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

Autor:	Guidetti, Xavier, Rupenyan, Alisa, Fassl, Lutz, Nabavi, Majid, Lygeros, John
Rok vydání:	2021
Předmět:	Electrical Engineering and Systems Science - Systems and Control Computer Science - Robotics
Druh dokumentu:	Working Paper
DOI:	10.1109/CASE49439.2021.9551499
Popis:	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. Comment: Accepted for IEEE CASE 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2103.13881 Zobrazit plný text záznamu View this record from Arxiv