首页|National University of Defense Technology Researchers Describe Recent Advances in Machine Learning (Machine learning guided phase and hardness controlled AlCoCrCuFeNi high-entropy alloy design)
National University of Defense Technology Researchers Describe Recent Advances in Machine Learning (Machine learning guided phase and hardness controlled AlCoCrCuFeNi high-entropy alloy design)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Data detailed on artificial intelligence have been presented. According to news originating from Changsha, People’s Republic of China, by NewsRx correspondents, research stated, “Machine learning(ML) assisted high-entropy alloys(HEA) design is dedicated to solving the problem of long period and high cost of designing by traditional trial and error experimental methods.” Our news reporters obtained a quote from the research from National University of Defense Technology: “The classic AlCoCrCuFeNi HEA was taken as the research object. The phase structure prediction model and hardness prediction model were established respectively. The support vector machine(SVM) models have the best training performance in both tasks. The best phase classification accuracy is 0.944, and the root mean square error(RMSE) of the hardness regression model is 56.065HV. The two ML models are further connected in series. Based on the upper and lower limits of the data set, the high-throughput prediction and selection of phases and hardness of AlCoCrCuFeNi HEA are carried out simultaneously, thus realizing the efficient composition design of the new alloy.”
National University of Defense TechnologyChangshaPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
