2017
DOI: 10.1016/j.cma.2017.02.027
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Computational anisotropic hardening multiplicative elastoplasticity based on the corrector elastic logarithmic strain rate

Abstract: Highlights • New computational framework for multiplicative anisotropic elastoplasticity. • Based on a six-dimensional additive corrector update for elastic logarithmic strains. • No hypothesis needed for the plastic spin in order to integrate the symmetric flow. • Yields a fully symmetric finite element formulation parallel to the infinitesimal one. • Overcomes the so-called "rate issue" from a computational standpoint.

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Cited by 30 publications
(32 citation statements)
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“…(33), which drives changes in volume via mass growth, resembles a pressure-sensitive yield function in rate-independent elastoplasticity, which triggers changes in volume via inelastic mechanisms, generally coupled with plastic distortions but without mass exchange (such as for geomaterials, metallic foams, or filled polymers [16]). Since the present G&R formulation contains other noticeable differences when compared to classical elastoplasticity (e.g., mixture theory describing different behaviors of different constituents, rather than a homogenized material; pre-stresses with evolving natural configurations; and a multiplicative decomposition without explicit consideration of an elastic gradient), elastic predictor / inelastic corrector integration schemes typically used in computational plasticity [17,18] do not seem well suited for integrating the present rateindependent constrained-mixture equations. Rather, we solved this set of nonlinear equations exactly using a different stress-point resolution procedure (Box 1), whose consistent linearization for efficient finite element implementations is addressed next.…”
Section: Remarkmentioning
confidence: 99%
“…(33), which drives changes in volume via mass growth, resembles a pressure-sensitive yield function in rate-independent elastoplasticity, which triggers changes in volume via inelastic mechanisms, generally coupled with plastic distortions but without mass exchange (such as for geomaterials, metallic foams, or filled polymers [16]). Since the present G&R formulation contains other noticeable differences when compared to classical elastoplasticity (e.g., mixture theory describing different behaviors of different constituents, rather than a homogenized material; pre-stresses with evolving natural configurations; and a multiplicative decomposition without explicit consideration of an elastic gradient), elastic predictor / inelastic corrector integration schemes typically used in computational plasticity [17,18] do not seem well suited for integrating the present rateindependent constrained-mixture equations. Rather, we solved this set of nonlinear equations exactly using a different stress-point resolution procedure (Box 1), whose consistent linearization for efficient finite element implementations is addressed next.…”
Section: Remarkmentioning
confidence: 99%
“…By choosing more complex models that account for e.g. multiplicative decomposition of deformation gradient [27], hyperelasticity [42], anisotropic hardening [10] or even complete anisotropic elastoplasticity [40] more complex material description could be reached, but this has not been considered in this work.…”
Section: Constitutive Modelsmentioning
confidence: 99%
“…where we used the usual assumption that tr R e ≈ t+∆t 0 R e [75], [73], [76], [69]. By systematic use of the chain rule, the tangent of the small strains algorithm may be converted to the typical tangent employed in finite element codes which relates second Piola-Kirchhoff stresses with Green-Lagrange strains in the reference configuration.…”
Section: Large Strains Formulationmentioning
confidence: 99%
“…We defined the trial and corrector partial derivative contributions trε e ≡ε e |ε p=0 and ctε e ≡ε e |ε =0 . In the new formulation of large strain elasto-plasticity presented in [69] and [70], this framework based on the chain rule remains additive and unaltered at large strains even when using the multiplicative decomposition. The stored energy is -see Figure 2.1 Ψ (ε e , ξ, γ) = W (ε e ) + H (ξ, γ) = 1 2 ε e : C : ε e + H (ξ, γ)…”
Section: Introductionmentioning
confidence: 99%
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