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

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

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

ZAVOIANU, 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