Neural Network Prediction of Slurry Erosion Wear of Ni-WC Coated Stainless Steel 420

Abstract

In the present study, Erosion wear of stainless steel 420 was predicted using an artificial neural network (ANN). Stainless steel 420 is used for making slurry transportation components, such as pump impellers and casings. The erosion wear performance was analyzed by using a slurry pot tester at the rotational speed of 600–1500 rpm with a time duration of 80–200 min. Fly ash was used as an erodent medium, and the solid concentration varied from 20 to 50%. The particle size of erodent selected for the erosion tests was <53 µm, 53–106 µm, 106–150 µm, 150–250 µm. A standard artificial neural network (ANN) for the prediction of erosion wear was designed using the MATLAB program. Erosion wear results obtained from experiments showed a good agreement with the ANN results. This technique helps in saving time and resources for a large number of experimental trials and successfully predicts the erosion wear rate of the coatings both within and beyond the experimental domain.