On the Use of a Cluster Identification Method and a Statistical Approach for Analyzing Atom Probe Tomography Data for GP Zones in Al-Zn-Mg(-Cu) Alloys.

Early-stage clustering in two Al-Mg-Zn(-Cu) alloys has been investigated using atom probe tomography and transmission electron microscopy. Cluster identification by the isoposition method and a statistical approach based on the pair correlation function have both been applied to estimate the cluster size, composition, and volume fraction from atom probe data sets. To assess the accuracy of the quantification of clusters of different mean sizes, synthesized virtual data sets were used, accounting for a simulated degraded spatial resolution. The quality of the predictions made by the two complementary methods is discussed, considering the experimental and simulated data sets.

AutomAl 6000: Semi-automatic structural labelling of HAADF-STEM images of precipitates in Al-Mg-Si(-Cu) alloys.

When the Al-Mg-Si(-Cu) alloy system is subjected to age hardening, different types of precipitates nucleate depending on the composition and thermomechanical treatment. The main hardening precipitates extend as needles, laths or rods along the <100> directions in the aluminium matrix. It has been found that the structures of all metastable precipitates may be generalized as stacks of <100> columns, where most of these columns are replaced by solute elements. In the precipitates, a column relates to neighbour columns by a set of simple structural principles, which allows identification of species and relative longitudinal displacement over the (100) cross-section. Aberration-corrected high-angle annular dark field scanning transmission electron microscopy (HAADF-STEM) is an important tool for studying such precipitates. With the goal of analysing atomic resolution HAADF-STEM images of precipitate cross-sections in the Al-Mg-Si(-Cu) system, we have developed the stand-alone software AutomAl 6000, which features a column characterization algorithm based on the symbiosis of a statistical model and the structural principles formulated in a digraph-like framework. The software can semi-autonomously determine the 3D column positions in the image, as well as column species. In turn, AutomAl 6000 can then display, analyse and/or export the structure data. This paper describes the methodology of AutomAl 6000 and applies it on three different HAADF-STEM images, which demonstrate the methodology. The software, as well as other resources, are available at http://automal.org. The source code is also directly available from https://github.com/Haawk666/AutomAl-6000.