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.

翻译：我们根据贝叶斯优化提出一个设计和运作昂贵的先进制造方法的框架。该框架统一了定制的获取功能、平行的获取程序以及流程信息整合,为优化程序提供了背景。新的获取功能在基准示例问题上得到了展示和分析。我们在模拟和实验中应用了大气等离子喷洒的最佳方法。我们的结果表明,拟议的框架可以有效地找到产生预期结果和最大限度地降低流程成本的投入参数。