Investigation of wear behaviour of magnesium reinforced with boron nitride nanocomposite using ANN

The present study aims to the wear behaviour of Mg reinforced with boron nitride nanocomposite. dry sliding (0.5 wt.%) is reviewed by following ASTM standards G99, i.e., on pin-on-disk test apparatus. Three parameters, namely load, speed, and distance, were considered in this study. experiments for rate have been conducted as per G99. obtained predicted ANN toolbox Matlab R2021a using Levenberg-Marquardt (trainlm) algorithm, which trains feed-forward neural network having 3-5-1 (three input neurons, five hidden neurons single layer one output neuron). Experimental data sets from utilized develop ANN. results concluded that error loss lies within 20%, an average percentage 2.6% between experimental values values.