Jisoo Kim

Last Name

Kim

First Name

Jisoo

ORCID

0000-0002-2313-6037

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Publications 1 - 9 of 9

Fast acquisition protocol for X-ray scattering tensor tomography
Kim, Jisoo; Kagias, Matias; Marone, Federica; et al. (2021)
Scientific Reports
Microstructural information over an entire sample is important to understand the macroscopic behaviour of materials. X-ray scattering tensor tomography facilitates the investigation of the microstructural organisation in statistically large sample volumes. However, established acquisition protocols based on scanning small-angle X-ray scattering and X-ray grating interferometry inherently require long scan times even with highly brilliant X-ray sources. Recent developments in X-ray diffractive optics towards circular pattern arrays enable fast single-shot acquisition of the sample scattering properties with 2D omnidirectional sensitivity. X-ray scattering tensor tomography with the use of this circular grating array has been demonstrated. We propose here simple yet inherently rapid acquisition protocols for X-ray scattering tensor tomography leveraging on these new optical elements. Results from both simulation and experimental data, supported by a null space analysis, suggest that the proposed acquisition protocols are not only rapid but also corroborate that sufficient information for the accurate volumetric reconstruction of the scattering properties is provided. The proposed acquisition protocols will build the basis for rapid inspection and/or time-resolved tensor tomography of the microstructural organisation over an extended field of view.
Towards Time-resolved X-ray Scattering Tensor Tomography
Kim, Jisoo (2022)
Macroscopic mapping of microscale fibers in freeform injection molded fiber-reinforced composites using X-ray scattering tensor tomography
Kim, Jisoo; Slyamov, Azat; Lauridsen, Erik M.; et al. (2022)
Composites Part B: Engineering
Fiber-reinforced composites deliver lightweight but strong structures that are crucial in applications ranging from aerospace to the automotive industry. The advent of freeform injection molding has made the manufacturing of complex fiber-reinforced composites with full design freedom possible. Prediction of the mechanical properties, dictated by the local microfiber orientation, is essential for the performance characterization of fiber-reinforced composites. However, with conventional microtomography, the required microscale spatial resolution and the macroscopic field of view for full-size fiber-reinforced composite pieces cannot be effectively decoupled. X-ray scattering tensor tomography enables non-destructive macroscopic mapping of the local microfiber orientation as well as their degree of alignment. Recent advancements in X-ray optics have significantly increased the acquisition speed, making the tensor tomography attractive for industrial applications. Nonetheless, integration of the tensor tomography within production lines requires a flexible and robust implementation. In this work, we demonstrate the potential of X-ray scattering tensor tomography for industrial applications by characterizing the microstructure of a centimeter-sized industrially relevant freeform injection molding fiber-reinforced composite sample. We also show that the tensor tomography is compatible with robotic arms, which can position and orient objects in three dimensions with high flexibility and therefore are ideal sample manipulators for the tensor tomography in industrial settings. The results obtained with the robotic arm are compared to those obtained with the state-of-the-art 2-axis sample manipulation scheme. The retrieved information is highly consistent and shows agreement also with structure tensor analyses of conventional microtomography data taken at selected regions of the sample for additional validation.
Robust dark-field signal extraction for modulation-based x-ray tensor tomography
Lautizi, Ginevra; Di Trapani, Vittorio; Studer, Alain; et al. (2024)
Applied Physics Letters
We demonstrate a robust signal extraction method for x-ray speckle-based tensor tomography. We validate the effectiveness of the method for several carbon fiber composites, highlighting its potential for industrial applications. The proposed method can be adapted to various acquisition schemes and wavefront-marking optical elements, making it a versatile and robust tool for x-ray scattering tensor tomography.
X-ray scattering tensor tomography based finite element modelling of heterogeneous materials
Auenhammer, Robert M.; Kim, Jisoo; Oddy, Carolyn; et al. (2024)
npj Computational Materials
Among micro-scale imaging technologies of materials, X-ray micro-computed tomography has evolved as most popular choice, even though it is restricted to limited field-of-views and long acquisition times. With recent progress in small-angle X-ray scattering these downsides of conventional absorption-based computed tomography have been overcome, allowing complete analysis of the micro-architecture for samples in the dimension of centimetres in a matter of minutes. These advances have been triggered through improved X-ray optical elements and acquisition methods. However, it has not yet been shown how to effectively transfer this small-angle X-ray scattering data into a numerical model capable of accurately predicting the actual material properties. Here, a method is presented to numerically predict mechanical properties of a carbon fibre-reinforced polymer based on imaging data with a voxel-size of 100 mu m corresponding to approximately fifteen times the fibre diameter. This extremely low resolution requires a completely new way of constructing the material's constitutive law based on the fibre orientation, the X-ray scattering anisotropy, and the X-ray scattering intensity. The proposed method combining the advances in X-ray imaging and the presented material model opens for an accurate tensile modulus prediction for volumes of interest between three to six orders of magnitude larger than those conventional carbon fibre orientation image-based models can cover.
X-ray scattering tensor tomography with circular gratings
Kim, Jisoo; Kagias, Matias; Marone, Federica; et al. (2020)
Applied Physics Letters
Author Correction: Fast Acquisition Protocol for X-Ray Scattering Tensor Tomography
Kim, Jisoo; Kagias, Matias; Marone, Federica; et al. (2022)
Scientific Reports
Tomosipo: fast, flexible, and convenient 3D tomography for complex scanning geometries in Python
Hendriksen, Allard A.; Schut, Dirk; Palenstijn, Willem J.; et al. (2021)
Optics Express
Tomography is a powerful tool for reconstructing the interior of an object from a series of projection images. Typically, the source and detector traverse a standard path (e.g., circular, helical). Recently, various techniques have emerged that use more complex acquisition geometries. Current software packages require significant handwork, or lack the flexibility to handle such geometries. Therefore, software is needed that can concisely represent, visualize, and compute reconstructions of complex acquisition geometries. We present tomosipo, a Python package that provides these capabilities in a concise and intuitive way. Case studies demonstrate the power and flexibility of tomosipo. © 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Tomographic Reconstruction of the Small-Angle X-Ray Scattering Tensor with Filtered Back Projection
Kim, Jisoo; Pelt, Daniël M.; Kagias, Matias; et al. (2022)
Physical Review Applied
Small-angle x-ray scattering tensor tomography provides three-dimensional information on the unre-solved material anisotropic microarchitecture, which can be hundreds of times smaller than an image pixel. We develop a direct filtered back-projection method based on algebraic filters that enables rapid tensor-tomographic reconstructions and is a few orders of magnitude faster compared to established techniques, given the same computational resources. We demonstrate the accuracy of the method on exper-imental data for a fiber-reinforced material sample. The achieved acceleration may pave the way toward the investigation of multiple large samples as well as rapid control and feedback during in situ tensor-tomographic experiments, opening perspectives for the understanding of the fundamental link between functional material properties and microarchitecture.

Publications 1 - 9 of 9

Jisoo Kim

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