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. The novel acquisition function is demonstrated and analyzed on benchmark illustrative problems. We apply the optimization approach to atmospheric plasma spraying in simulation and experiments. Our results demonstrate that the proposed framework can efficiently find input parameters that produce the desired outcome and minimize the process cost.