A framework for improving process robustness with quantification of uncertainties in Industry 4.0

C. Giannetti
{"title":"A framework for improving process robustness with quantification of uncertainties in Industry 4.0","authors":"C. Giannetti","doi":"10.1109/INISTA.2017.8001155","DOIUrl":null,"url":null,"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 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.","PeriodicalId":314687,"journal":{"name":"2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INISTA.2017.8001155","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

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 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.
工业4.0中通过量化不确定性提高过程鲁棒性的框架
工业过程的数字化,也被称为第四次工业革命,正在导致包含许多过程变量测量的大量数据的可用性。这为深入了解过程可变性及其对质量和性能的影响提供了新的机会。制造工厂已经使用数据驱动的方法来研究过程可变性,并通过实验设计(DOE)和六西格玛等方法找到改进机会。然而,目前的方法不足以模拟现代制造系统的复杂性,特别是当这些系统表现出大量变量之间的非线性相互作用时。本文提出了一种提高过程鲁棒性的方法。该方法使用响应分位数的非参数估计来发现新的因素容忍极限。该方法不作任何严格的线性假设,能很好地求出协变量对响应分位数的相互作用。过程稳健性,定义为过程在容忍输入变化的同时具有可接受质量的能力,通过计算与新公差限制相关的似然比(LR)来测量。利用自举法进行仿真,量化了该估计的不确定性。本文的新贡献是将分位数回归和似然比应用于低合金铸造厂的公差综合问题。它显示了使用数据驱动的方法在复杂制造过程建模行为的有效性,以获得对过程变量的原因的新见解,并发现新的产品特定的过程知识。这项工作有助于弥合理论和应用之间的差距,以实现工业4.0预测分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信