  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. \cmtb{The novel acquisition function is
demonstrated, analyzed and compared on state-of-the-art benchmarking problems.
We apply the optimization approach to atmospheric plasma spraying and fused
deposition modeling.} Our results demonstrate that the proposed framework can
efficiently find input parameters that produce the desired outcome and minimize
the process cost.
