The effect of shearable clusters and precipitates on dynamic recovery of Al alloys
Source abstract
Abstract
The formability of Al alloys is strongly influenced by their strain hardening capacity. Whilst the effect of precipitates on yield strength has been thoroughly studied, their effects on the strain hardening behaviour have been comparatively less studied. This is especially true for the case of shearable particles, such as those formed during natural aging, or underaging. This work presents a detailed study of the effect of shearable clusters/precipitates on the room temperature dynamic recovery of 7xxx Al alloys. The dynamic recovery behaviour is characterised by the slope of the stage III hardening (β) curve in a Kocks-Mecking plot, and the cluster/precipitate state has been characterised using small angle x-ray scattering and atom probe tomography. The rate of dynamic recovery is shown to depend non-monotonically on the yield strength of the alloy. For alloys in the solution treated and quenched state, or with an extremely fine distribution of clusters, dynamic recovery becomes more difficult with increasing alloy yield strength. However, as the cluster/particle spacing increases, such as during artificial aging, dynamic recovery becomes easier. A phenomenological model is presented showing that the critical microstructural features controlling this non-monotonic dependence of dynamic recovery on yield strength is the ratio of the cluster/precipitate spacing and the critical annihilation distance between dynamically recovering dislocations. The model is general and describes well the experimental data. It can be used as a predictive tool to guide microstructure design for combinations of yield strength and strain hardening behaviour.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Materials Characterization Techniques · Aluminum Alloy Microstructure Properties · Microstructure and mechanical properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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