Improved grain mapping by laboratory X-ray diffraction contrast tomography

Postprocessing Workflow for Laboratory Diffraction Contrast Tomography: A Case Study on Chromite Geomaterials

Texture stands as a fundamental descriptor in the realms of geology and earth and planetary science. Beyond offering insights into the geological processes underlying mineral formation, its characterization plays a pivotal role in advancing engineering applications, notably in mining, mineral processing, and metal extraction, by providing quantitative data for predictive modeling. Laboratory diffraction contrast tomography (LabDCT), a recently developed 3D characterization technique, offers nondestructive measurement of grain phases including their morphology, distribution, and crystal orientation. It has recently shown its potential to assess 3D textures in complex natural rock samples. This study looks at improving on previous work by examining the artifacts and presents a novel postprocessing workflow designed to correct them. The workflow is developed to rectify inaccurate grain boundaries and interpolate partially reconstructed grains to provide more accurate results and is illustrated using multi-scan examples on chromite sands and natural chromitite from the Upper Group 2 Reef layer in South Africa. The postcorrected LabDCT results were validated through qualitative and quantitative assessment using 2D electron back-scattered diffraction on polished sample surfaces. The successful implementation of this postprocessing workflow underscores its substantial potential in achieving precise textural characterization and will provide valuable insights for both earth science and engineering applications.

Implementation of grain mapping by diffraction contrast tomography on a conventional laboratory tomography setup with various detectors

Laboratory-based diffraction contrast tomography (LabDCT) is a novel technique used to resolve grain orientations and shapes in three dimensions at the micrometre scale using laboratory X-ray sources, allowing the user to overcome the constraint of limited access to synchrotron facilities. To foster the development of this technique, the implementation of LabDCT is illustrated in detail using a conventional laboratory-based X-ray tomography setup, and it is shown that such implementation is possible with the two most common types of detectors: CCD and flat panel. As a benchmark, LabDCT projections were acquired on an AlCu alloy sample using the two types of detectors at different exposure times. Grain maps were subsequently reconstructed using the open-source grain reconstruction method reported in the authors' previous work. To characterize the detection limit and the spatial resolution for the current implementation, the reconstructed LabDCT grain maps were compared with the map obtained from a synchrotron measurement, which is considered as ground truth. The results show that the final grain maps from measurements by the CCD and flat panel detector are similar and show comparable quality, while the CCD gives a much better contrast-to-noise ratio than the flat panel. The analysis of the grain maps reconstructed from measurements with different exposure times suggests that a grain map of comparable quality could be obtained in less than 1 h total acquisition time without a significant loss of grain reconstruction quality and indicates a clear potential for time-lapse LabDCT experiments. The current implementation is suggested to promote the generic use of the LabDCT technique for grain mapping on conventional tomography setups.

Reconstruction algorithms for grain mapping by laboratory X-ray diffraction contrast tomography

X-ray-based non-destructive 3D grain mapping techniques are well established at synchrotron facilities. To facilitate everyday access to grain mapping instruments, laboratory diffraction contrast tomography (LabDCT), using a laboratory-based conical polychromatic X-ray beam, has been developed and commercialized. Yet the currently available LabDCT grain reconstruction methods are either ill-suited for handling a large number of grains or require a commercial licence bound to a specific instrument. To promote the availability of LabDCT, grain reconstruction methods have been developed with multiple reconstruction algorithms based on both forward and back calculations. The different algorithms are presented in detail and their efficient implementation using parallel computing is described. The performance of different reconstruction methods is assessed on synthetic data. The code to implement all the described algorithms has been made publicly accessible with the intention of fostering the development of grain mapping techniques on widely available laboratory instruments.

Level-Set Modeling of Grain Growth in 316L Stainless Steel under Different Assumptions Regarding Grain Boundary Properties

Two finite element level-set (FE-LS) formulations are compared for the modeling of grain growth of 316L stainless steel in terms of grain size, mean values, and histograms. Two kinds of microstructures are considered: some are generated statistically from EBSD maps, and the others are generated by the immersion of EBSD data in the FE formulation. Grain boundary (GB) mobility is heterogeneously defined as a function of the GB disorientation. On the other hand, GB energy is considered as heterogeneous or anisotropic, which are, respectively, defined as a function of the disorientation and both the GB misorientation and the GB inclination. In terms of mean grain size value and grain size distribution (GSD), both formulations provide similar responses. However, the anisotropic formulation better respects the experimental disorientation distribution function (DDF) and predicts more realistic grain morphologies. It was also found that the heterogeneous GB mobility described with a sigmoidal function only affects the DDF and the morphology of grains. Thus, a slower evolution of twin boundaries (TBs) is perceived.

Three-dimensional grain resolved strain mapping using laboratory X-ray diffraction contrast tomography: theoretical analysis

Laboratory diffraction contrast tomography (LabDCT) is a recently developed technique to map crystallographic orientations of polycrystalline samples in three dimensions non-destructively using a laboratory X-ray source. In this work, a new theoretical procedure, named LabXRS, expanding LabDCT to include mapping of the deviatoric strain tensors on the grain scale, is proposed and validated using simulated data. For the validation, the geometries investigated include a typical near-field LabDCT setup utilizing Laue focusing with equal source-to-sample and sample-to-detector distances of 14 mm, a magnified setup where the sample-to-detector distance is increased to 200 mm, a far-field Laue focusing setup where the source-to-sample distance is also increased to 200 mm, and a near-field setup with a source-to-sample distance of 200 mm. The strain resolution is found to be in the range of 1–5 × 10−4, depending on the geometry of the experiment. The effects of other experimental parameters, including pixel binning, number of projections and imaging noise, as well as microstructural parameters, including grain position, grain size and grain orientation, on the strain resolution are examined. The dependencies of these parameters, as well as the implications for practical experiments, are discussed.

Deep learning for improving non-destructive grain mapping in 3D

Laboratory X-ray diffraction contrast tomography (LabDCT) is a novel imaging technique for non-destructive 3D characterization of grain structures. An accurate grain reconstruction critically relies on precise segmentation of diffraction spots in the LabDCT images. The conventional method utilizing various filters generally satisfies segmentation of sharp spots in the images, thereby serving as a standard routine, but it also very often leads to over or under segmentation of spots, especially those with low signal-to-noise ratios and/or small sizes. The standard routine also requires a fine tuning of the filtering parameters. To overcome these challenges, a deep learning neural network is presented to efficiently and accurately clean the background noise, thereby easing the spot segmentation. The deep learning network is first trained with input images, synthesized using a forward simulation model for LabDCT in combination with a generic approach to extract features of experimental backgrounds. Then, the network is applied to remove the background noise from experimental images measured under different geometrical conditions for different samples. Comparisons of both processed images and grain reconstructions show that the deep learning method outperforms the standard routine, demonstrating significantly better grain mapping.