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Supervisor of Doctorate Candidates

Master Tutor

Gender:Female

Alma Mater:哈尔滨工业大学

Other Post:中国金属学会金属材料深度加工分会委员

Degree:Doctoral Degree

School/Department:机械工程学院

Discipline:Mechanical Manufacture and Automation

Business Address:知方楼8051

Contact Information:13390043773 微信同号

Hot Deformation Behavior of a 2024 Aluminum Alloy Sheet and its Modeling by Fields-Backofen Model Considering Strain Rate Evolution

Date:2019-07-01  Hits:

Indexed by:期刊论文

Journal:METALS

Included Journals:SCIE

Volume:9

Issue:2

ISSN No.:2075-4701

Key Words:non-uniform deformation behavior; digital image correlation; high temperature; improved Fields-Backofen model; aluminum alloy

Abstract:The deformation behavior of a 2024 aluminum alloy sheet at elevated temperatures was studied by uniaxial hot tensile tests over the nominal initial strain rate range of 0.001-0.1 s(-1) and temperature range of 375-450 degrees C. In order to analyze the deformation behavior with higher accuracy, a digital image correlation (DIC) system was applied to determine the strain distribution during hot tensile tests. Local stress-strain curves for different local points on the specimens were calculated. The strain rate evolution of each point during the tensile tests was investigated under different deformation conditions. Then, an improved Fields-Backofen (FB) model, taking into account the local strain rate evolution instead of the fixed strain rate, was proposed to describe the constitutive behaviors. It has been found that obvious non-uniform strain distribution occurred when the true strain was larger than 0.3 during hot tensile tests. The strain rate distribution during deformation was also non-uniform. It showed increasing, steady, and decreasing variation tendencies for different points with the increasing of strain, which led to the local flow stress being different at different local points. The flow stresses predicted by the improved FB model showed good agreement with experimental results when the strain rate evolutions of local points during tensile tests were considered. The prediction accuracy was higher than that of traditional FB models.

Date of Publication:2019-02-01

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