Conference Paper/Proceeding/Abstract 818 views
A framework for improving process robustness with quantification of uncertainties in Industry 4.0
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
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...
|Published in:||INnovations in Intelligent SysTems and Applications (INISTA), 2017 IEEE International Conference on|
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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.
Faculty of Science and Engineering