Additive manufacturing (AM) has emerged as a transformative manufacturing technology for producing complex components with unprecedented design flexibility. However, the widespread application of AM parts in tribological environments is often limited by inherent defects such as high surface roughness, porosity, residual stresses, anisotropy, and weak interlayer bonding, which adversely affect friction, wear resistance, and tribocorrosion performance. This review critically examines the tribological behavior of AM materials and components, emphasizing the influence of processing routes, material selection, secondary reinforcing phases, and microstructural evolution on tribological performance. Particular attention is given to surface engineering strategies, including thermal spray coatings, laser surface treatments, plasma electrolytic oxidation, vapor deposition technologies, and mechanical surface modification techniques for mitigating AM-induced defects and improving surface durability. Recent advances in machine learning (ML) and artificial intelligence (AI) for wear prediction, process optimization, and intelligent tribological monitoring are also discussed. The review highlights the relationships among manufacturing parameters, surface integrity, and wear mechanisms, while identifying key challenges associated with process variability, long-term reliability, and industrial implementation. Future research should focus on multifunctional surface systems, smart coatings, real-time condition monitoring, and data-driven design approaches to accelerate the deployment of tribologically optimized AM components in aerospace, biomedical, automotive, and energy applications.
Verma et al. (Thu,) studied this question.
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