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arXiv · 2103.13881

Plasma Spray Process Parameters Configuration using Sample-efficient Batch Bayesian Optimization

Xavier Guidetti·Alisa Rupenyan·Lutz Fassl·Majid Nabavi·John Lygeros
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Abstract

Recent work has shown constrained Bayesian optimization to be a powerful technique for the optimization of industrial processes. In complex manufacturing processes, the possibility to run extensive sequences of experiments with the goal of finding good process parameters is severely limited by the time required for quality evaluation of the produced parts. To accelerate the process parameter optimization, we introduce a parallel acquisition procedure tailored on the process characteristics. We further propose an algorithm that adapts to equipment status to improve run-to-run reproducibility. We validate our optimization method numerically and experimentally, and demonstrate that it can efficiently find input parameters that produce the desired outcome and minimize the process cost.

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BibTeXRIS

Xavier Guidetti, Alisa Rupenyan, Lutz Fassl, Majid Nabavi, John Lygeros. 2021-03-25. Plasma Spray Process Parameters Configuration using Sample-efficient Batch Bayesian Optimization. https://doi.org/10.1109/case49439.2021.9551499

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