Development of the post-form strength prediction model for a high-strength 6xxx aluminium alloy with pre-existing precipitates and residual dislocations
Politis, Denis J.
Fu, Ming Wang
Gharbi, Mohammad M.
SourceInternational Journal of Plasticity
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MetadataΕμφάνιση πλήρους εγγραφής
The applications of lightweight and high strength sheet aluminium alloys are increasing rapidly in the automotive industry due to the expanding global demand in this industrial cluster. Accurate prediction of the post-form strength and the microstructural evolutions of structural components made of Al-alloys has been a challenge, especially when the material undergoes complex processes involving ultra-fast heating and high temperature deformation, followed by multi-stage artificial ageing treatment. In this research, the effects of pre-existing precipitates induced during ultra-fast heating and residual dislocations generated through high temperature deformation on precipitation hardening behaviour have been investigated. A mechanism-based post-form strength (PFS) prediction model, incorporating the flow stress model and age-hardening model, was developed ab-initio to predict strength evolution during the whole process. To model the stress-strain viscoplastic behaviour and represent the evolution of dislocation density of the material in forming process, constitutive models were proposed and the related equations were formulated. The effect of pre-existing precipitates was considered in the age-hardening model via introducing the complex correlations of microstructural variables into the model. In addition, an alternative time-equivalent method was developed to link the different stages of ageing and hence the prediction of precipitation behaviours in multi-stage ageing was performed. Furthermore, forming tests of a U-shaped component were performed to verify the model. It was found that the model is able to accurately predict the post-form strength with excellent agreement with deviation of less than 5% when extensively validated by experimental data. Therefore, the model is considered to be competent for predicting the pre-empting material response as well as a powerful tool for optimising forming parameters to exploit age hardening to its maximum potential in real manufacturing processes.