  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.
