Wear Behavior Prediction of Mg/2.5wt.%BN Composite Using Artificial Neural Networks in MATLAB

Abstract:

The research aims to examine the wear characteristics of a Magnesium (Mg) and Boron Nitride (BN) nanocomposite. Mg reinforced with 2.5 weight percent BN exhibits dry sliding wear characteristics. This study investigates these characteristics using the pin-on-disk wear testing apparatus outlined in the American Society for Testing and Materials (ASTM) standard G99. This study examined wear factors such as load, Sliding Velocity (SV), and Sliding Distance (SD). The wear rate assessments were performed according to the specifications outlined in ASTM Standard G99. The Levenberg-Marquardt (trainlm) algorithm, within MATLAB R2021a's Artificial Neural Network (ANN) Toolbox, estimates a wear rate for Mg reinforced with BN (2.5 wt.%). This algorithm's feed-forward neural network training employs a 3-5-1 architecture, with 3 input neurons, 5 hidden neurons within a single hidden layer, and 1 output neuron. ANNs were developed using experimental data from the pin-on-disk wear test. The average percentage discrepancy between the experimental data and the predicted values from the ANN was 3.49%, indicating that the inaccuracy in wear loss prediction for Mg reinforced with BN (2.5 wt.%) is 15.58%.

• Periodical: Materials Science Forum (Volume 1196)
• Pages: 3-10
• DOI: https://doi.org/10.4028/p-I2opny
• Online since: June 2026
• Authors: S. C. Ramesh Kumar*, R. Vara Prasad Kaviti, L. Mahesh
• Keywords: Artificial Neural Network, Levenberg-Marquardt Algorithm, MATLAB, Mg/BN Nanocomposite, Wear Rate

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