Prediction of Particle Properties in Plasma Spraying based on Machine Learning

Thermal Spraying processes include complex non-linear interdependencies among process parameters, in-flight particle properties and coating structure. Therefore, employing computer-aided methods is essential to quantify these complex relationships and subsequently enhance the process reproducibility. Typically, classic modeling approaches are pursued to understand these interactions. While these approaches are able to capture very complex systems, the increasingly sophisticated models have the drawback of requiring considerable calculation time. In this study, two different Machine Learning (ML) methods, residual neural networks (RNN) and support vector machines (SVM), are used to estimate the in-flight particle properties in plasma spraying in a much faster manner. To this end, the datasets of the process parameters such as electrical current and gas flow as well as the particle sizes, velocities and temperatures have been extracted from a CFD simulation of the plasma jet. Furthermore, a Design of Experiments approach has been conducted to cover a set of representative process parameters for training the ML models. The results show that the developed ML models are able to estimate the trends of particle properties precisely and dramatically faster than the computation-intensive CFD simulations.

Keywords: Atmospheric plasma spraying, Machine Learning, CFD simulation, Computational speed-up, Design of Experiments, In-flight particle properties