Advanced Manufacturing Configuration by Sample-efficient Batch Bayesian Optimization
This work addresses the challenge of optimizing costly manufacturing setups for industries, but it appears incremental as it builds on existing Bayesian optimization methods with specific adaptations.
The paper tackles the problem of configuring expensive advanced manufacturing processes by proposing a Bayesian optimization framework with a tailored acquisition function and parallel acquisition, applied to atmospheric plasma spraying and fused deposition modeling, demonstrating efficient parameter finding and cost minimization.
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.