Investigation and Constitutive Modelling of High Strength 6xxx Series Aluminium Alloy: Precipitation Hardening Responses to FAST (Fast Light Alloys Stamping Technology) and Artificial Ageing

Abstract:

FAST (Fast light Alloys Stamping Technology) has recently been developed to efficiently and economically manufacture lightweight, high strength structural components from aluminium alloys sheet. Post-form strength prediction of 6xxx series aluminium alloy (AA6xxx) after FAST and multiple stage heat treatments has been a challenge. This is due to the effect of pre-existing dislocations induced via high temperature plastic deformation in the forming process. In the present research, a new PFS (post-form strength) model has been proposed to predict the age-hardening response of AA6xxx alloys undergoing FAST and subsequent thermal cycles. The model incorporates two sub-models, for simulating viscoplastic flow and predicting strength evolution respectively. The first sub-model incorporates a set of constitutive equations, developed to model the stress-strain curve of AA6xxx during FAST. The second sub-model employs precipitation-hardening and dislocation-hardening theories to simulate the evolution of microstructure and, as a consequence, strength of alloys undergoing artificial ageing cycles. This is calculated by considering the intrinsic resistance of the alloy to dislocation movement due to solute atoms and precipitates. The strength was computed accurately via the internal state variables method, in which dislocation density, volume fraction of precipitates, solute concentration and radii of precipitates were correlated. Furthermore, the model was validated by comparing results with transmission electron microscope (TEM) images as well as hardness measurements. Hence, the model performs as a powerful and comprehensive tool to simulate post-form strength of 6xxx series aluminium alloys that undergo complicated thermomechanical processes including high temperature deformation and post-form heat treatment, with less than 5% deviation between measured and predicted values.

• Periodical: Materials Science Forum (Volume 941)
• Pages: 814-820
• DOI: https://doi.org/10.4028/www.scientific.net/MSF.941.814
• Online since: December 2018
• Authors: Qun Li Zhang, Saksham Dhawan, Xi Luan, Qiang Du, Jun Liu, Li Liang Wang*
• Keywords: AA6xxx, Artificial Ageing, FAST, Materials Modelling, Precipitation Response

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