SUPPORT VECTOR MACHINE LEARNING ON SLURRY EROSION CHARACTERISTICS ANALYSIS OF Ni- AND Co-ALLOY COATINGS

Abstract

In this paper, a novel support vector machine (SVM) learning approach has been used to predict the silt erosion of coatings sprayed by the high-velocity oxy-fuel (HVOF) process. The silt erosion resistance of HVOF-coated Co- and Ni-alloys is studied using an erosion tester. Both of these spraying powders were deposited on the 18Cr-12Ni-2Mo austenitic steel substrate. For the erosion experiments, the fixtures were developed to test the erosion characteristics at different impingement conditions (i.e. [Formula: see text], [Formula: see text], and [Formula: see text]). The image processing technique enables an understanding of the erosion mechanisms of coatings. The support vector regression is more effective than the traditional regression models. The proposed model employs support vector regression with parameter tuning. The ideal parameters are selected with the help of the grid search method. The model is trained by supplying 70% data and validated with 15% data. Afterward, the remaining 15% is given to the prepared model as testing data. The dataset obtained is employed for building an effective support vector regression model. The predicted results are compared against the original experimental values. The developed model is also compared with other state-of-the-art machine learning techniques for showing the preciseness of the proposed model. The Ni-based HVOF coating showed superior performance ([Formula: see text]) better than the Co-based HVOF coating under similar experimental conditions. An SVM model was successfully developed to predict the erosion of coated and uncoated 18Cr-12Ni-2Mo austenitic steel. The precision performance of the SVM model was calculated in terms of [Formula: see text], RMSE, [Formula: see text], and MAE as 0.99, [Formula: see text], 0.99, and [Formula: see text], respectively.