Prediction of wear loss quantities of ferro-alloy coating using different machine learning algorithms
surface coating, plasma transfer arc (PTA) welding, wear, prediction, machine learning algorithms
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.
Tsinghua University Press
Osman ALTAY, Turan GURGENC, Mustafa ULAS et al. Prediction of wear loss quantities of ferro-alloy coating using different machine learning algorithms. Friction 2020, 8(1): 107-114.