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A soft-computing framework for automated optimization of multiple product quality criteria with application to micro-fluidic chip production.

Zăvoianu, Alexandru-Ciprian; Lughofer, Edwin; Pollak, Robert; Eitzinger, Christian; Radauer, Thomas

Authors

Edwin Lughofer

Robert Pollak

Christian Eitzinger

Thomas Radauer



Abstract

We describe a general strategy for optimizing the quality of products of industrial batch processes that comprise multiple production stages. We focus on the particularities of applying this strategy in the field of micro-fluidic chip production. Our approach is based on three interacting components: (i) a new hybrid design of experiments (DoE) strategy that combines expert- and distribution-based space exploration with model-based uncertainty criteria to obtain a representative set of initial samples (i.e., settings of essential machining process parameters), (ii) construction of linear and non-linear predictive mappings from these samples to describe the relation between machining process parameters and resulting quality control (QC) values and (iii) incorporation of these mappings as surrogate fitness estimators into a multi-objective optimization process to discover settings that outperform those routinely used by operators. These optimized settings lead to final products with better quality and/or higher functionality for the clients. The optimization module employs a co-evolutionary strategy we developed that is able to deliver better Pareto non-dominated solutions than the renowned NSGA-II multi-objective solver. We applied our proposed high-level surrogate-based multi-objective strategy both in a single/late-stage optimization scenario and in a more challenging multi-stage scenario, yielding final optimization results that improved parameter settings and thus product quality compared to standard expert-based production process parameterizations.

Citation

ZĂVOIANU, A.-C., LUGHOFER, E., POLLAK, R., EITZINGER, C. and RADAUER, T. 2021. A soft-computing framework for automated optimization of multiple product quality criteria with application to micro-fluidic chip production. Applied soft computing [online], 98, article ID 106827. Available from: https://doi.org/10.1016/j.asoc.2020.106827

Journal Article Type Article
Acceptance Date Oct 19, 2020
Online Publication Date Oct 22, 2020
Publication Date Jan 31, 2021
Deposit Date Oct 23, 2020
Publicly Available Date Oct 23, 2021
Journal Applied soft computing
Print ISSN 1568-4946
Electronic ISSN 1872-9681
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 98
Article Number 106827
DOI https://doi.org/10.1016/j.asoc.2020.106827
Keywords Single- and multi-stage process optimization; Evolutionary multi-objective optimization; Hybrid design of experiments; Surrogate modeling; Process parameters; Production quality criteria; Micro-fluidic chips
Public URL https://rgu-repository.worktribe.com/output/977857

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