Al-enabled properties distribution prediction for high-pressure die casting Al-Si alloy

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外文摘要：High-pressure die casting(HPDC)is one of the most popular mass production processes in the automotive industry owing to its capability for part consolidation.How-ever,the nonuniform distribution of mechanical properties in large-sized HPDC products adds complexity to part property evaluation.Therefore,a methodology for property predic-tion must be developed.Material characterization,simula-tion technologies,and artificial intelligence(AI)algorithms were employed.Firstly,an image recognition technique was employed to construct a temperature-microstructure charac-teristic model for a typical HPDC A17Si0.2Mg alloy.Moreo-ver,a porosity/microstructure-mechanical property model was established using a machine learning method based on the finite element method and representative volume element model results.Additionally,the computational results of the casting simulation software were mapped with the porosity/microstructure-mechanical property model,allowing accurate prediction of the property distribution of the HPDC Al-Si alloy.The AI-enabled property distribution model developed in this study is expected to serve as a foundation for intelligent HPDC part design platforms in the automotive industry.

外文关键词：

Artificial intelligence(AI)Properties predictionHigh-pressure die-casting(HPDC)Image recognitionMachine learning

作者：

Yu-Tong Yang、Zhong-Yuan Qiu、Zhen Zheng、Liang-Xi Pu、Ding-Ding Chen、Jiang Zheng、Rui-Jie Zhang、Bo Zhang、Shi-Yao Huang

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作者单位：

Key Laboratory for Light-Weight Materials,Nanjing Tech University,Nanjing 211816,People's Republic of China

School of Chemistry,Xi'an Jiaotong-Liverpool University,Suzhou 215000,Jiangsu,People's Republic of China

Materials Academy JITRI,Suzhou 215100,Jiangsu,People's Republic of China

Key Laboratory for Light-weight Materials,Nanjing University of Science and Technology,Nanjing 210009,People's Republic of China

College of Materials Science and Engineering,Chongqing University,Chongqing 400044,People's Republic of China

Collaborate Innovation Center of Steel Technology,University of Science and Technology Beijing,Beijing 100083,People's Republic of China

Chongqing Millison Technologies Inc.,Chongqing 401321,People's Republic of China

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出版年：

2024

DOI：

10.1007/s40436-024-00485-1

先进制造进展(英文版)

ISSN：

年,卷(期)：2024.12(3)