Artificial neural network prediction on wear properties of high vanadium high speed steel (HVHSS) rolls

Abstract

The present paper is dedicated to the application of artificial neural networks in the prediction of the wear properties of high vanadium high speed steel (HVHSS) rolls, including predictions of wear weight loss W according to carbon content C and number of revolutions N. Multilayer backpropagation networks were created and trained using comprehensive datasets tested by the authors. Very good performances of the neural networks were achieved. The prediction results show that the wear weight loss nearly linear increases with increasing number of revolutions at constant carbon content. The relative wear resistance of roll reaches the optimal value when the carbon content is ∼2·55 wt-%. The prediction values have sufficiently mined the basic domain knowledge of wear process of HVHSS rolls. A convenient and powerful method of optimising the process parameters of abrasive resistant materials has been provided by the authors.