Active learning-driven uncertainty reduction for in-flight particle characteristics of atmospheric plasma spraying of silicon

The first-of-its-kind use of the active learning (AL) framework in thermal spray is adapted to enhance the pre-diction accuracy of the in-flight particle characteristics. The successful AL framework implementation via Bayesian Optimisation is beneficial in, first, reducing the maximum uncertainty, which greatly improves the prediction accuracy and informativeness of the existing database. Second, it reduces local uncertainty around a contrived test point that offers the capability to find improvement in a limited search area, allowing an accurate prediction around a desired test point. The dataset for Machine Learning (ML) training consists of 26 atmospheric plasma spray (APS) parameters of silicon and a further six AL-guided spray runs carried out to reduce maximum uncertainty in the initial database. 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 contrived test point optimisation to predict the best possible characteristics in a limited search space resulted in a three-fold increase in prediction accuracy compared to the non-optimised prediction. The AL-driven optimisation proved to be greatly beneficial for resource-intensive thermal spray-ing, as the framework not only allowed an accurate prediction of the in-flight particle characteristics but also found expected improvement around a desired in-flight characteristic. Furthermore, the framework uses the Gaussian Process (GP) ML model as a surrogate that generalises a global solution without necessarily involving physical and underlying mechanisms, thus extending the framework to other thermal spraying methods.

Automated summary

This study proposes the first use of the active learning framework in thermal spray to enhance the accuracy of in-flight particle characteristics prediction. By implementing Bayesian Optimization, the maximum uncertainty is reduced, significantly improving the prediction accuracy and informativeness of the existing database. The AL-driven optimization not only accurately predicts the particle characteristics but also finds expected improvements around desired in-flight characteristics.