材料科学
制作
合金
退火(玻璃)
沉积(地质)
微观结构
降水
冶金
吞吐量
化学工程
计算机科学
病理
沉积物
无线
替代医学
气象学
古生物学
工程类
物理
生物
电信
医学
作者
Xiukuang Zhang,Jun Yin,Qian Lei,Xiangpeng Meng,Xiaobo Chen,Zhou Li
标识
DOI:10.1016/j.matlet.2021.131247
摘要
Fabrication of gradient-composition alloys is a significant challenge because the adjustment of chemical composition and physical performance is essential. The directed energy deposition manufacturing provided a high-throughput fabrication method for gradient Cu-Fe-Cr alloys, on a master alloy substrate. The liquid diffusion of Fe and Cr contributed to the gradient variation in microstructure and composition. Followed decomposition precipitation improved the hardness of as-fabricated Cu-Fe-Cr alloys, which increased from 84.6 HV to 100 HV. Machine learning was employed to predict the hardness of Cu-18.6Fe-5Cr alloy at different annealing time, the experimental results agreed well with the prediction. These findings indicated that high-throughput manufacturing combined with machine learning can accelerate the design and fabrication of gradient-composition copper alloys.
科研通智能强力驱动
Strongly Powered by AbleSci AI