论文标题
数据分析方法预测铜基质复合材料的硬度
Data analytics approach to predict the hardness of the copper matrix composites
论文作者
论文摘要
铜基质复合材料在需要出色的电导率和机械性能的应用中具有很高的潜力。在这项研究中，已经应用了机器学习模型来预测通过粉末冶金技术生产的铜基质复合材料的硬度。在这项工作中考虑了两个特定的复合材料。从实验中，我们提取了铣削时间（MT，小时），位错密度（DD，1/m2），平均粒径（PS，NM），密度（GM/CM3）和屈服应力（MPA），而Vickers Harts（MPA）则用作依赖性变量。通过计算自变量和因变量之间的Pearson相关系数（PCC）进行特征选择。我们采用了六个不同的机器学习回归模型来预测两个基质复合材料的硬度。
Copper matrix composite materials have exhibited a high potential in applications where excellent conductivity and mechanical properties are required. In this study, the machine learning models have been applied to predict the hardness of the copper matrix composite materials produced via powder metallurgy technique. Two particular composites were considered in this work. From experiments, we extracted the independent variables (features) like the milling time (MT, Hours), dislocation density (DD, 1/m2 ), average particle size (PS,nm), density (gm/cm3 ) and yield stress (MPa) while the Vickers Hardness (MPa) was used as the dependent variable. Feature selection was performed by calculation the Pearson correlation coefficient (PCC) between the independent and dependent variables. We employed six different machine learning regression models to predict the hardness for the two matrix composites.
