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Research on stamping formability and process simulation of AZ31 magnesium alloy based on deep learning

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成果类型:
期刊论文
作者:
Wang, Shuo;Wu, Yan;Xu, Qian;Ma, Yanman;Wang, Tianzhu
通讯作者:
Wu, Y
作者机构:
[Wang, Shuo; Wu, Y; Wu, Yan; Ma, Yanman; Xu, Qian; Wang, Tianzhu] Wuhan Polytech Univ, Coll Mech Engn, Wuhan 430048, Peoples R China.
通讯机构:
[Wu, Y ] W
Wuhan Polytech Univ, Coll Mech Engn, Wuhan 430048, Peoples R China.
语种:
英文
关键词:
AZ31 magnesium alloy;Stamping forming;GA;DBO algorithm;BP neural network;RF regression model
期刊:
Materials Today Communications
ISSN:
2352-4928
年:
2024
卷:
40
基金类别:
Science Research Plan of the Department of Education of Hubei Province [D20221606]
机构署名:
本校为第一且通讯机构
院系归属:
机械工程学院
摘要:
To study the formability of AZ31 magnesium alloy under different temperature conditions and improve the forming quality of its parts, this paper aims to solve the minimization problem of pursuing the "maximum thinning rate". This study obtained the mechanical properties of AZ31 magnesium alloy at 25 degrees C, 150 degrees C, 250 degrees C, and 350 degrees C through uniaxial tensile tests and explored the stamping formability of AZ31 magnesium alloy mobile phone cases using the finite element inverse calculation method (MSTEP). Based on the hot stamping of AZ31 magnesium alloy at 250 degrees C,...

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