This study thoroughly assesses tribo-corrosion (TC) maps as crucial instruments for choosing high-performance thermally sprayed coatings (TSC) made of cemented carbide (CC). These coatings are widely used in the chemical, oil & gas, and transportation industries. This article examines the essential sintering process that affects the mechanical characteristics and microstructure of the coatings. Additionally, it investigates how surface texturing–via micro-dimples or grooves–may strengthen adhesion and serve as lubricant reservoirs to improve wear resistance. The review stresses the idea of synergism–the idea that wear and corrosion can have a combined negative impact that is greater than the sum of their individual effects–and critically examines the mechanisms of corrosion that can weaken the metallic binder. Lastly, the review delves into the reported possibilities of Machine Learning (ML) to transform the process of creating tribo-corrosion maps. The development of next-generation thermally sprayed coatings can be accelerated by using machine learning (ML) algorithms to estimate tribo-corrosion performance for untested settings by analyzing large datasets on coating composition, processing factors, and wear behaviors.
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