Recent work has shown constrained Bayesian optimization to be a powerful technique for the optimization of industrial processes. We adapt this framework to the set-up and optimization of atmospheric plasma spraying processes. We propose and validate a Gaussian process modeling structure to predict coatings properties. We introduce a parallel acquisition procedure tailored on the process characteristics and propose an algorithm that adapts to real-time process measurements to improve reproducibility. We validate our optimization method numerically and experimentally, and demonstrate that it can efficiently find input parameters that produce the desired coating and minimize the process cost.

翻译：最近的工作显示,受限制的贝叶斯优化是优化工业流程的强大技术。我们根据大气等离子喷洒流程的设置和优化调整这一框架。我们提议并验证高斯进程模型结构,以预测涂层特性。我们引入了一种针对流程特性的平行获取程序,并提出了一种适应实时流程测量的算法,以改进再复制。我们从数字和实验角度验证了我们的优化方法,并证明它能够有效地找到能够产生所需涂层和尽量减少流程成本的投入参数。