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

Autor:	I. S. N. V. R. Prasanth, Prabahar Jeevanandam, P. Selvaraju, K. Sathish, S. K. Hasane Ahammad, P. Sujatha, M. Kaarthik, S. Mayakannan, Bashyam Sasikumar
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	Article Subject General Materials Science
Zdroj:	Journal of Nanomaterials.
ISSN:	1687-4110
DOI:	10.1155/2023/5723730
Popis:	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.
Databáze:	OpenAIRE
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