Thermomechanical modeling of distortional hardening fully coupled with ductile damage under non-proportional loading paths. (July 2018)

• Record Type: Journal Article
• Authors: Zhang, Kai; Badreddine, Houssem; Saanouni, Khemais
• Is Part Of: International journal of solids and structures. Volume 144/145(2018)
• Page Start: 123
• Page End: 136
• Publication Date: 2018-07
• Subjects: Thermomechanical coupling -- Anisotropy -- Asymmetry -- Distortional hardening -- Damage; Mechanics, Applied -- Periodicals; Structural analysis (Engineering) -- Periodicals; Elastic solids -- Periodicals; Mécanique appliquée -- Périodiques; Constructions, Théorie des -- Périodiques; Solides élastiques -- Périodiques; Elastic solids; Mechanics, Applied; Structural analysis (Engineering); Periodicals; 624.18
• DOI: 10.1016/j.ijsolstr.2018.04.018
• Languages: English
• ISSNs: 0020-7683

Abstract

Abstract: In order to capture, as accurately as possible, the complex physical effects during texture evolution of high strength materials subject to non-proportional loading paths at high temperature, a macroscale thermo-mechanical model is developed. This model accounts, in the framework of large inelastic strains, for anisotropic thermo-elasto-viscoplasticity with isotropic, kinematic and distortional hardenings strongly coupled with isotropic ductile damage. Following the footsteps of François (2001), induced anisotropy is modeled to predict the behavior under complex non-proportional loading paths. The initial plastic anisotropy and tension-compression asymmetry are considered through two different temperature dependent fourth-rank tensors. The full coupling of thermo-mechanical model with ductile damage is considered based on the total energy equivalence assumption. The proposed constitutive equations are implemented into finite element code ABAQUS/Explicit using appropriate user subroutine VUMAT. The local integration algorithm (at each Gauss point) of the developed model is based on a fully-implicit scheme. Applications have been made to three different materials (aluminum alloy AU4G T4 (2024), titanium alloy Ti-6Al-4V and magnesium alloy AZ31) to demonstrate the predictive capabilities of the proposed model.