@article{WANG2026, 
author = {Yihan WANG and Kaili FENG and Zhaohui HUANG and Bingxue CHENG and Tianmin SHAO},
title = {Progress in high-temperature metal-matrix friction-reducing and wear-resistant composite coatings},
year = {2026},
journal = {Journal of Aeronautical Materials},
volume = {46},
number = {8},
pages = {41-57},
keywords = {metal-matrix composite coating, high temperature, friction-reducing and wear-resistance, tribological performance, friction and wear mechanism},
url = {https://www.sciopen.com/article/10.11868/j.issn.1005-5053.2026.000097},
doi = {10.11868/j.issn.1005-5053.2026.000097},
abstract = {Fabricating metal-matrix high-temperature friction-reducing and wear-resistant composite coatings on the surface of tribo-pairs represents an effective strategy to improve the service life, stability and reliability of mechanical components under high-temperature conditions, and has thus received extensive attention in the aerospace industry. This article provides a review of the advancements in metal-matrix high-temperature friction-reducing and wear-resistant composite coatings, with emphasis on their performance requirements, compositional and structural design, typical coating systems, friction and wear mechanisms, and optimization methods. The coefficients of friction and wear rates of typical coatings across a temperature range from room temperature to 1000 ℃ are summarized and analyzed, and the evolution of their wear mechanisms with increasing temperature is concluded: at low temperatures, abrasive grooving and adhesive transfer dominate; as temperature increases, the mechanism gradually shifts to oxidative delamination and the formation of a tribo-oxide layer. Currently, the design and optimization of the coatings mainly depend on empirical design and trial-and-error method through adjusting the compositions of hard phases and lubricating phases. In recent years, data-driven machine learning methods have emerged as a new direction for coating composition design and performance optimization.}
}