Predicting product quality in continuous manufacturing processes using a scalable robust Gaussian Process approach

Echeverria-Rios, Diego and Green, Peter L ORCID: 0000-0002-6279-3769 (2024) Predicting product quality in continuous manufacturing processes using a scalable robust Gaussian Process approach. Engineering Applications of Artificial Intelligence, 127. p. 107233.

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Official URL: http://dx.doi.org/10.1016/j.engappai.2023.107233

Abstract

This work describes an Artificial Intelligence (AI)-based solution that predicts product quality when applied to a continuous manufacturing process. The proposed solution uses process parameters and product quality measurements that are obtained from a production line. The work detailed herein is problem-driven, showing an application within one of the UK's foundation industries and identifying five key criteria an AI solution should ideally satisfy in continuous manufacturing applications; scalability, modularity, stable out-of-data performance, uncertainty quantification and robustness to unrepresentative data. The shortcomings, relative to these five criteria, of available AI approaches are discussed before a potential solution is presented. The proposed approach involves the application of a generalised product-of-expert Gaussian process whose noise model is constructed from a Dirichlet process. The ability of the model to fulfil the five key criteria and its performance when applied to the foundation industry case study is demonstrated.

Item Type:	Article
Uncontrolled Keywords:	9 Industry, Innovation and Infrastructure
Divisions:	Faculty of Science and Engineering > School of Engineering
Depositing User:	Symplectic Admin
Date Deposited:	16 Oct 2023 13:05
Last Modified:	17 Mar 2024 18:39
DOI:	10.1016/j.engappai.2023.107233
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3173760