典型文献
Rapid design of secondary deformation-aging parameters for ultra-low Co con-tent Cu–Ni–Co–Si–X alloy via Bayesian optimization machine learning
文献摘要：
It is difficult to rapidly design the process parameters of copper alloys by using the traditional trial-and-error method and simultan-eously improve the conflicting mechanical and electrical properties. The purpose of this work is to develop a new type of Cu–Ni–Co–Si alloy saving scarce and expensive Co element, in which the Co content is less than half of the lower limit in ASTM standard C70350 alloy, while the properties are as the same level as C70350 alloy. Here we adopted a strategy combining Bayesian optimization machine learning and experi-mental iteration and quickly designed the secondary deformation-aging parameters (cold rolling deformation 90％, aging temperature 450℃, and aging time 1.25 h) of the new copper alloy with only 32 experiments (27 basic sample data acquisition experiments and 5 iteration experi-ments), which broke through the barrier of low efficiency and high cost of trial-and-error design of deformation-aging parameters in precipita-tion strengthened copper alloy. The experimental hardness, tensile strength, and electrical conductivity of the new copper alloy are HV (285 ± 4), (872 ± 3) MPa, and (44.2 ± 0.7)％ IACS (international annealed copper standard), reaching the property level of the commercial lead frame C70350 alloy. This work provides a new idea for the rapid design of material process parameters and the simultaneous improvement of mech-anical and electrical properties.
文献关键词：
中图分类号：
作者姓名：
Hongtao Zhang;Huadong Fu;Yuheng Shen;and Jianxin Xie
作者机构：
Beijing Advanced Innovation Center for Materials Genome Engineering,University of Science and Technology Beijing,Beijing 100083,China;Key Laboratory for Advanced Materials Processing(MOE),University of Science and Technology Beijing,Beijing 100083,China;Beijing Laboratory of Metallic Materials and Processing for Modern Transportation,University of Science and Technology Beijing,Beijing 100083,China
文献出处：
引用格式：
[1]Hongtao Zhang;Huadong Fu;Yuheng Shen;and Jianxin Xie-.Rapid design of secondary deformation-aging parameters for ultra-low Co con-tent Cu–Ni–Co–Si–X alloy via Bayesian optimization machine learning)[J].矿物冶金与材料学报,2022(06):1197-1205
A类：
C70350,anical
B类：
Rapid,secondary,deformation,aging,parameters,ultra,Co,Si,via,Bayesian,optimization,machine,learning,It,difficult,rapidly,process,copper,alloys,by,using,traditional,trial,error,method,eously,conflicting,mechanical,electrical,properties,purpose,this,work,develop,new,type,saving,scarce,expensive,element,which,content,less,than,half,lower,limit,ASTM,standard,while,are,same,level,Here,adopted,strategy,combining,iteration,quickly,designed,cold,rolling,temperature,only,experiments,basic,sample,data,acquisition,broke,through,barrier,efficiency,high,cost,precipita,strengthened,experimental,hardness,tensile,conductivity,HV,IACS,international,annealed,reaching,property,commercial,lead,frame,This,provides,idea,material,simultaneous,improvement
AB值：
0.474038
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