  Recent work has shown constrained Bayesian optimization to be a powerful
technique for the optimization of industrial processes. In complex
manufacturing processes, the possibility to run extensive sequences of
experiments with the goal of finding good process parameters is severely
limited by the time required for quality evaluation of the produced parts. To
accelerate the process parameter optimization, we introduce a parallel
acquisition procedure tailored on the process characteristics. We further
propose an algorithm that adapts to equipment status to improve run-to-run
reproducibility. We validate our optimization method numerically and
experimentally, and demonstrate that it can efficiently find input parameters
that produce the desired outcome and minimize the process cost.
