Abstract
The thermal spray coatings are commonly employed in slurry pump components and hydrodynamic turbine blades, where wear progression is an intricate phenomenon. In this research work, the performance analysis of HVOF and APS sprayed WC20Cr3C27Ni coatings for slurry erosion wear is carried out by using artificial neural networks (ANN). The influence of time, particle size, impact angle, speed, and slurry concentration on wear performance of coatings and turbine steel substrate are evaluated. Under the experimental settings, slurry erosion wear rates and mass loss for both coatings and substrate were determined. When ASTM A743 steel was coated with thermal sprayed WC20Cr3C27Ni coatings, the slurry erosion wear resistance of the steel was enhanced by 2 and 3.5 times for APS and HVOF coatings, respectively. The design of ANN made it possible to examine the interactions between the seven input variables. A robust model was formed by the two outputs that followed. This model enables the prediction of slurry erosion wear rate and mass loss of WC20Cr3C27Ni coatings and substrate.
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