Prediction of wear loss quantities of ferro-alloy coating using different machine learning algorithms
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In this study, experimental wear losses under different loads and sliding distances of AISI 1020 steel surfaces coated with (wt.%) 50FeCrC-20FeW-30FeB and 70FeCrC-30FeB powder mixtures by plasma transfer arc welding were determined. The dataset comprised 99 different wear amount measurements obtained experimentally in the laboratory. The linear regression (LR), support vector machine (SVM), and Gaussian process regression (GPR) algorithms are used for predicting wear quantities. A success rate of 0.93 was obtained from the LR algorithm and 0.96 from the SVM and GPR algorithms.
Keywordssurface coating plasma transfer arc (PTA) welding wear prediction machine learning algorithms
All the Matlab scripts of related algorithms in the article are coded ourselves. The used Matlab platform is licensed by Firat University.
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