Machine-learning-assisted prediction of the mechanical properties of Cu–Al alloy

作     者：Zheng-hua Deng Hai-qing Yin Xue Jiang Cong Zhang Guo-fei Zhang Bin Xu Guo-qiang Yang Tong Zhang Mao Wu Xuan-hui Qu Zheng-hua Deng;Hai-qing Yin;Xue Jiang;Cong Zhang;Guo-fei Zhang;Bin Xu;Guo-qiang Yang;Tong Zhang;Mao Wu;Xuan-hui Qu

作者机构：Collaborative Innovation Center of Steel TechnologyUniversity of Science and Technology BeijingBeijing 100083China Chongqing Engineering Technology Research Center for Light Alloy and ProcessingChongqing Three Gorges UniversityChongqing 404000China Beijing Advanced Innovation Center for Materials Genome EngineeringBeijing 100083China Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijing 100083China 

出 版 物：《International Journal of Minerals,Metallurgy and Materials》 (矿物冶金与材料学报（英文版）)

年 卷 期：2020年第27卷第3期

页      面：362-373页

核心收录：

学科分类：08[工学] 080502[工学-材料学] 0805[工学-材料科学与工程（可授工学、理学学位）] 

基　　金：financial support from the National Key Research and Development Program of China(No.2016YFB0700503) the National High Technology Research and Development Program of China(No.2015AA03420) Beijing Science and Technology Plan(No.D16110300240000) National Natural Science Foundation of China(No.51172018) the Science and Technology Research Program of Chongqing Municipal Education Commission(No.KJQN201801202) 

主　　题：powder metallurgy tensile strength hardness machine learning Cu–Al alloy SMOreg/puk 

摘      要：The machine-learning approach was investigated to predict the mechanical properties of Cu–Al alloys manufactured using the powder metallurgy technique to increase the rate of fabrication and characterization of new materials and provide physical insights into their *** algorithms were used to construct the prediction models, with chemical composition and porosity of the compacts chosen as the *** results show that the sequential minimal optimization algorithm for support vector regression with a puk kernel(SMOreg/puk) model demonstrated the best prediction ability. Specifically, its predictions exhibited the highest correlation coefficient and lowest error among the predictions of the six models. The SMOreg/puk model was subsequently applied to predict the tensile strength and hardness of Cu–Al alloys and provide guidance for composition design to achieve the expected values. With the guidance of the SMOreg/puk model, Cu–12Al–6Ni alloy with a tensile strength(390 MPa) and hardness(HB 139) that reached the expected values was developed.