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Machine Learning in Copper-Graphene Composites by Makhmud Assentay is a document available to read on EtoBox.
What is Machine Learning in Copper-Graphene Composites about?
This study explores the application of machine learning (ML) models to predict the mechanical properties of copper-graphene (Cu/Gr) composites, focusing on yield strength and ultimate tensile strength. The results demonstrate that ML can accurately predict these properties, with Random Forest emerging as the most effective model. Feature analysis reveals that the volume percentage of graphene and processing methods significantly influence the mechanical properties of the composites.
- Author
- Makhmud Assentay
- Language
- EN