数据驱动的铝基复合材料性能预测和逆向设计

采用数据驱动的方法对 SiCp(0.5CNT)/7075Al 铝基复合材料的化学成分以及制备工艺进行了分析, 针对抗拉强度和延伸率两个力学性能进行了特征重要性分析, 构建了包含 8 种机器学习算法的集成框架, 自动进行模型的参数调优和最优模型选择, 并在此基础上进行了材料逆向设计. 实验结果表明, 在 470 ${^\circ}$C 固溶 40 min, 120${^\circ}$C 时效 15 h 的热处理工艺下, SiCp(0.5CNT)/7075Al-1.0Mg 复合材料抗拉强度和延伸率的预测值为 617.48 MPa 和 2.98%, 实验值为 647.0 MPa 和 3.31%, 两项物理性能的平均绝对百分比误差(mean absolute percentage errors, MAPE)较小, 依次为 4.56% 和 9.97%. 这说明本数据驱动方法对铝基复合材料的工艺优化和性能提升有一定指导意义.

Data-driven based properties prediction and reverse design of aluminum matrix composites

A data-driven approach was used to analyze the chemical composition and preparation process of aluminum matrix composites SiCp(0.5CNT)/7075Al, and analyze the tensile strength and elongation. An integrated framework comprising eight machine learning algorithms was constructed to automatically perform parameters tuning and optimal model selection. Subsequently, an inverse design of the material was conducted. Experimental results showed that under the heat treatment of a solid solution at 470 ${^\circ}$C for 40 min and aging at 120 ${^\circ}$C for 15 h, the predicted tensile strength and elongation of SiCp(0.5CNT)/7075Al-1.0Mg were 617.48 MPa and 2.98%, respectively, whereas the real experimental values were 647.0 MPa and 3.31%, respectively. The mean absolute percentage errors (MAPE) of the two mechanical properties between the predicted and experimental results were 4.56% and 9.97%, respectively. It indicated the effectiveness of the data-driven method for the process optimization and property improvement of aluminum matrix composites.