Calibration of thermal spray microstructure simulations using Bayesian optimization

Thermal spray deposition is an inherently stochastic manufacturing process used for generating thick coatings of metals, ceramics and composites. The generated coatings exhibit hierarchically complex internal structures that affect the overall properties of the coating. The deposition process can be adequately simulated using rules-based process simulations. Nevertheless, in order for the simulation to accurately model particle spreading upon deposition, a set of predefined rules and parameters need to be calibrated to the specific material and processing conditions of interest. The calibration process is not trivial given the fact that many parameters do not correspond directly to experimentally measurable quantities. This work presents a protocol that automatically calibrates the parameters and rules of a given simulation in order to generate the synthetic microstructures with the closest statistics to an experimentally generated coating. Specifically, this work developed a protocol for tantalum coatings prepared using air plasma spray. The protocol starts by quantifying the internal structure using 2-point statistics and then representing it in a low-dimensional space using Principal Component Analysis. Subsequently, our protocol leverages Bayesian optimization to determine the parameters that yield the minimum distance between synthetic microstructure and the experimental coating in the low-dimensional space.