Prediction and optimization of tensile properties of 2219-T8 aluminum alloy TIG welding joint by machine learning - Details

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Abstract：

The　tensile　properties　of　2219-T8　aluminum　alloy　TIG　welding　joint　were　significantly　affected　by　the　microstructure,　local　mechanical　properties　and　weld　geometry.　This　paper　proposed　a　machine　learning　model　to　predict　and　optimize　the　tensile　properties　of　2219-T8　aluminum　alloy　TIG　welding　joint.　The　relationship　between　tensile　strength　of　joint　and　weld　geometry,　weld　zone　and　partially　melted　zone　(PMZ)　properties　was　developed　by　Kriging　model　combining　whale　optimization　algorithm　(WOA).　This　surrogate　model　demonstrated　a　high　precision,　with　R2　=　0.952　and　RMSE=3.77　MPa.　The　surrogate　model,　which　also　served　as　a　welding　process　guide,　was　utilized　to　determine　the　ideal　weld　geometry　corresponding　to　various　weak　zone　properties.　By　applying　the　optimization　process　based　on　the　surrogate　model,　the　optimized　joint　strength　coefficient　reached　70　%,　and　elongation　exceeded　4　%.　The　collaborative　regulation　mechanism　of　geometry　and　property　was　also　discussed.　©　2024　The　Author(s)

Keyword：

2219-T8 aluminum alloy Tensile properties Machine learning Kriging model TIG welding

Author Community：

[ 1 ] [Wan Z.]College of Materials Science and Engineering, Beijing University of Technology, Beijing, 100124, China
[ 2 ] [Wan Z.]Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China
[ 3 ] [Yi Z.]Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China
[ 4 ] [Zhao Y.]Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China
[ 5 ] [Zhao Y.]State Key Laboratory of Clean and Efficient Turbomachinery Power Equipment, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China
[ 6 ] [Zhao Y.]Key Laboratory for Advanced Materials Processing Technology, Ministry of Education, Tsinghua University, Beijing, 100084, China
[ 7 ] [Zhang S.]Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China
[ 8 ] [Li Q.]Capital Aerospace Machinery Corporation Limited, Beijing, 100076, China
[ 9 ] [Lin J.]College of Materials Science and Engineering, Beijing University of Technology, Beijing, 100124, China
[ 10 ] [Wu A.]Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China
[ 11 ] [Wu A.]State Key Laboratory of Clean and Efficient Turbomachinery Power Equipment, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China
[ 12 ] [Wu A.]Key Laboratory for Advanced Materials Processing Technology, Ministry of Education, Tsinghua University, Beijing, 100084, China

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Source ：

Materials and Design

ISSN： 0264-1275

Year： 2024

Volume： 245

8 . 4 0 0

JCR@2022

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WoS CC Cited Count：

SCOPUS Cited Count： 1

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 4

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