Neural computing for erosion assessment in Al-20TiO 2 HVOF thermal spray coating

Stainless steel (SS) 316L is widely used for hydraulic machinery of ash disposal slurry pumps. In this work, the Al-20TiO 2 coating powders were sprayed on SS316L materials using the HVOF technique. The various properties such as erosion resistance, microhardness, microstructure, roughness, etc. were tested during the experiments. A pot tester was used to examine the rate of erosion. At an impact angle of 60 degrees, Al-20TiO 2 coatings were found to erode the most. The neural computing was performed by using the artificial neural network model (ANN). The present ANN model produced the Pearson coefficient (R) of 0.99903, 0.99301, and 0.99194 respectively for training, validation, and testing. The overall R-value of the model was found as 0.99686. Microscopically, Al-20TiO 2 demonstrated semi-brittle erosion behavior. Craters and ductile fractures were the most common erosion wear mechanisms detected on the Al-20TiO 2 coating, indicating that this material had semi-ductile properties.