Novel uniaxial and biaxial reverse experiments for material parameter identification in an advanced anisotropic cyclic plastic-damage model

IF 3.4 3区 材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY Mechanics of Materials Pub Date : 2025-02-21 DOI:10.1016/j.mechmat.2025.105294
Zhichao Wei , Steffen Gerke , Michael Brünig
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引用次数: 0

Abstract

This paper discusses the calibration and verification of material parameters based on novel one-axis and biaxial reverse loading experiments. The uniaxially loaded tension–compression (TC-), one-axis-loaded shear, and biaxially loaded HC-specimens are designed to perform different cyclic experiments, covering a wide range of stress triaxialities. Special anti-buckling clamping jaws and a newly designed downholder are used during the experiments to avoid buckling under compression loads. During the experiments, strain fields are recorded and analyzed using the digital image correlation (DIC) technique. A combination of direct and indirect fitting approaches is employed to identify the essential elastic–plastic material parameters for the proposed advanced elastic–plastic-damage constitutive model. The characterization of damage parameters is not discussed in this paper. A quantitative error analysis method is introduced to check the quality of the numerical simulation using the obtained material parameters. The comparison between experimental and numerical results demonstrates that the proposed damage model with identified parameters can predict global load–displacement curves and local strain fields with good accuracy.
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来源期刊
Mechanics of Materials
Mechanics of Materials 工程技术-材料科学:综合
CiteScore
7.60
自引率
5.10%
发文量
243
审稿时长
46 days
期刊介绍: Mechanics of Materials is a forum for original scientific research on the flow, fracture, and general constitutive behavior of geophysical, geotechnical and technological materials, with balanced coverage of advanced technological and natural materials, with balanced coverage of theoretical, experimental, and field investigations. Of special concern are macroscopic predictions based on microscopic models, identification of microscopic structures from limited overall macroscopic data, experimental and field results that lead to fundamental understanding of the behavior of materials, and coordinated experimental and analytical investigations that culminate in theories with predictive quality.
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