Feature extraction from Atom Probe Tomography (APT) data is usually performed by repeatedly delineating iso-concentration surfaces of a chemical component of the sample material at different values of concentration threshold, until the user visually determines a satisfactory result in line with prior knowledge. However, this approach allows for important features, buried within the sample, to be visually obscured by the high density and volume (~10(7) atoms) of APT data. This work provides a data driven methodology to objectively determine the appropriate concentration threshold for classifying different phases, such as precipitates, by mapping the topology of the APT data set using a concept from algebraic topology termed persistent simplicial homology. A case study of Sc precipitates in an Al-Mg-Sc alloy is presented demonstrating the power of this technique to capture features, such as precise demarcation of Sc clusters and Al segregation at the cluster boundaries, not easily available by routine visual adjustment.
Identifying nanoscale chemical features from atom probe tomography (APT) data routinely involves adjustment of voxel size as an input parameter, through visual supervision, making the final outcome user dependent, reliant on heuristic knowledge and potentially prone to error. This work utilizes Kernel density estimators to select an optimal voxel size in an unsupervised manner to perform feature selection, in particular targeting resolution of interfacial features and chemistries. The capability of this approach is demonstrated through analysis of the γ / γ' interface in a Ni-Al-Cr superalloy.
Understanding the impact of noise and incomplete data is a critical need for using atom probe tomography effectively. Although many tools and techniques have been developed to address this problem, visualization of the raw data remains an important part of this process. In this paper, we present two contributions to the visualization of data acquired through atom probe tomography. First, we describe the application of a rendering technique, ray-cast spherical impostors, that enables the interactive rendering of large numbers (as large as 10 million plus) of pixel perfect, lit spheres representing individual atoms. This technique is made possible by the use of a consumer-level graphics processing unit (GPU), and it yields an order of magnitude improvement both in render quality and speed over techniques previously used to render spherical glyphs in this domain. Second, we present an interactive tool that allows the user to mask, filter, and colorize the data in real time to help them understand and visualize a precise subset and properties of the raw data. We demonstrate the effectiveness of our tool through benchmarks and an example that shows how the ability to interactively render large numbers of spheres, combined with the use of filters and masks, leads to improved understanding of the three-dimensional (3D) and incomplete nature of atom probe data. This improvement arises from the ability of lit spheres to more effectively show the 3D position and the local spatial distribution of individual atoms than what is possible with point or isosurface renderings. The techniques described in this paper serve to introduce new rendering and interaction techniques that have only recently become practical as well as new ways of interactively exploring the raw data.
