Abstract
Recent advancements in neutron and X-ray sources, instrumentation and data collection modes have significantly increased the experimental data size (which could easily contain 108–1010 data points), so that conventional volumetric visualization approaches become inefficient for both still imaging and interactive OpenGL rendition in a 3D setting. We introduce a new approach based on the unsupervised machine learning algorithm, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), to efficiently analyze and visualize large volumetric datasets. Here we present two examples of analyzing and visualizing datasets from the diffuse scattering experiment of a single crystal sample and the tomographic reconstruction of a neutron scanning of a turbine blade. We found that by using the intensity as the weighting factor in the clustering process, DBSCAN becomes very effective in de-noising and feature/boundary detection, and thus enables better visualization of the hierarchical internal structures of the neutron scattering data.
This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
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Notes
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Paraview – https://www.paraview.org/.
- 2.
Tomviz – https://tomviz.org/.
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Acknowledgment
We first thank Dr. Hassina Z. Bilheux (Neutron Scattering Division, ORNL) for providing the Turbine dataset used in this article, and Dr. Jean-Christophe Bilheux (Neutron Scattering Division, ORNL) for discussions and illustrations of functionalities of tomography visualization in VGStudio. We also thank our colleagues, Mr. Eric Lingerfelt (EarthCube Science Support Office, UCAR) and Dr. Christina Hoffmann (Neutron Scattering Division, ORNL) for insightful discussions. This research used resources of the Oak Ridge Leadership Computing Facility (OLCF) and the Compute and Data Environment for Science (CADES) at ORNL, which are supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. Research conducted at ORNL’s Spallation Neutron Source and the High Flux Isotope Reactor was sponsored by the Scientific User Facilities Division, Office of Basic Energy Sciences, U.S. Department of Energy.
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Hui, Y., Liu, Y. (2020). Volumetric Data Exploration with Machine Learning-Aided Visualization in Neutron Science. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-17795-9_18
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