论文标题
将机器学习应用于铜石墨烯复合材料的机械性能
Application of Machine Learning to Mechanical Properties of Copper Graphene Composites
论文作者
论文摘要
虽然铜 - 石膏（CU/GR）复合材料由于理论上的强度和电导率而变得有前途的材料，但其设计受到影响其性质的大量变量的阻碍。我们应用了四种不同的机器学习（ML）模型，以手动收集的数据集编译了用粉状冶金技术加工的石墨烯增强铜复合材料的产量强度和最终拉伸强度。我们的结果表明，ML模型可以以令人满意的精度预测Cu/GR复合材料的机械性能。功能分析为影响这些特性的最重要因素提供了新的见解。
While copper-graphene (Cu/Gr) composites have been promising materials due to their theoretically high strength and conductivity, their design has been hampered by the large number of variables affecting their properties. We applied four different Machine Learning (ML) models to manually collected datasets compiling the yield strength and ultimate tensile strength of graphene-reinforced copper composites processed with powder metallurgy techniques. Our results indicate that ML models can predict the mechanical properties of Cu/Gr composites with satisfactory accuracy. Feature analysis provided new insights into the most important factors that affect these properties.
