Process Diagnostics and Control in Thermal Spray

Abstract

This perspective paper summarizes the authors’ view on how process diagnostics and control can help to gain a deeper insight into thermal spray processes and to better understand the underlying mechanisms. The current situation in terms of available process control strategies and suitable sensors is described. In perspective, it is assumed that with suitable models, sensors and machine learning tools, it will be possible to perform a smaller number of experiments to develop coatings with specific target characteristics. In addition, trained machine learning tools can be used to implement an efficient control strategy to produce coatings with high reproducibility and reliability. The corresponding existing knowledge gaps are analyzed to identify needs for future research.