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

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Authors

  • R Vara Prasad Kaviti
     ,IN
  • D. Jeyasimman
     ,IN
  • Ramesh Kumar S C
     ,IN
  • Mohan Babu BM
     ,IN

DOI:

https://doi.org/10.18311/jmmf/2021/30101

Keywords:

Mg nanocomposite, wear rate, and (ANN) artificial neural network.

Abstract

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

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Published

2022-04-28

How to Cite

Kaviti, R. V. P., Jeyasimman, D., Kumar S C, R., & Babu BM, M. (2022). Investigation of wear behaviour of magnesium reinforced with boron nitride nanocomposite using ANN. Journal of Mines, Metals and Fuels, 69(12A), 190–194. https://doi.org/10.18311/jmmf/2021/30101

Issue

Volume 69, Issue 12A, December 2021

Section

Articles
Crossref
Scopus
Google Scholar
Europe PMC
Received 2022-04-27
Accepted 2022-04-27
Published 2022-04-28

 

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