缩颈
材料科学
应变率
各向异性
硬化(计算)
可塑性
铝
应变硬化指数
灵敏度(控制系统)
复合材料
正规化(语言学)
金属薄板
张力(地质)
冶金
极限抗拉强度
计算机科学
电子工程
人工智能
物理
光学
工程类
图层(电子)
作者
X Li,Christian C. Roth,Kedar S. Pandya,Nikolaos Karathanasopoulos,Dirk Mohr
出处
期刊:IOP conference series
[IOP Publishing]
日期:2022-05-01
卷期号:1238 (1): 012006-012006
被引量:1
标识
DOI:10.1088/1757-899x/1238/1/012006
摘要
Abstract The accurate description of the strain rate and temperature dependent response of Aluminium alloys is a perpetual quest in the hot forming industry. In the present study, uniaxial tension, and notched tension experiments are conducted for an aluminium AA7075-T6 sheet metal at various temperatures and strain rates. The experimental campaign covers strain rates ranging from 0.001/s to 100/s, and temperatures ranging from 20°C to 360°C. We observe low strain rate sensitivity at room temperature, with an increase in strain rate sensitivity as temperature is increased up to 360°C. An YLD2000-3D model is employed to describe the anisotropy of the material. A machine learning based hardening model is employed to capture the complex strain rate and temperature effect on the observed hardening response. Counter-example regularization is utilized to guarantee a convergence in the numeric return-mapping algorithm. Comparing the experimental force-displacement curves with the numerical predictions, the neural network model accurately describes the large deformation response of the material in the post-necking range.
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