Abstract
Lightweight Al–Mg–Si alloys (6xxx series) are widely used as structural components in the aviation, automotive, and construction industries due to their balanced combination of strength, formability, corrosion resistance, and recyclability. Their strength is primarily achieved through a carefully controlled artificial ageing (AA) treatment of solution-treated alloys, during which a fine dispersion of nanoscale precipitates forms, effectively hindering dislocation motion during deformation.
In industrial applications, however, storage of the solution-treated alloy at room temperature prior to the final AA step is unavoidable due to handling and transportation requirements. During this storage period, “vacancies”—empty points in the crystal lattice where atoms are missing—play a crucial role in the clustering of solute atoms. This atom clustering process leads to an increase in hardness, known as the natural ageing (NA) effect.
To uncover how these “invisible” defects shape alloy strength, my research was designed to use and develop a series of modelling tools spanning atomic, nanometer, and micrometer scales. These include atomistic Monte Carlo simulations, mesoscale cluster-dynamics models, and grain-scale numerical models, complemented by machine-learning techniques. This work reveals how excess vacancies migrate and are annihilated within alloy grains after quenching from solution treatment, how they assist the movement and gathering of solute atoms, and how they gradually become bound to growing solute clusters.
These insights significantly advance our understanding of natural ageing behavior and, consequently, how natural ageing influences the subsequent artificial ageing response of the alloys. The developed models and simulations can help industrial partners further optimize the chemistry of commercial alloys and the corresponding processing parameters in industrial production