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Prediction of Wear Rate for Aluminium-Based Nano and Hybrid Nano-Composites Using Machine Learning

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Abstract:

Aluminium based MMNCs have gained significant traction across various industries due to their superior stiffness, strength-to-weight ratio, and enhanced mechanical and tribological properties. Despite extensive research in this field, the application of ML techniques to predict the properties of these materials remains limited. Present work aims to predict the wear rate of A-MMNCs based on their chemical compositions. The nanocomposites were fabricated using ultrasonic assisted stir casting method and studied their wear results. Classification models achieved an accuracy of 0.92 with SVM, 0.95 with KNN, and 0.97 with ANN. Additionally, prediction models for wear rate yielded R² values of 0.8876 with linear regression and 0.9165 with ANN, with minimal MAE for the ANN model. Genetic algorithms were employed to optimize wear test parameters.

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Periodical:

Materials Science Forum (Volume 1168)

Pages:

65-70

DOI:

https://doi.org/10.4028/p-3gzKRF

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Online since:

November 2025

Authors:

Vamsi Pasam Krishna*, Talari Guru Sai Nandan, Vipin Kumar Verma, Sri Vidhya Komarina

Keywords:

Aluminium-Metal Matrix Nanocomposites (A-MMNCs), Artificial Neural Network, Machine Learning, Wear Rate

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© 2025 Trans Tech Publications Ltd. All Rights Reserved

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