Abstract
The technique of atom probe tomography is often used to image solute clusters and solute atom segregation to dislocation lines in structural alloys. Quantitative analysis, however, remains a common challenge. To address this gap, we combined a cluster finding algorithm, a skeleton finder algorithm, and morphological classification of dense objects to distinguish solute clusters from solute-decorated dislocation lines, both being characterized by high solute atom densities. The proposed workflow is packaged into a graphical user interface available through GitHub. We illustrate its application on a synthetic dataset containing known objects and apply it to an experimental dataset obtained from a proton-irradiated Alloy 625 that contains high densities of Si-decorated dislocations and Si-rich clusters.
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