Boundary Character Research Articles

Effect of chromium doping on the grain boundary character of WC-Co

The life of cutting tool inserts is critically important for efficient machining, reducing manufacturing cost, embedded energy, and enabling more complex parts to be machined. For these applications, cemented carbide (WC-Co) materials are a prime candidate. The performance of these materials can be limited by early fracture, typically via an intergranular fracture path with respect to carbide grains. This motivates further studies to understand the character of the grain boundary network so that grain boundary engineering (GBE) of WC-Co tools can be used to improve tool life and performance. In this work, we have used Rohrer et al.'s five-parameter grain boundary character distribution (GBCD) analysis to examine the grain boundary network of WC-10wt%Co and WC-10wt%Co-1wt%Cr samples (Rohrer et al., 2004a [1]). It was found that the measured area fraction of the Σ2 boundaries was comparable to the values reported in the literature despite the relatively larger grain sizes (~14 μm) and higher cobalt contents. The result suggests that chromium doping increases the area fraction of Σ2 boundaries from 12.8 % to 14.8 %. It is proposed that this is a consequence of altering the Σ2 boundary energy, as associated with adding chromium.

Bubble boundary R-CNN: A multitask model for segmenting and reconstructing overlapping bubbles

When measuring the dynamic characteristics of bubbles using image-based methods, the images including complex overlapping of bubbles with irregular shape pose a huge challenge to current bubble reconstruction algorithms. Based on the Mask R-CNN architecture, a multitask model “Bubble Boundary R-CNN” is proposed for detection, segmentation and shape reconstruction of overlapping bubbles with high deformation at high gas velocities. By leveraging shared deep features from detection and segmentation, model achieves flexible bubble reconstruction and accurate size distribution extraction from high gas velocity bubble flows. To overcome the limitations of Mask R-CNN and address the challenges posed by high-deformation and overlapping bubbles, a semantic segmentation auxiliary head and the Boundary-preserving Mask Head (BMask Head) are introduced to enhance edge information and extract bubble boundary features. Additionally, an Overlapping Boundary Prediction Network (OBPN) is proposed to reconstruct boundary features that are lost due to bubble occlusion and predict the complete bubble shapes. The optimized model shows its ability to flexibly and accurately reconstruct overlapped and deformed bubbles by varying occlusion rates and circularity. The new model has also proven its reliability when applied to bubble images from a bubble column at gas velocities of up to 0.067 m3/s. In future work, more efforts are needed to further enhance the detection capability of the model and reduce missed and multiple detections at high gas velocity.

Salt dome identification using incremental semi-supervised learning and unsupervised learning-based label generation

Salt domes represent distinctive geological anomalies in seismic data, crucial for pinpointing hydrocarbon reservoirs and strategizing drilling paths. Conventional seismic attributes or computer vision methods usually fail to capture the intricate details of salt domes, resulting in interpretation results marred by noise. While deep learning presents a promising approach for intelligent 3D salt dome interpretation, its effectiveness is heavily dependent on the availability of labeled samples. To facilitate accurate interpretation, we propose an innovative workflow that integrates an unsupervised label generation component with an incremental semi-supervised learning framework utilizing the U-Net architecture. To generate salt dome labels, we prioritize both the root mean square (RMS) amplitude attribute and variance attribute (VA) as foundational data. Utilizing convolutional autoencoders (CAE), we establish a relationship between the input RMS attribute and the output reconstructed attribute. The intermediate features extracted by CAE are transformed into the salt boundary feature via principal component analysis and K-Means clustering. Concurrently, we employ K-Means clustering on VA to ascertain the salt internal feature. We further propose a feature aggregation method to consolidate the salt boundary feature and the salt internal feature for label generation of the salt dome. For 3D salt dome interpretation, we begin by predicting adjacent test datasets using labels generated by the unsupervised salt dome label generation module. The prediction results of these test datasets are then integrated into the training datasets to enhance the interpretation performance of U-Net, steering it towards an incremental semi-supervised learning method for salt dome interpretation. Additionally, we extend this research by applying transfer learning techniques for identifying mound-shoals using the same semi-supervised model parameters initially developed for interpreting salt domes. This method is validated using datasets from the Netherlands F3 block for salt domes and the North China block for mound-shoals. The results demonstrate that this innovative process only requires a minimal number of labels from unsupervised methods to precisely interpret salt domes across 3D seismic data. Furthermore, the low-level features of salt domes learned from neural network can be seamlessly transferred to mound-shoal identification. This automated approach significantly streamlines the interpretation process, reducing the time and resources traditionally necessary for reservoir analysis.

SPFDNet: Water Extraction Method Based on Spatial Partition and Feature Decoupling

Extracting water information from remote-sensing images is of great research significance for applications such as water resource protection and flood monitoring. Current water extraction methods aggregated richer multi-level features to enhance the output results. In fact, there is a difference in the requirements for the water body and the water boundary. Indiscriminate multi-feature fusion can lead to perturbation and competition of information between these two types of features during the optimization. Consequently, models cannot accurately locate the internal vacancies within the water body with the external boundary. Therefore, this paper proposes a water feature extraction network with spatial partitioning and feature decoupling. To ensure that the water features are extracted with deep semantic features and stable spatial information before decoupling, we first design a chunked multi-scale feature aggregation module (CMFAM) to construct a context path for obtaining deep semantic information. Then, an information interaction module (IIM) is designed to exchange information between two spatial paths with two fixed resolution intervals and the two paths through. During decoding, a feature decoupling module (FDM) is developed to utilize internal flow prediction to acquire the main body features, and erasing techniques are employed to obtain boundary features. Therefore, the deep features of the water body and the detailed boundary information are supplemented, strengthening the decoupled body and boundary features. Furthermore, the integrated expansion recoupling module (IERM) module is designed for the recoupling stage. The IERM expands the water body and boundary features using expansion and adaptively compensates the transition region between the water body and boundary through information guidance. Finally, multi-level constraints are combined to realize the supervision of the decoupled features. Thus, the water body and boundaries can be extracted more accurately. A comparative validation analysis is conducted on the public datasets, including the gaofen image dataset (GID) and the gaofen2020 challenge dataset (GF2020). By comparing with seven SOTAs, the results show that the proposed method achieves the best results, with IOUs of 91.22 and 78.93, especially in the localization of water bodies and boundaries. By applying the proposed method in different scenarios, the results show the stable capability of the proposed method for extracting water with various shapes and areas.

Evaluation and optimization of park cooling benefits based on cumulative impact and landscape pattern

City parks can cool the surrounding environment and mitigate the urban heat island (UHI) effect, considerable improving the city’s adaptability to climate. In this study, 20 city parks in Nanjing, China, were considered, and four indexes for quantifying the cooling benefits from a cumulative impact perspective were proposed. These indexes are park cooling area (PCA), park cooling efficiency (PCE), park cooling intensity (PCI), and park cooling gradient (PCG). The results reveal the following: first, city parks have a positive impact on the surrounding thermal environment. The factors park area (PA), park perimeter (PP), landscape shape index (LSI), and normalized difference vegetation index (NDVI) determine cooling benefits. Second, PA and PP are significantly positively correlated with PCA but are significantly negatively correlated with PCE. LSI is negatively correlated with PCE, while NDVI is positively correlated with PCI and PCG. No significant correlation exists between the four cooling indexes and modified normalized difference water index(MNDWI). Finally, different parks exhibit variations in their ability to provide cooling benefits. Special or community parks are more appropriately situated in areas with constrained urban land resources. In designing comprehensive parks, the intricate boundary features and vegetation conditions need to be considered to optimize their cooling effects. Moreover, a larger number of residents are allowed to enjoy cooling services. The findings of this project will aid in the construction and optimization of city parks in future to combat the UHI effect.

Electron Backscatter Diffraction Analysis of Low-Misorientation-Angle Boundary and High-Energy Boundary in the Hot-Rolled Plate of Grain-Orientated Silicon Steel

In order to study the texture evolution and the formation of an inhomogeneous microstructure in hot-rolled plate of grain-orientated silicon steel, Fe3C (hexagonal) and ferrite phases in the subsurface layer were studied using electron backscatter diffraction. The results indicate that fiber texture (ferrite) mainly composed of {441}<104> and (110)[001] Goss oriented grains was formed at a depth of 25% of the thickness of hot-rolled plate. Matrix grains in the subsurface layer were arbitrary separated into irregular large grains (≥40 μm) and fine grains (<40 μm), and the grain boundary characteristics and texture evolution of matrix grains were studied. The results indicated that the formation of the colonies of fine grains was the result of dynamic recrystallization, and high-frequency low-misorientation-angle boundaries (0~20°) were formed between large grains (≥40 μm) and fine grains (<40 μm), which can be considered as the irregularity of large grains caused by solid-state wetting. Due to the texture evolution of large grains (≥40 μm), a large number of high-energy boundaries (20~45°) were formed between irregular large grains (≥40 μm), resulting in rapid consumption between adjacent large grains and the elongation of large grains along the rolling direction. Therefore, it can be assumed that the migration of low-misorientation-angle boundaries (0~20°) under solid-state wetting and high-energy boundaries (20~45°) are important mechanisms for non-uniform grain growth in hot-rolled plate of grain-orientated silicon steel.

BIF-Net: Boundary information fusion network for abdominal aortic aneurysm segmentation

The accurate abdominal aortic aneurysm (AAA) segmentation is significant for assisting clinicians in diagnosis and treatment planning. However, existing segmentation methods exhibit a low utilization rate for the semantic information of vessel boundaries, which is disadvantageous for segmenting AAA with significant scale variability of vessel diameter (diameter ranges from 4 mm to 85 mm). To tackle this problem, we introduce a boundary information fusion network (BIF-Net) specially designed for AAA segmentation. BIF-Net initially constructs convolutional kernel functions based on Gabor and Sobel operators, enriching the global semantic features and localization information through the Gabor and Sobel dilated convolution (GSDC) module. Additionally, BIF-Net supplements lost boundary feature information during the sampling process through the guided filtering feature supplementation (GFFS) module and the channel-spatial attention module (CSAM), enhancing the ability to capture targets with shape diversity and boundary features. Finally, we introduce a boundary feature loss function to alleviate the impact of the imbalance between positive and negative samples. The results demonstrate that BIF-Net outperforms current state-of-the-art methods across multiple evaluation metrics, achieving the highest Dice similarity coefficient (DSC) accuracies of 93.29 % and 91.01 % on the preoperative and postoperative datasets, respectively. Compared to the state-of-the-art methods, BIF-Net improves DSC by 6.86 % and 3.85 %. Due to the powerful boundary feature extraction ability, the proposed BIF-Net is a competitive AAA segmentation method exhibiting significant potential for application in diagnosis and treatment processes of AAA.

Influence of heat treatment temperature on grain structures and properties of a Ni-based superalloy fabricated via selective laser melting

This paper investigated the grain structures and mechanical properties of GH3625 alloy fabricated by Selective Laser Melting （SLM）technology at different heat treatment temperatures. GH3625 alloy samples were fabricated using SLM and subjected to heat treatment at different temperatures. Microstructural characterization was conducted using Optical Microscopy (OM), Scanning Electron Microscopy (SEM), and Electron Backscatter Diffraction (EBSD). Tensile tests were also performed to evaluate the mechanical properties. It was found that the heat treatment temperature significantly affects the microstructure of SLM-fabricated GH3625 alloy. As the temperature increases from 950°C to 1100°C, the grains gradually become uniform equiaxed morphologies with weakened grain preferred orientation, increased grain size, reduced average misorientation, and increased proportion of high-angle grain boundaries. The transformation of microstructure leads to a decrease in tensile strength and an increase in plasticity of the alloy. Samples treated at 950°C have higher tensile strength,while at 1100°C exhibits higher plasticity. The study shows that by controlling the heat treatment temperature, the grain size, grain boundary characteristics, and dislocation density of SLM GH3625 alloy can be adjusted, thereby affecting the mechanical properties of the alloy, providing important technical support for its application in high-performance fields.

Fast communication-aided protection scheme for half-bridge MMC-based Multi-terminal HVDC grids

The protection of half-bridge modular multilevel converter-based (MMC) HVDC grids demands balancing selectivity, sensitivity, and robustness while ensuring fast operation. However, existing single-ended protection schemes often struggle with balancing selectivity and sensitivity, while double-ended schemes face challenges in achieving both speed and sensitivity. This study investigates the theoretical characteristics of first fault current traveling waves (FFCTWs) as they propagate along the faulted and non-faulted lines within a grid. Theoretical analysis reveals unique mode-1 FFCTW characteristics at faulted line ends over that of non-faulted ones, overcoming the shortcomings in methods based on the similarity of FFCTWs in ring grids. Protection criteria implementation utilizes directional morphological features of mode-1 currents at the line ends and of local poles’ currents for fault discrimination and classification, respectively. The proposed method avoids certain conditions like strong boundary features or line parameters to be applicable and can operate effectively even under measurement anomalies. To validate the proposed protection scheme, a meshed MMCHVDC grid is modeled using the PSCAD software. Simulation studies verify the selectivity and sensitivity of the scheme in case of various types of low and high-impedance faults, as well as robustness against non-fault operating conditions. Moreover, the applicability of the proposed method is assessed as primary protection and in comparison to state-of-the-art protection methods.

Simulated hydrogen diffusion in diamond grain boundaries

To evaluate hydrogen diffusion within diamond, a series of molecular dynamics simulations have been carried out in which diffusion coefficients and activation energies were determined. Diamond grown via chemical vapour deposition (CVD) contains a high hydrogen concentration within grain boundaries, because of this, common tilt grain boundaries were recreated from transmission electron microscopy images taken from literature. Diffusion coefficients of hydrogen placed within the grain boundary were estimated and compared to the bulk diffusion. Unlike many crystalline structures, some grain boundaries presented limited diffusion when compared to the bulk. Diffusion characteristics of grain boundaries are thought to be a result of channelling effects combined with the formation of sp3C-H bonds with sp2 carbon present within some grain boundaries - increasing and decreasing diffusion rates respectively. Potential wells were observed across some but not all the grain boundaries resulting in hydrogen trapping and anisotropic diffusion.

Enhancing mechanical property and corrosion resistance of Al0.3CoCrFeNi1.5 high entropy alloy via grain boundary engineering

In the present study, to improve the performances of Al0.3CoCrFeNi1.5 high entropy alloys (HEAs), grain boundary character distribution (GBCD) of Al0.3CoCrFeNi1.5 HEA has been optimized by an appropriate thermo-mechanical processing. The experiment results showed that the fraction of low-Σ coincidence site lattice (CSL) boundaries could reach approximately 80 % through cold rolling with deformation of 8 % and subsequent annealing at 1050 °C for 5 min. The reason for GBCD optimization could be attributed to sufficient strain-induced boundary migration (SIBM) or grain growth after recrystallization. While recrystallization is not favorable for optimizing GBCD. The mechanical properties and corrosion resistance have been enhanced, with a more pronounced improvement observed in the corrosion resistance. The corrosion current density icorr of the GBEM specimen stands at 0.23 μA∙cm−2, representing a reduction of 66 % in comparison to the BM specimen (0.68 μA∙cm−2). The improvement of corrosion resistance of Al0.3CoCrFeNi1.5 HEA resulted from the discontinuous random grain boundaries (RGBs) broken by the high fraction of low-ΣCSL boundaries, especially Σ3 boundaries suppressed the propagation of corrosion crack.

Enhanced framework for solving general energy equations based on metropolis-hasting Markov chain Monte Carlo

Due to the widespread presence of heat and mass transfer phenomena in industrial applications, numerous studies have been devoted to the accurate solution of energy equations, providing a foundation for the analysis of heat and mass transfer processes in practical applications. In this study, a Green's Function Markov Superposition Monte Carlo (GMSMC) for solving general energy equations has been developed based on probability and statistical principles owing to its advantageous features of insensitivity towards dimension and geometric complexity as well as the capability to handle multiple integrals in complex domains. The energy equation is first decomposed, and corresponding probability models are established for each component, considering their interrelationships. Subsequently, a solution framework for solving the general energy equation is constructed by integrating these probability models based on a Markov chain structure. The mathematical principles and formulae of the proposed method are derived in detail. The performance of the proposed method is validated by several heat transfer systems with different combinations of boundary conditions and features, which mainly include the distribution of the internal heat source and whether the convection or transient term is included. Results of the validation show that the temperatures obtained by the proposed method are in good agreement with the FEM based on a fine grid, no matter whether the calculation is for a single point or a distribution.

Effect of Cu content and quenching rate on the intergranular corrosion behavior of Al-Zn-Mg-Cu alloys

The effect of Cu content and quenching rate on the intergranular corrosion behavior of Al-Zn-Mg-Cu alloys has been investigated. The study reveals that at the same quenching rate, the intergranular corrosion susceptibility increases with the elevation of Cu content. Meanwhile, for alloys containing the same Cu content, the intergranular corrosion susceptibility increases as the quenching rate decreases. Remarkably, at high quenching rates, primarily pitting corrosion occurs. However, once the quenching rate falls below a specific threshold, the corrosion tends to propagate along grain boundaries. The critical quenching rate required for this transition increases with the increase of Cu content. Both the corrosion initiation and propagation are related to the Volta potential difference between grain boundary precipitates and the matrix (VPDG), which escalates correspondingly with the elevation of Cu content or the reduction of quenching rate. The intergranular corrosion behavior has been discussed through a comprehensive analysis encompassing VPDG, the features of grain boundary precipitates and precipitate-free zones.

Development and morphological analysis of the zone refining process for high purity germanium

A highly efficient purification process has been developed to enhance the purity of germanium (Ge) up to 13 N from an initial 4N purity. Hall effect measurements demonstrated that part of the zone refined Ge (ZRGe) bars exhibited p-type conductivity and high purity with a net charge carrier concentration of around 4–9 × 1010 cm−3. Noteworthy observations include a rise in carrier concentration (1011 to 1012 cm−3) at a very low zone refining (ZR) velocity of 1 mm/min, which is attributed to the diffusion of impurities from the container into the Ge melt. The purification efficiency was enhanced by optimizing the ZR velocity to around 4–5 mm/min. Purification was carried out at different process steps using graphite and quartz containers. Subsequent microstructural examinations, conducted in conjunction with wet-chemical etching of the sample unveiled the presence of various grains, grain boundaries (GBs), and low & high-density of dislocation clusters: These observations were made in samples that had undergone zone refining under various gas atmospheres including argon, nitrogen and hydrogen. Electron back scattered diffraction (EBSD) analysis reveals the characteristics of multiple grains and grain boundaries (GBs) of Ge refined in argon and nitrogen atmosphere, including low-angle GBs, high-angle GBs with coincide site lattice (CSL) Σ3, Σ9, Σ27a, and random angle GBs. In contrast, the EBSD results of Ge refined in hydrogen atmosphere shows larger grains with only a few low-angle grain boundaries. The materials refined in argon and nitrogen show a lower carrier lifetime (10–30 μs) owing to the presence of various high angle grains boundaries as well as low purity. Conversely, zone refining in hydrogen resulted in the carrier lifetime of around 50 μs in polycrystalline Ge wafers with larger grains.

Boundary-aware convolutional attention network for liver segmentation in ultrasound images

Liver ultrasound is widely used in clinical practice due to its advantages of non-invasiveness, non-radiation, and real-time imaging. Accurate segmentation of the liver region in ultrasound images is essential for accelerating the auxiliary diagnosis of liver-related diseases. This paper proposes BACANet, a deep learning algorithm designed for real-time liver ultrasound segmentation. Our approach utilizes a lightweight network backbone for liver feature extraction and incorporates a convolutional attention mechanism to enhance the network's ability to capture global contextual information. To improve early localization of liver boundaries, we developed a selective large kernel convolution module for boundary feature extraction and introduced explicit liver boundary supervision. Additionally, we designed an enhanced attention gate to efficiently convey liver body and boundary features to the decoder to enhance the feature representation capability. Experimental results across multiple datasets demonstrate that BACANet effectively completes the task of liver ultrasound segmentation, achieving a balance between inference speed and segmentation accuracy. On a public dataset, BACANet achieved a DSC of 0.921 and an IOU of 0.854. On a private test dataset, BACANet achieved a DSC of 0.950 and an IOU of 0.907, with an inference time of approximately 0.32 s per image on a CPU processor.

Enhanced comprehensive mechanical properties of laser additive manufactured TC17 by design of new heat treatment based on continuous cooling transition

Heat treatment is critical for enhancing the mechanical properties of high strength titanium alloys, especially for exploiting the potential of laser additive manufactured titanium alloys. In this work, the influence of the cooling rate of continuous cooling transition on microstructure evolution and mechanical behavior was investigated in TC17 titanium alloy fabricated by laser directed energy deposition (LDED) technology. It was found that the number density and orientation characteristics of grain boundary α phases (αGB) are jointly influenced by the cooling rate and the structure of β/β grain boundaries (GBs). The average number density (λavg) of αGB has a consistent trend with the degree of variant selection (DVS) for precipitated α clusters subsequently, which is attributed to the autocatalytic effect of the pre-existing α on the post-precipitated α phases. The largest λavg of αGB and the highest DVS of α clusters could be simultaneously obtained at a suitable cooling rate (4 °C/min). In that case, plenty of α/β phase interfaces and dominant variant type ensure high strength, meanwhile, the combinations of activated multi-slip systems and varied crack propagation paths extend work-hardening to maintain greater plastic deformation. This paper provides a novel thought for designing customized heat treatments of LDEDed high strength titanium alloys, and more importantly, promotes the engineering applications of large and complex components prepared by additive manufacturing technology.

Unraveling impacts of polycrystalline microstructures on ionic conductivity of ceramic electrolytes by computational homogenization and machine learning

The ionic conductivity at the grain boundaries (GBs) in oxide ceramics is typically several orders of magnitude lower than that within the grain interior. This detrimental GB effect is the main bottleneck for designing high-performance ceramic electrolytes intended for use in solid-state lithium-ion batteries, fuel cells, and electrolyzer cells. The macroscopic ionic conductivity in oxide ceramics is essentially governed by the underlying polycrystalline microstructures where GBs and grain morphology go hand in hand. This provides the possibility to enhance the ion conductivity by microstructure engineering. To this end, a thorough understanding of microstructure–property correlation is highly desirable. In this work, we investigate numerous polycrystalline microstructure samples with varying grain and grain boundary features. Their macroscopic ionic conductivities are numerically evaluated by the finite element homogenization method, whereby the GB resistance is explicitly regarded. The influence of different microstructural features on the effective ionic conductivity is systematically studied. The microstructure–property relationships are revealed. Additionally, a graph neural network-based machine learning model is constructed and trained. It can accurately predict the effective ionic conductivity for a given polycrystalline microstructure. This work provides crucial quantitative guidelines for optimizing the ionic conducting performance of oxide ceramics by tailoring microstructures.

Effect of Grain Boundary Character Distribution on the Precipitation Behavior: A Comparative Study for 304 Steel and T91 Steel

Effect of Grain Boundary Character Distribution on the Precipitation Behavior: A Comparative Study for 304 Steel and T91 Steel

Refining soil nutrient assessment: Incorporating land use boundaries for precision agriculture.

Soil nutrient levels play a crucial role in determining crop yield. A comprehensive understanding of the spatial distribution patterns and evaluation grades of soil nutrients is of significant practical importance for informed fertilization practices, enhancing crop production, and optimizing agricultural land utilization. This study focuses on the urban area of Kashi Prefecture in Xinjiang as a case study. Utilizing soil sample data, GIS spatial interpolation analysis was conducted, incorporating plot boundary information to propose a comprehensive evaluation method for assessing soil nutrient levels at the plot level. Experimental findings revealed the following: (1) The average values of soil organic matter (SOM), total nitrogen (AN), total potassium (AK), and total phosphorus (AP) in the study area were determined to be 13.3 g/kg, 0.74 g/kg, 0.33 g/kg, and 0.03 g/kg, respectively. Among these, AN and SOM were classified as the fourth grade, indicating relatively deficient levels, while AK and AP were classified as the first and second grade, indicating relatively abundant levels. (2) The comprehensive evaluation of soil nutrient grades in the study area primarily fell within the third, fourth, and second grades, representing areas of 29.08 km2, 25 km2, and 4.05 km2, accounting for 50.03%, 43%, and 6.97% of the total area, respectively. (3) The evaluation results of soil nutrient levels at the plot level emphasized the boundary characteristics and provided a more refined assessment grade. This evaluation method is better suited to meet the practical production requirements of farmers and is considered feasible. The outcomes of this study can serve as a reference for precision agriculture management.

Determination of five-parameter grain boundary characteristics in nanocrystalline Ni-W by scanning precession electron diffraction tomography

Determining the full five-parameter grain boundary characteristics from experiments is essential for understanding grain boundaries impact on material properties, improving related models, and designing advanced alloys. However, achieving this is generally challenging, in particular at nanoscale, due to their 3D nature. In our study, we successfully determined the grain boundary characteristics of an annealed nickel-tungsten alloy (NiW) nanocrystalline needle-shaped specimen (tip) containing twins using Scanning Precession Electron Diffraction (SPED) Tomography. The presence of annealing twins in this face-centered cubic (fcc) material gives rise to common reflections in the SPED diffraction patterns, which challenges the reconstruction of orientation-specific virtual dark field (VDF) images required for tomographic reconstruction of the 3D grain shapes. To address this, an automated post-processing step identifies and deselects these shared reflections prior to the reconstruction of the VDF images. Combined with appropriate intensity normalization and projection alignment procedures, this approach enables high-fidelity 3D reconstruction of the individual grains contained in the needle-shaped sample volume. To probe the accuracy of the resulting boundary characteristics, the twin boundary surface normal directions were extracted from the 3D voxelated grain boundary map using a 3D Hough transform. For the sub-set of coherent Σ3 boundaries, the expected {111} grain boundary plane normals were obtained with an angular error of <3° for boundary sizes down to 400 nm². This work advances our ability to precisely characterize and understand the complex grain boundaries that govern material properties.