Immersive Analysis: Enhancing Material Inspection of X-Ray Computed Tomography Datasets in Augmented Reality

TL;DR

The paper tackles the challenge of analyzing complex, high‑dimensional XCT data for materials on-site by introducing an AR framework that overlays primary XCT volumes and derived secondary data onto real objects using a Hololens 2. The approach combines immersive visualization (volume rendering and abstract charts) with embodied interaction and gaze‑driven data loading within a Unity‑based, open‑source platform, enabling in‑place material inspection. A case study on fiber‑reinforced polymers and a qualitative user study with domain experts demonstrate improved spatial understanding and natural interaction, while also highlighting hardware and scalability limitations. The work advances immersive analytics in materials science by delivering an end‑to‑end AR workflow that bridges physical samples with complex XCT data and sets the stage for integration with conventional analysis tools.

Abstract

This work introduces a novel Augmented Reality (AR) approach to visualize material data alongside real objects in order to facilitate detailed material analyses based on spatial non-destructive testing (NDT) data as generated in X-ray computed tomography (XCT) imaging. For this purpose, we introduce a framework that leverages the potential of AR devices, visualization and interaction techniques to seamlessly explore complex primary and secondary XCT data matched with real-world objects. The overall goal of the proposed analysis scheme is to enable researchers and analysts to inspect material properties and structures onsite and in-place. Coupling immersive visualization techniques with real physical objects allows for highly intuitive workflows in material analysis and inspection, which enables the identification of anomalies and accelerates informed decision making. As a result, this framework generates an immersive experience, which provides a more engaging and more natural analysis of material data. A case study on fiber-reinforced polymer datasets was used to validate the AR framework and its new workflow. Initial results revealed positive feedback from experts, in particular regarding improved understanding of spatial data and a more natural interaction with material samples, which may have significant potential when combined with conventional analysis systems.

Figures (4)

• Figure 1: The AR HMD uses the stored shape to detect the material. The image of the sample captured by XCT is displayed alongside the physical sample and moves in sync with it. A traditional analysis can still be carried out via monitors in the background.
• Figure 2: Visualization techniques for analyzing abstract data: The user in this example utilizes histograms, bar charts, a distribution plot, and a three-dimensional scatterplot to display different fiber characteristics, and has arranged these visualizations in the workspace.
• Figure 3: The material is recognized by tracking the image target, which automatically loads the associated data. The XCT scan and model-based surface representation are both displayed and synchronized with the position and orientation of the material sample.
• Figure 4: After recognizing the dataset, different visualization techniques can be utilized. In this case, two histograms (top) and a 3D scatterplot (bottom) are used to analyze, e.g., the lengths and diameters of the fibers.