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Data Challenges of In Situ X-Ray Tomography for Materials Discovery and Characterization

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Book cover Materials Discovery and Design

Part of the book series: Springer Series in Materials Science ((SSMATERIALS,volume 280))

Abstract

Since its development in the 1970s (Hounsfield, Br J Radiol 46(552):1016–1022, 1973) [1], X-ray tomography has been used to study the three dimensional (3D) structure of nearly every type of material of interest to science, both in the laboratory (Elliott and Dover, J Microsc 126(2):211–213, 1982) [2] and at synchrotron facilities (Thompson et al., Nucl Instrum Methods Phys Res 222(1):319–323, 1984) [3]. The ability to nondestructively image internal structures is useful in the medical community for patient diagnosis. For this same reason, it is critical for understanding material structural morphology . X-ray tomography of static materials can generate a true 3D structure to map out content and distribution within materials including voids, cracks, inclusions, microstructure, and interfacial quality. This technology is even more useful when applying a time component and studying the changes in materials as they are subjected to non-equilibrium stimulations. For example, testing mechanical properties (e.g., compressive or tensile loading), thermal properties (e.g., melting or solidification), corrosion, or electrostatic responses, while simultaneously imaging the material in situ, can replicate real world conditions leading to an increase in the fundamental understanding of how materials react to these stimuli. Mechanical buckling in foams, migration of cracks in composite materials, progression of a solidification front during metal solidification, and the formation of sub-surface corrosion pits are just a few of the many applications of this technology. This chapter will outline the challenges of taking a series of radiographs while simultaneously stressing a material, and processing it to answer questions about material properties. The path is complex, highly user interactive, and the resulting quality of the processing at each step can greatly affect the accuracy and usefulness of the derived information. Understanding the current state-of-the-art is critical to informing the audience of what capabilities are available for materials studies, what the challenges are in processing these large data sets, and which developments can guide future experiments. For example, one particular challenge in this type of measurement is the need for a carefully designed experiment so that the requirements of 3D imaging are also met. Additionally, the rapid collection of many terabytes of data in just a few days leads to the required development of automated reconstruction, filtering, segmentation , visualization, and animation techniques. Finally, taking these qualitative images and acquiring quantitative metrics (e.g., morphological statistics ), converting the high quality 3D images to meshes suitable for modeling, and coordinating the images to secondary measures (e.g., temperature, force response) has proven to be a significant challenge when a materials scientist ‘simply’ needs an understanding of how material processing affects its response to stimuli. This chapter will outline the types of in situ experiments and the large data challenges in extracting materials properties information.

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Funding

Funding for the work shown in this chapter are from a variety of LANL sources including: the Enhanced Surveillance Campaign (Tom Zocco), the Engineering Campaign (Antranik Siranosian), DSW (Jennifer Young), and Technology Maturation (Ryan Maupin) in support of the Materials of the Future.

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Authors and Affiliations

  1. Materials Science and Technology Division, Engineered Materials Group, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA

    Brian M. Patterson, Nikolaus L. Cordes & Kevin Henderson

  2. X-ray Photons Sciences, Argonne National Laboratory, Argonne, IL, USA

    Xianghui Xiao

  3. 4D Materials Science Center, Arizona State University, Tempe, AZ, USA

    Nikhilesh Chawla

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  1. Brian M. Patterson
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  2. Nikolaus L. Cordes
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  3. Kevin Henderson
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  4. Xianghui Xiao
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  5. Nikhilesh Chawla
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Correspondence to Brian M. Patterson .

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Editors and Affiliations

  1. Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA

    Turab Lookman

  2. Los Alamos National Laboratory, Los Alamos, NM, USA

    Stephan Eidenbenz

  3. Brookhaven National Laboratory, Brookhaven, NY, USA

    Frank Alexander

  4. Los Alamos National Laboratory, Los Alamos, NM, USA

    Cris Barnes

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Disclaimer: Commercial products are identified in this document in order to specify the experimental procedure and options adequately. Such identification is not intended to imply recommendation or endorsement by LANL or DOE, nor is it intended to imply that the products identified are necessarily the best available for the purpose.

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Patterson, B.M., Cordes, N.L., Henderson, K., Xiao, X., Chawla, N. (2018). Data Challenges of In Situ X-Ray Tomography for Materials Discovery and Characterization. In: Lookman, T., Eidenbenz, S., Alexander, F., Barnes, C. (eds) Materials Discovery and Design. Springer Series in Materials Science, vol 280. Springer, Cham. https://doi.org/10.1007/978-3-319-99465-9_6

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