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 

机构地区：[1]Collaborative Innovation Center of Steel Technology,University of Science and Technology Beijing,Beijing 100083,China [2]Chongqing Engineering Technology Research Center for Light Alloy and Processing,Chongqing Three Gorges University,Chongqing 404000,China [3]Beijing Advanced Innovation Center for Materials Genome Engineering,Beijing 100083,China [4]Institute for Advanced Materials and Technology,University of Science and Technology Beijing,Beijing 100083,China

出　　处：《International Journal of Minerals,Metallurgy and Materials》2020年第3期362-373,共12页矿物冶金与材料学报（英文版）

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)

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 properties.Six algorithms were used to construct the prediction models, with chemical composition and porosity of the compacts chosen as the descriptors.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.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 properties.Six algorithms were used to construct the prediction models, with chemical composition and porosity of the compacts chosen as the descriptors.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.

关 键 词：powder metallurgy tensile strength HARDNESS machine learning Cu–Al alloy SMOreg/puk 

分 类 号：TF125.211[冶金工程—粉末冶金]