Abstract

There exists a critical need to use high recycle fraction aluminium in automotive applications to lightweight and reduce greenhouse gas emissions. The increased availability of recycled aluminium has financial, energy and environmental benefits since secondary aluminium manufacturing requires just ~2.8 kWh/kg of metal produced whereas primary production takes ~45 kWh/kg [1]. Meanwhile, forming components from aluminium is extremely important to sustainable transport because it can save over 40% weight, compared to steel, while every 10% reduction in vehicle weight can result in a 5-6% reduction in fuel consumption [2]. Due to the high recyclability of aluminium, the performance of recycled aluminium alloys is comparable to that of the primary aluminium product, and they are significantly less expensive and more recyclable than composites.

However, some impurity elements are inevitably introduced and built up during the recycling process [3-4]. These elements such as iron, manganese, and silicon readily form intermetallic compounds (IMC) which affect the processing and eventual mechanical properties of the finished product. Mitigating the heterogeneous micromechanical response caused by IMCs has emerged as a critical point in improving the formability of aluminium alloys.

In this study, a combination of experiments and simulations is used to examine the heterogeneity caused by intermetallic particle distributions. In recent years, simulation methods, such as full-field crystal plasticity, have been widely used for basic microstructure-based mechanical predictions as well as for performance simulation and are ideally suited to study heterogeneous micromechanical response on 6xxx series aluminium alloys by modelling the deformation of the alloys with varying intermetallic compounds [5].

The advanced crystal plasticity modelling framework – DAMASK [6], combined with experimental data, is used to simulate recycled aluminium alloys with varying microstructures under different loading conditions. This involves constructing a set of Representative Volume Elements (RVEs) that contain IMCs with varying volume fractions and distributions. By simulating different strain paths in these RVEs localization behaviours are studied to better understand the effect of particle shape, size, volume fraction, and distribution on formability in recycled aluminium alloys by predicted Forming Limit Diagram (FLD).

Besides, mechanical testing with Digital Image Correlation (DIC) will be used to investigate the relationship between microstructure and heterogeneous response. Meanwhile, the predicted FLD of recycled 6xxx aluminium will be compared with the experimental results of the Nakajima forming test with DIC. Finally, a modelling workflow will be set up to predict the formability of created synthetic microstructures of recycled aluminium alloys with different orientations, volume fractions, distributions and morphology.

Reference

[1] Das, S. K., Green, J., & Kaufman, J. (2010). Aluminum recycling: economic and environmental benefits. Light Metal Age, 68(1), 42.

[2] Benedyk, J. (2010). Aluminum alloys for lightweight automotive structures. In Materials, design and manufacturing for lightweight vehicles (pp. 79-113).

[3] Hosseinifar, M., & Malakhov, D. V. (2008). Effect of Ce and La on microstructure and properties of a 6xxx series type aluminum alloy. Journal of materials science, 43(22), 7157-7164.

[4] Khan, M. H., Das, A., Li, Z., & Kotadia, H. R. (2021). Effects of Fe, Mn, chemical grain refinement and cooling rate on the evolution of Fe intermetallics in a model 6082 Al-alloy. Intermetallics, 132, 107132.

[5] Roters, F. (2011). Advanced material models for the crystal plasticity finite element method: development of a general CPFEM framework.

[6] Roters, F., Diehl, M., Shanthraj, P., Eisenlohr, P., & Reuber, C. (2019). Su Leen Wong, Tias Maiti, Alireza Ebrahimi, Thomas Hochrainer, HO Fabritius, et al. DAMASK–The Düsseldorf Advanced Material Simulation Kit for modeling multiphysics crystal plasticity, thermal, and damage phenomena from the single crystal up to the component scale. Computational Materials Science, 158, 420-478.