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.
翻译:我们提议了一个基于贝叶斯优化的昂贵到评估的先进制造方法配置和运作框架。该框架统一了定制的购置功能、平行的购置程序以及流程信息整合,为优化程序提供了背景。\cmtb{新的购置功能在最先进的基准问题上得到了演示、分析和比较。我们对大气等离子喷洒和引信沉积模型采用了优化方法。}我们的结果显示,拟议框架能够有效地找到能够产生预期结果和尽量减少流程成本的投入参数。