Plasma Spray Process Parameters Configuration using Sample-efficient Batch Bayesian Optimization
This work addresses process optimization for industrial manufacturing, but it appears incremental as it builds on existing constrained Bayesian optimization techniques.
The authors tackled the problem of optimizing plasma spray process parameters under constraints of limited experimental runs and quality evaluation time, and demonstrated that their method efficiently finds parameters that achieve desired outcomes and minimize costs.
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.