  In this study, the first-of-its-kind use of active learning (AL) framework in
thermal spray is adapted to improve the prediction accuracy of the in-flight
particle characteristics and uses Gaussian Process (GP) ML model as a surrogate
that generalises a global solution without necessarily involving physical
mechanisms. The AL framework via the Bayesian Optimisation was utilised to: (a)
reduce the maximum uncertainty in the given database and (b) reduce local
uncertainty around a contrived test point. The initial dataset consists of 26
atmospheric plasma spray (APS) parameters of silicon, aimed at ceramic matrix
composites (CMCs) for the next generation of aerospace applications. The
maximum uncertainty in the initial dataset was reduced by AL-driven
identification of search spaces and conducting six guided spray trails in the
identified search spaces. On average, a 52.9% improvement (error reduction) of
RMSE and an R2 increase of 8.5% were reported on the predicted in-flight
particle velocities and temperatures after the AL-driven optimisation.
Furthermore, the Bayesian Optimisation around a contrived test point to predict
the best possible characteristics resulted in a three-fold increase in
prediction accuracy as compared to the non-optimised prediction. These
AL-guided experimental validations not only increase the informativeness of the
limited dataset but is adaptable for other thermal spraying methods without
necessarily involving physical mechanisms and underlying mechanisms. The use of
AL-driven optimisation may drive the thermal spraying towards
resource-efficiency and may serve as the first step towards fully digital
thermal spraying environments.
