Advanced Manufacturing Configuration by Sample-efficient Batch Bayesian Optimization

TL;DR

This paper introduces a Bayesian optimization framework tailored for advanced manufacturing processes, enabling efficient configuration of costly-to-evaluate methods through a novel acquisition function and process information integration.

Contribution

It presents a unified Bayesian optimization framework with a new acquisition function, applied to real manufacturing processes, improving efficiency and cost-effectiveness.

Findings

The framework effectively finds optimal process parameters.

The novel acquisition function outperforms existing methods.

Application to real manufacturing processes demonstrates practical benefits.

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.