Artificial neural network model for wear characteristic analysis of WC-10Co4Cr and Stellite 6 thermal spray coatings

Abstract

In this paper, wear characteristic analysis of the thermal spray powders namely WC-10Co4Cr and Stellite 6 high velocity oxy-fuel (HVOF) coated on SS 316 L steel were studied. Artificial neural network (ANN) model predicts the accuracy by the coefficient of R= 0.99 and R2 = 0.98. The R2 of present ANN model was far better as compared to regression models Enbt (0.85), Coarse Tree (0.04), linear regression i.e. LR (0.37), and Enbag (0.19). Results show that uncoated SS 316 L steel was severely eroded, and wear occurred on the bare SS 316 L was about 7.5 ± 2.5 times higher as compared to WC-10Co4Cr coating, and 3.5 ± 1.5 times than Stellite 6 coating.