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

Study of Friction and Wear Behavior of Graphene-Reinforced AA7075 Nanocomposites by Machine Learning I. S. N. V. R. Prasanth Department of Mechanical Engineering, Malla Reddy Engineering College, Hyderabad 500100, India Prabahar Jeevanandam Department of Mechanical Engineering, JCT College of Engineering and Technology, Pichanur, Coimbatore, Tamil Nadu, India P. Selvaraju Department of Mathematics, Rajalakshmi Institute of Technology, Chennai 600124, Tamil Nadu, India K. Sathish Department of Mechanical Engineering, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India https://orcid.org/0000-0002-0597-0327 S. K. Hasane Ahammad Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Guntur 522302, India P. Sujatha Department of Information Technology, Vels Institute of Science, Technology and Advanced Studies, Pallavaram, Chennai 600117, Tamil Nadu, India M. Kaarthik Department of Civil Engineering, Coimbatore Institute of Technology, Coimbatore, Tamil Nadu, India S. Mayakannan Department of Mechanical Engineering, Vidyaa Vikas College of Engineering and Technology, Namakkal, Tiruchengode, Tamil Nadu, India Bashyam Sasikumar Faculty of Mechanical and Production Engineering, Arba Minch University, Arba Minch, Ethiopia https://orcid.org/0000-0003-3115-438X Muhammad P. Jahan

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.
2 15 2023 1 15 5723730 5723730 https://creativecommons.org/licenses/by/4.0/ 10.1155/2023/5723730 https://www.hindawi.com/journals/jnm/2023/5723730/ http://downloads.hindawi.com/journals/jnm/2023/5723730.pdf http://downloads.hindawi.com/journals/jnm/2023/5723730.pdf http://downloads.hindawi.com/journals/jnm/2023/5723730.pdf http://downloads.hindawi.com/journals/jnm/2023/5723730.pdf http://downloads.hindawi.com/journals/jnm/2023/5723730.pdf http://downloads.hindawi.com/journals/jnm/2023/5723730.pdf http://downloads.hindawi.com/journals/jnm/2023/5723730.pdf http://downloads.hindawi.com/journals/jnm/2023/5723730.xml 10.1016/j.triboint.2022.107527 10.1016/j.triboint.2021.107326 10.1016/j.jwpe.2022.103046 10.1155/2022/6103595 Tribology and Lubrication Technology W. T. Tysoe 76 6 2020 Designing lubricants by artificial intelligence 10.17222/mit.2018.116 10.1155/2022/7910072 10.1115/1.4050525 10.1007/s40033-022-00344-y 10.1177/14644207211025810 10.1007/s40735-021-00475-x 10.1007/s12633-018-9967-0 10.1007/s12633-017-9675-1 10.1007/978-3-030-92567-3_3 10.1109/TNB.2022.3201237 10.1021/acs.langmuir.2c01331 10.1021/acsanm.2c01950 10.1002/pc.26974 10.1002/smtd.202200537 10.1177/09544089221115306 10.1109/LED.2022.3189204 10.1016/j.matpr.2022.06.253 10.1007/s40843-022-2103-9 10.1080/10408398.2022.2078950 10.1080/17455030.2022.2041763 10.1109/TNB.2022.3155264 10.1142/S242491302144001X 10.1002/er.7602 10.1021/acsnano.1c11118 10.1002/eem2.12304 10.1080/17455030.2021.2003475 10.1021/acsami.1c12767 10.1002/adfm.202100547 10.1557/adv.2020.327 10.1007/s43452-022-00411-x Fratura ed Integrità Strutturale A. Mishra 15 58 242 2021 10.3221/IGF-ESIS.58.18 Supervised machine learning classification algorithms for detection of fracture location in dissimilar friction stir welded joints 10.1007/s11837-021-04706-x 10.1016/j.jajp.2020.100040 10.1016/j.apt.2020.12.024 10.1016/j.ijplas.2020.102788 10.1111/ffe.12983 10.1080/2374068X.2022.2080330 10.1088/2053-1591/aacc50