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

The effect of graphene on the material properties, friction, and wear of self-lubricating aluminum-based metal matrix composites (MMC) was compared with the effect of graphite as the reinforcement. Notable enhancement of mechanical properties and friction and wear performance was observed with graph...

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Bibliographic Details
Published in:Tribology international 2022-06, Vol.170, p.107527, Article 107527
Main Authors: Hasan, Md Syam, Wong, Tien, Rohatgi, Pradeep K., Nosonovsky, Michael
Format: Article
Language:English
Subjects:
Aluminum
Aluminum matrix composites
Aluminum-graphene composite
Coefficient of friction
Friction
Graphene
Graphite
Machine learning
Material properties
Mechanical properties
Metal matrix composites
Regression models
Self lubrication
Solid lubricant
Statistical analysis
Triboinformatics
Wear rate
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Summary:The effect of graphene on the material properties, friction, and wear of self-lubricating aluminum-based metal matrix composites (MMC) was compared with the effect of graphite as the reinforcement. Notable enhancement of mechanical properties and friction and wear performance was observed with graphene addition. Statistical analysis suggested that a much lesser amount of graphene reinforcement can produce friction and wear performance similar to that of aluminum MMCs with a higher amount of graphite. Five machine learning (ML) regression models were developed to predict the wear rate and coefficient of friction (COF) of aluminum-graphene MMCs. ML study suggested that the weight percent of graphene, loading conditions, and hardness had the largest influence on the wear and friction of aluminum-graphene composites.
ISSN:0301-679X
1879-2464
DOI:10.1016/j.triboint.2022.107527