AI-Driven Surface Engineering

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Surface engineering has progressed far beyond its original purpose of preventing the wear, corrosion, and environmental degradation of materials. It has emerged as a major force of innovation in aerospace, automotive, biomedical, energy, and electronics sectors. Carburizing, electroplating, thermal spraying, physical vapor deposition, and chemical vapor deposition were among the early methods developed to enhance hardness, corrosion resistance, wear performance, and biocompatibility. However, in recent years, the emphasis has not been on mere protection of surfaces but instead on designing surfaces with customized functions, such as reducing friction, enhancing biocompatibility, or providing catalytic activity for advanced applications. Modern industrial demands call for materials capable of operating under harsh conditions, with the ability to maintain performance accuracy at microscopic and nanoscopic levels. Traditional experimental and computational methods are time-consuming, costly, and incapable of appropriately capturing the complexity of surface phenomena. Surface engineering is being transformed by artificial intelligence (AI) approaches (including machine learning and deep learning), which can rapidly analyze large volumes of data, identify correlations between processing conditions and material behavior, and predict the performance of a coating with high precision. In addition to conventional experimental and computational methods, AI can be used to optimize the processes more rapidly, identify defects at an early stage, and provide the control in real time throughout the manufacturing process. This chapter explores how AI speeds up discovery, improves predictive modeling, optimizes surface modification strategies, and leads to smarter and efficient and sustainable technologies.

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