Specimens of three Al-Mg-Si alloys, 6060, 6005 and 6082, were solution heat treated, stored at different temperatures for different time, and artificially aged. Properties were measured before and after artificial ageing. The natural ageing response of the alloys is dependent on the storage temperature. Decreasing storage temperature leads to a delayed onset of natural ageing, but also to a higher strength after prolonged ageing, particularly for lean alloys such as 6060. The temperature and time of intermediate storage between solution heat treatment and artificial ageing has a significant effect on the strength of the artificially aged material. For the 6005 and 6082 alloys the processes that take place during natural ageing lead to a reduced strength after artificial ageing.
The effect of deformation after solution treatment on the two-step artificial aging response has been examined for AA7030 and AA7108 alloys. Aging experiments were conducted where the prestrain level was varied between 0 and 1.2. It was observed that the kinetics of aging were accelerated and the magnitude of the peak strength decreased in the presence of prestrain. This was attributed to the increased growth/coarsening rate of precipitates on dislocations and the widening of the precipitatesize distribution, respectively. A model was developed based on the internal state variable approach to predict yield stress as a function of the heat treatment and the level of prestrain. The increase in the kinetics of aging was accounted for in an average sense by the use of an effective diffusion coefficient that combines bulk and short-circuit diffusion. The precipitation model was based on two variables; the average spacing between precipitates and the average strength of precipitates. The variation of the average strength of precipitates with precipitate radius was varied to account for the change in the width of the precipitate-size distribution. Static recovery of the deformed structure during artificial aging was also accounted for in a first-order approximation. Good agreement was found between the model predictions and experimental results. Additional experimental data were obtained after the model was developed, and it was observed that the model made excellent predictions.
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