Advancements in Thermal Spray Coating Techniques: A Review of Processes, Materials, and AI-driven Optimization

Abstract:

Thermal spray coating is a widely adopted surface engineering technique used to enhance component durability and performance in critical sectors such as power generation, aerospace, automotive, and oil and gas. This review systematically examines major thermal spray processes, including high-velocity oxy-fuel (HVOF) spraying, plasma spraying, cold spray, and suspension-based techniques, with emphasis on key process parameters and their influence on coating characteristics. The role of coating materials metallic, ceramic, cermet, and composite systems is discussed in relation to microstructure-property correlations governing performance attributes, including hardness (HV), bond strength (MPa), and porosity (%). Recent advancements in artificial intelligence (AI) and machine learning (ML) for process optimization are critically analyzed. Data-driven models, including artificial neural networks, support vector machines, and ensemble learning approaches, have demonstrated strong predictive capability for coating properties and process windows. Reported outcomes indicate reductions in coating porosity of approximately 30-50%, improvements in mechanical strength and wear resistance, and a significant reduction in the number of experimental trials required. Furthermore, integrating AI/ML with real-time process monitoring and multi-objective optimization frameworks is enabling the development of adaptive and intelligent thermal spray systems. These advancements provide a pathway toward data-driven coating design and improved process reliability, addressing the increasing demands of advanced industrial applications.