Advanced Manufacturing Configuration by Sample-efficient Batch Bayesian Optimization

Abstract

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.

Accepted for IEEE RA-L. 8 pages, 6 figures. arXiv admin note: text overlap with arXiv:2103.13881

References in corpus (2)

• Self-Optimizing Grinding Machines using Gaussian Process Models and Constrained Bayesian Optimization
• Plasma Spray Process Parameters Configuration using Sample-efficient Batch Bayesian Optimization

Cited by in corpus (1)

• Guided Bayesian Optimization: Data-Efficient Controller Tuning with Digital Twin