Alexander Wessel | Searcharxiv

arXiv2024

Incorporating precipitation-related effects on plastic anisotropy of age-hardenable aluminium alloys into crystal plasticity constitutive models

Crystal plasticity finite element simulations are frequently employed to predict the plastic anisotropy of polycrystalline metals based on their crystallographic texture. In age-hardenable aluminium alloys, however, the texture-induced plastic anisotropy is known to affect by precipitation. This paper presents a new modelling approach to incorporate this effect into crystal plasticity constitutive models. The approach focuses on the overall effect of precipitation, which is assumed to result in an additional directional dependency with respect to a global material orientation, superimposed with the texture-induced plastic anisotropy. This additional directional dependency is implemented into a conventional crystal plasticity constitutive model via a modified hardening law that introduces two new parameters, of which only one is treated as a free parameter. To demonstrate the applicability of the new modelling approach, it is applied to an age-hardenable AA6014-T4 aluminium alloy and compared against a state-of-the-art crystal plasticity constitutive model that considers only crystallographic texture. The results demonstrate that the new modelling approach significantly improves the prediction accuracy of the plastic anisotropy for the AA6014-T4 aluminium alloy studied.

cond-mat.mtrl-sci↗

arXiv2022

Machine learning-based sampling of virtual experiments within the full stress state

This paper presents a new machine learning-based approach to investigate anisotropic yield surfaces of sheet metals by means of virtual experiments. The new sampling approach is based on the machine learning technique known as active learning, which has been adapted to efficiently sample virtual experiments with respect to the full stress state in order to identify parameters of anisotropic yield functions. The approach was employed to sample virtual experiments based on the crystal plasticity finite element method (CPFEM) for a DX56D deep drawing steel and compared with two state-of-the-art sampling methods taken from the literature. The resulting points on the initial yield surface for all three sampling methods were used to identify parameters of the anisotropic yield functions Hill48, Yld91, Yld2004-18p and Yld2004-27p. The results show that the new machine learning-based sampling approach has a higher sampling efficiency than the two state-of-the-art sampling methods. Consequently, fewer computationally expensive crystal plasticity simulations are required. By comparing different variants of the Hill48, Yld91, Yld2004-18p and Yld2004-27p yield surfaces, it was also found that identifying parameters of anisotropic yield functions based on virtual experiments sampled within the full stress state can lead to a degraded representation of the in-plane anisotropy. With respect to DX56D deep drawing steel, this degradation was observed for the Yld2004-18p yield function. As a consequence, the representation of the in-plane anisotropy must be carefully reviewed when taking the full stress state into account. In this context, Yld2004-27p was identified as being sufficiently flexible to simultaneously represent the plastic anisotropy of DX56D with respect to the in-plane and out-of-plane behaviour with high accuracy.

cond-mat.mtrl-sci↗