Study of Friction and Wear Behavior of Graphene-Reinforced AA7075 Nanocomposites by Machine Learning-Reference-Cited by-同舟云学术

Study of Friction and Wear Behavior of Graphene-Reinforced AA7075 Nanocomposites by Machine Learning

Published:2023-02-15 Issue: Volume:2023 Page:1-15
ISSN:1687-4129
Container-title:Journal of Nanomaterials
language:en
Short-container-title:Journal of Nanomaterials

Author:

Prasanth I. S. N. V. R.¹,Jeevanandam Prabahar²,Selvaraju P.³,Sathish K.⁴^ORCID,Hasane Ahammad S. K.⁵,Sujatha P.⁶,Kaarthik M.⁷,Mayakannan S.⁸,Sasikumar Bashyam⁹^ORCID

Affiliation:

1. Department of Mechanical Engineering, Malla Reddy Engineering College, Hyderabad 500100, India

2. Department of Mechanical Engineering, JCT College of Engineering and Technology, Pichanur, Coimbatore, Tamil Nadu, India

3. Department of Mathematics, Rajalakshmi Institute of Technology, Chennai 600124, Tamil Nadu, India

4. Department of Mechanical Engineering, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India

5. Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Guntur 522302, India

6. Department of Information Technology, Vels Institute of Science, Technology and Advanced Studies, Pallavaram, Chennai 600117, Tamil Nadu, India

7. Department of Civil Engineering, Coimbatore Institute of Technology, Coimbatore, Tamil Nadu, India

8. Department of Mechanical Engineering, Vidyaa Vikas College of Engineering and Technology, Namakkal, Tiruchengode, Tamil Nadu, India

9. Faculty of Mechanical and Production Engineering, Arba Minch University, Arba Minch, Ethiopia

Abstract

In this research, the friction and wear of AA7075 nanocomposites reinforced with graphene and graphite were studied. Graphene’s inclusion dramatically enhanced the material’s mechanical characteristics, friction, and wear resistance. AA7075 is strengthened with less graphene, and AA7075, reinforced with more graphite, exhibits similar wear and friction behavior. Wear rate and coefficient of friction predictions for AA7075-graphene nanocomposites were made using five machine learning (ML) regression models. ML simulations reveal that the wear and friction of AA7075-graphene composites are most sensitive to the proportion of graphene presence, the loadings, and the hardness.

Publisher

Hindawi Limited

Subject

General Materials Science

Link

http://downloads.hindawi.com/journals/jnm/2023/5723730.pdf

Reference43 articles.

1. Analysis of the friction and wear of graphene reinforced aluminum metal matrix composites using machine learning models

2. Machine learning models of the transition from solid to liquid lubricated friction and wear in aluminum-graphite composites

3. Process modeling and toxicological evaluation of adsorption of tetracycline onto the magnetized cotton dust biochar

4. Structural Behaviour of Metakaolin Geopolymer Concrete Wall-Type Abutments with Connected Wing Walls

5. Designing lubricants by artificial intelligence;W. T. Tysoe;Tribology and Lubrication Technology,2020

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