ABSTRACT
The surface engineering community extensively uses the thermal spraying (TS) technique to solve wear and corrosion-related issues. It is utilized to deposit the coatings of different materials, such as metals, ceramics, and cermets, on the substrate surface. New techniques and materials have been researched and invented in the past few decades. As a result, TS technology is constantly changing to satisfy the demands and challenges of the present and future markets of surface engineering. The evolution of tools, processes, and materials has been thoroughly chronicled over the years. However, the sector’s concomitant digital revolution is also taking place in a fragmented manner. Physical experimentation in TS is an expensive process. Finally, outcomes such as coating microstructures and mechanical properties also depend on environmental and process parameters used during TS. Moreover, monitoring and controlling TS operations is difficult since they involve many variables such as powder feed rates, stand-off distance, and gas flow rates. Data collection, process modeling, and machine learning technologies are now emerging as standard practices in the industries, allowing for better production-level spray process control and accurate prediction of coating behavior under aggressive conditions such as wear and corrosion. This introductory chapter promotes and enlightens new research that can ultimately hasten the digital transformation of the surface engineering industry by discussing the contribution of artificial intelligence toward achieving durable TS coatings.
