Advanced Manufacturing Configuration by Sample-efficient Batch Bayesian Optimization

Autor:	Guidetti, Xavier, Rupenyan, Alisa, Fassl, Lutz, Nabavi, Majid, Lygeros, John
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Electrical Engineering and Systems Science - Systems and Control Statistics - Machine Learning
Zdroj:	IEEE Robotics and Automation Letters, 2022
Druh dokumentu:	Working Paper
DOI:	10.1109/LRA.2022.3208370
Popis:	We propose a framework for the configuration and operation of expensive-to-evaluate advanced manufacturing methods, based on Bayesian optimization. The framework unifies a tailored acquisition function, a parallel acquisition procedure, and the integration of process information providing context to the optimization procedure. \cmtb{The novel acquisition function is demonstrated, analyzed and compared on state-of-the-art benchmarking problems. We apply the optimization approach to atmospheric plasma spraying and fused deposition modeling.} Our results demonstrate that the proposed framework can efficiently find input parameters that produce the desired outcome and minimize the process cost. Comment: Accepted for IEEE RA-L. 8 pages, 6 figures. arXiv admin note: text overlap with arXiv:2103.13881
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2205.11827 Zobrazit plný text záznamu View this record from Arxiv