Process Diagnostics and Control in Thermal Spray

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.

Automated summary

This perspective paper summarizes the authors' view on the role of process diagnostics and control in thermal spray processes, and describes the current situation in terms of control strategies and sensors. The authors believe that through the use of models, sensors, and machine learning tools, it will be possible to develop coatings with specific target characteristics with fewer experiments, and implement an efficient control strategy for high reproducibility and reliability. The identification of existing knowledge gaps helps to guide future research needs.