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Risk based uncertainty quantification to improve robustness of manufacturing operations / Cinzia, Giannetti; Rajesh, Ransing

Computers & Industrial Engineering, Volume: 101, Pages: 70 - 80

Swansea University Authors: Cinzia, Giannetti, Rajesh, Ransing

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Abstract

The cyber-physical systems of Industry 4.0 are expected to generate vast amount of in-process data and revolutionise the way data, knowledge and wisdom is captured and reused in manufacturing industries. The goal is to increase profits by dramatically reducing the occurrence of unexpected process re...

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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/cronfa29528
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spelling 2018-01-19T14:02:30.8590344 v2 29528 2016-08-10 Risk based uncertainty quantification to improve robustness of manufacturing operations a8d947a38cb58a8d2dfe6f50cb7eb1c6 0000-0003-0339-5872 Cinzia Giannetti Cinzia Giannetti true false 0136f9a20abec3819b54088d9647c39f 0000-0003-4848-4545 Rajesh Ransing Rajesh Ransing true false 2016-08-10 EEN The cyber-physical systems of Industry 4.0 are expected to generate vast amount of in-process data and revolutionise the way data, knowledge and wisdom is captured and reused in manufacturing industries. The goal is to increase profits by dramatically reducing the occurrence of unexpected process results and waste. ISO9001:2015 defines risk as effect of uncertainty. In the 7Epsilon context, the risk is defined as effect of uncertainty on expected results. The paper proposes a novel algorithm to embed risk based thinking in quantifying uncertainty in manufacturing operations during the tolerance synthesis process. This method uses penalty functions to mathematically represent deviation from expected results and solves the tolerance synthesis problem by proposing a quantile regression tree approach. The latter involves non parametric estimation of conditional quantiles of a response variable from in-process data and allows process engineers to discover and visualise optimal ranges that are associated with quality improvements. In order to quantify uncertainty and predict process robustness, a probabilistic approach, based on the likelihood ratio test with bootstrapping, is proposed which uses smoothed probability estimation of conditional probabilities. The mathematical formulation presented in this paper will allow organisations to extend Six Sigma process improvement principles in the Industry 4.0 context and implement the 7 steps of 7Epsilon in order to satisfy the requirements of clauses 6.1 and 7.1.6 of the ISO9001:2015 and the aerospace AS9100:2016 quality standard. Journal Article Computers &amp; Industrial Engineering 101 70 80 0360-8352 30 11 2016 2016-11-30 10.1016/j.cie.2016.08.002 COLLEGE NANME Engineering COLLEGE CODE EEN Swansea University 2018-01-19T14:02:30.8590344 2016-08-10T14:01:43.3128938 Cinzia Giannetti 0000-0003-0339-5872 1 Rajesh Ransing 0000-0003-4848-4545 2 0029528-09092016150334.pdf giannetti2016(2).pdf 2016-09-09T15:03:34.7530000 Output 1211823 application/pdf Version of Record true 2016-09-09T00:00:00.0000000 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). true
title Risk based uncertainty quantification to improve robustness of manufacturing operations
spellingShingle Risk based uncertainty quantification to improve robustness of manufacturing operations
Cinzia, Giannetti
Rajesh, Ransing
title_short Risk based uncertainty quantification to improve robustness of manufacturing operations
title_full Risk based uncertainty quantification to improve robustness of manufacturing operations
title_fullStr Risk based uncertainty quantification to improve robustness of manufacturing operations
title_full_unstemmed Risk based uncertainty quantification to improve robustness of manufacturing operations
title_sort Risk based uncertainty quantification to improve robustness of manufacturing operations
author_id_str_mv a8d947a38cb58a8d2dfe6f50cb7eb1c6
0136f9a20abec3819b54088d9647c39f
author_id_fullname_str_mv a8d947a38cb58a8d2dfe6f50cb7eb1c6_***_Cinzia, Giannetti
0136f9a20abec3819b54088d9647c39f_***_Rajesh, Ransing
author Cinzia, Giannetti
Rajesh, Ransing
format Journal article
container_title Computers &amp; Industrial Engineering
container_volume 101
container_start_page 70
publishDate 2016
institution Swansea University
issn 0360-8352
doi_str_mv 10.1016/j.cie.2016.08.002
document_store_str 1
active_str 0
description The cyber-physical systems of Industry 4.0 are expected to generate vast amount of in-process data and revolutionise the way data, knowledge and wisdom is captured and reused in manufacturing industries. The goal is to increase profits by dramatically reducing the occurrence of unexpected process results and waste. ISO9001:2015 defines risk as effect of uncertainty. In the 7Epsilon context, the risk is defined as effect of uncertainty on expected results. The paper proposes a novel algorithm to embed risk based thinking in quantifying uncertainty in manufacturing operations during the tolerance synthesis process. This method uses penalty functions to mathematically represent deviation from expected results and solves the tolerance synthesis problem by proposing a quantile regression tree approach. The latter involves non parametric estimation of conditional quantiles of a response variable from in-process data and allows process engineers to discover and visualise optimal ranges that are associated with quality improvements. In order to quantify uncertainty and predict process robustness, a probabilistic approach, based on the likelihood ratio test with bootstrapping, is proposed which uses smoothed probability estimation of conditional probabilities. The mathematical formulation presented in this paper will allow organisations to extend Six Sigma process improvement principles in the Industry 4.0 context and implement the 7 steps of 7Epsilon in order to satisfy the requirements of clauses 6.1 and 7.1.6 of the ISO9001:2015 and the aerospace AS9100:2016 quality standard.
published_date 2016-11-30T13:46:18Z
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