
author_id_str_mv 0136f9a20abec3819b54088d9647c39f
a8d947a38cb58a8d2dfe6f50cb7eb1c6
author_id_fullname_str_mv 0136f9a20abec3819b54088d9647c39f_***_Rajesh, Ransing
a8d947a38cb58a8d2dfe6f50cb7eb1c6_***_Cinzia, Giannetti
author Rajesh, Ransing
Cinzia, Giannetti
format Journal article
container_title Computers & Industrial Engineering
container_volume 93
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publishDate 2016
institution Swansea University
issn 0360-8352
doi_str_mv 10.1016/j.cie.2015.12.008
college_str College of Engineering
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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 The clause 6.1 of the ISO9001:2015 quality standard requires organisations to take specific actions to determine and address risks and opportunities in order to minimize undesired effects in the process and achieve process improvement. This paper proposes a new quality correlation algorithm to optimise tolerance limits of process variables across multiple processes. The algorithm uses reduced p-dimensional principal component scores to determine optimal tolerance limits and also embeds ISO9001:2015’s risk based thinking approach. The corresponding factor and response variable pairs are chosen by analysing the mixed data set formulation proposed by Giannetti etl al. (2014) and co-linearity index algorithm proposed by Ransing et al. (2013). The goal of this tolerance limit optimisation problem is to make several small changes to the process in order to reduce undesired process variation. The optimal and avoid ranges of multiple process parameters are determined by analysing in-process data on categorical as well as continuous variables and process responses being transformed using the risk based thinking approach. The proposed approach has been illustrated by analysing in-process chemistry data for a nickel based alloy for manufacturing cast components for an aerospace foundry. It is also demonstrated how the approach embeds the risk based thinking into the in-process quality improvement process as required by the ISO9001:2015 standard.
published_date 2016-03-01T03:40:56Z
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