Machine Learning Design of Aluminum-Lithium Alloys with High Strength

作     者：Hongxia Wang Zhiqiang Duan Qingwei Guo Yongmei Zhang Yuhong Zhao 

作者机构：College of Semiconductors and PhysicsNorth University of ChinaTaiyuan030051China 2 School of Materials Science and EngineeringCollaborative Innovation Center of Ministry of Education and Shanxi Province for High-Performance Al/Mg Alloy MaterialsNorth University of ChinaTaiyuan030051China Beijing Advanced Innovation Center for Materials Genome EngineeringUniversity of Science and Technology BeijingBeijing100083China Institute of Materials Intelligent TechnologyLiaoning Academy of MaterialsShenyang110004China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第77卷第11期

页      面：1393-1409页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 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）] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(Nos.52074246,52275390,52205429,52201146) National Defense Basic Scientific Research Program of China(JCKY2020408B002) Key Research and Development Program of Shanxi Province(202102050201011,202202050201014) 

主　　题：Aluminum-lithium alloys neural network tensile strength yield strength 

摘      要：Due to the large unexplored compositional space,long development cycle,and high cost of traditional trial-anderror experiments,designing high strength aluminum-lithium alloys is a great *** work establishes a performance-oriented machine learning design strategy for aluminum-lithium alloys to simplify and shorten the development *** calculation results indicate that radial basis function(RBF)neural networks exhibit better predictive ability than back propagation(BP)neural *** RBF neural network predicted tensile and yield strengths with determination coefficients of 0.90 and 0.96,root mean square errors of 30.68 and 25.30,and mean absolute errors of 28.15 and 19.08,*** the validation experiment,the comparison between experimental data and predicted data demonstrated the robustness of the two neural network *** tensile and yield strengths of Al-2Li-1Cu-3Mg-0.2Zr(wt.%)alloy are 17.8 and 3.5 MPa higher than those of the Al-1Li4.5Cu-0.2Zr(wt.%)alloy,which has the best overall performance,*** demonstrates the reliability of the neural network model in designing high strength aluminum-lithium alloys,which provides a way to improve research and development efficiency.