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A framework for improving process robustness with quantification of uncertainties in Industry 4.0 / Cinzia, Giannetti

INnovations in Intelligent SysTems and Applications (INISTA), 2017 IEEE International Conference on, Pages: 189 - 194

Swansea University Author: Cinzia, Giannetti

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DOI (Published version): 10.1109/INISTA.2017.8001155

Abstract

Digitalisation of industrial processes, also called the fourth industrial revolution, is leading to availability of large volume of data containing measurements of many process variables. This offers new opportunities to gain deeper insights on process variability and its effects on quality and perf...

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Published in: INnovations in Intelligent SysTems and Applications (INISTA), 2017 IEEE International Conference on
ISBN: 978-1-5090-5795-5
Published: 2017
URI: https://cronfa.swan.ac.uk/Record/cronfa38821
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spelling 2018-03-02T10:17:21.8887770 v2 38821 2018-02-20 A framework for improving process robustness with quantification of uncertainties in Industry 4.0 a8d947a38cb58a8d2dfe6f50cb7eb1c6 0000-0003-0339-5872 Cinzia Giannetti Cinzia Giannetti true false 2018-02-20 EEN Digitalisation of industrial processes, also called the fourth industrial revolution, is leading to availability of large volume of data containing measurements of many process variables. This offers new opportunities to gain deeper insights on process variability and its effects on quality and performance. Manufacturing facilities already use data driven approaches to study process variability and find improvement opportunities through methodologies such as Design of Experiment (DOE) and Six Sigma. However, current approaches are not adequate to model the complexity of modern manufacturing systems, especially when these systems exhibit non-linear interactions between high numbers of variables. In this paper a methodology to improve process robustness is proposed. This methodology uses non-parametric estimation of quantiles of response to discover new tolerance limits of factors. This method does not make any stringent assumption of linearity and works well in finding the interactions effects of covariates on response quantiles. Process robustness, which is defined as the ability of a process to have acceptable quality whilst tolerating variability of the input, is measured through calculation of Likelihood Ratios (LR) associated to the new tolerance limits. Uncertainty of this estimation is quantified via simulations using the bootstrapping method. The novel contribution of this paper is the application of quantile regression and likelihood ratios to the tolerance synthesis problem applied to a low alloy foundry. It shows the validity of the methodology in modelling behaviours of complex manufacturing processes using data driven approaches to gain new insights on causes of process variabilities and discover new product specific process knowledge. This work contributes to bridging the gap between theory and application towards implementing Industry 4.0 predictive analytics. Conference Paper/Proceeding/Abstract INnovations in Intelligent SysTems and Applications (INISTA), 2017 IEEE International Conference on 189 194 978-1-5090-5795-5 1 1 2017 2017-01-01 10.1109/INISTA.2017.8001155 COLLEGE NANME Engineering COLLEGE CODE EEN Swansea University 2018-03-02T10:17:21.8887770 2018-02-20T21:05:43.1874478 College of Engineering Engineering Cinzia Giannetti 0000-0003-0339-5872 1
title A framework for improving process robustness with quantification of uncertainties in Industry 4.0
spellingShingle A framework for improving process robustness with quantification of uncertainties in Industry 4.0
Cinzia, Giannetti
title_short A framework for improving process robustness with quantification of uncertainties in Industry 4.0
title_full A framework for improving process robustness with quantification of uncertainties in Industry 4.0
title_fullStr A framework for improving process robustness with quantification of uncertainties in Industry 4.0
title_full_unstemmed A framework for improving process robustness with quantification of uncertainties in Industry 4.0
title_sort A framework for improving process robustness with quantification of uncertainties in Industry 4.0
author_id_str_mv a8d947a38cb58a8d2dfe6f50cb7eb1c6
author_id_fullname_str_mv a8d947a38cb58a8d2dfe6f50cb7eb1c6_***_Cinzia, Giannetti
author Cinzia, Giannetti
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description Digitalisation of industrial processes, also called the fourth industrial revolution, is leading to availability of large volume of data containing measurements of many process variables. This offers new opportunities to gain deeper insights on process variability and its effects on quality and performance. Manufacturing facilities already use data driven approaches to study process variability and find improvement opportunities through methodologies such as Design of Experiment (DOE) and Six Sigma. However, current approaches are not adequate to model the complexity of modern manufacturing systems, especially when these systems exhibit non-linear interactions between high numbers of variables. In this paper a methodology to improve process robustness is proposed. This methodology uses non-parametric estimation of quantiles of response to discover new tolerance limits of factors. This method does not make any stringent assumption of linearity and works well in finding the interactions effects of covariates on response quantiles. Process robustness, which is defined as the ability of a process to have acceptable quality whilst tolerating variability of the input, is measured through calculation of Likelihood Ratios (LR) associated to the new tolerance limits. Uncertainty of this estimation is quantified via simulations using the bootstrapping method. The novel contribution of this paper is the application of quantile regression and likelihood ratios to the tolerance synthesis problem applied to a low alloy foundry. It shows the validity of the methodology in modelling behaviours of complex manufacturing processes using data driven approaches to gain new insights on causes of process variabilities and discover new product specific process knowledge. This work contributes to bridging the gap between theory and application towards implementing Industry 4.0 predictive analytics.
published_date 2017-01-01T03:57:57Z
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