Boundary Character Research Articles

The Influence of Deformation and Heat Treatment on the Grain Boundary Character Distribution in CoCrFeNi High-Entropy Alloy

The Influence of Deformation and Heat Treatment on the Grain Boundary Character Distribution in CoCrFeNi High-Entropy Alloy

Optimizing high-pressure sliding process parameters for grain refinement and enhanced mechanical properties using response surface methodology approach

Lightweight technology has emerged as a crucial focus within transportation machinery manufacturing, driving the pursuit for materials with exceptional strength-to-weight ratios. To address this need, ultra-fine grain (< 500 nm) and nanostructured (< 100 nm) materials are being developed using severe plastic deformation (SPD) techniques, particularly in confined flat sheets. This study focuses on the Multi Pass Incremental Feed High-Pressure Sliding (MPIF-HPS) technique, which offers significant potential for industries such as aerospace and automotive by enhancing the mechanical properties of materials like AA 7075 alloy. This work investigates the development and characterization of ultrafine-grained materials through SPD, focusing on the correlation between ultrafine grain structures and their distinct properties. Utilizing MPIF-HPS processes and design of experiment (DoE) methodologies, the study evaluates the evolution of fine-grained microstructures and the enhancement of boundary characteristics with increasing strain in AA 7075 alloy. The grain size was refined from 600 µm to 6 µm, indicating significant microstructural improvement. Concurrently, mechanical properties were notably enhanced, with hardness increasing from 102 to 165 HV, yield strength from 250 to 605 MPa, and ultimate tensile strength (UTS) from 400 to 650 MPa. The study systematically optimized processing parameters, including pressure, sliding distance, and number of passes, to determine the ideal conditions for grain refinement and mechanical performance. These findings underscore the critical role of strain-induced boundary characteristics in determining the material's final properties, paving the way for tailored solutions to meet specific industrial requirements.

Evaluation of microstructure, crystallographic texture, crystal orientation, grain boundary characteristics, and mechanical characteristics of dissimilar joints of ASTM A240 and alloy 625 in different welding techniques for high temperature applications

Evaluation of microstructure, crystallographic texture, crystal orientation, grain boundary characteristics, and mechanical characteristics of dissimilar joints of ASTM A240 and alloy 625 in different welding techniques for high temperature applications

Three-Dimensional Non-Uniform Sampled Data Visualization from Multibeam Echosounder Systems for Underwater Imaging and Environmental Monitoring

This paper proposes a method for visualizing three-dimensional non-uniformly sampled data from multibeam echosounder systems (MBESs), aimed at addressing the requirements of monitoring complex and dynamic underwater flow fields. To tackle the challenges associated with spatially non-uniform sampling, the proposed method employs linear interpolation along the radial direction and arc length weighted interpolation in the beam direction. This approach ensures consistent resolution of three-dimensional data across the same dimension. Additionally, an opacity transfer function is generated to enhance the visualization performance of the ray casting algorithm. This function leverages data values and gradient information, including the first and second directional derivatives, to suppress the rendering of background and non-interest regions while emphasizing target areas and boundary features. The simulation and experimental results demonstrate that, compared to conventional two-dimensional beam images and three-dimensional images, the proposed algorithm provides a more intuitive and accurate representation of three-dimensional data, offering significant support for the observation and analysis of spatial flow field characteristics.

A semi-supervised boundary segmentation network for remote sensing images

Accurately segmenting remote sensing images remains challenging due to the diverse target scales and ambiguous structural boundaries. In this work, we propose a semi-supervised boundary segmentation network (BS-GAN) to address these challenges. BS-GAN employs a semi-supervised learning approach to reduce dependency on labeled data while introducing a novel mixed attention (MA) mechanism to enhance segmentation accuracy by aggregating long-range contextual information. Additionally, we develop a Boundary Gating Module (BGM) to refine boundary segmentation through a multi-task learning strategy focused on boundary feature enhancement. Experimental results on three benchmark datasets demonstrate that BS-GAN achieves superior accuracy and generalization capabilities compared to existing segmentation networks.

Relationship between shape memory effect and density of annealing twin boundary in FeMnSiCrNi alloys microalloyed by B or Nb

Previous work showed that a strong negative dependence of shape memory effect (SME) existed on annealing twin boundaries (ATBs) in Fe-Mn-Si-based shape memory alloys (SMAs). Microalloying is expected to improve the SME by inhibiting recrystallization and reducing the density of ATBs. In this study, the effects of 0.01wt.% B or 1wt.% Nb addition on the recrystallization, and the resultant evolutions of grain boundary character distributions and SME were studied in the cold-rolled Fe-Mn-Si-based alloys. The addition of B and Nb obviously improved the SME after annealing at temperatures above 973K due to their impedance of recrystallization and the resultant lower density of ATBs. Independent of the B and Nb microalloying and recrystallization, there still existed a strong negative dependence of SME on the density of ATBs. These findings provide insight into improving the SME of Fe-Mn-Si-based SMAs through microalloying to impede the occurrence of recrystallization and reduce the density of ATBs.

LiteMamba-Bound: A Lightweight Mamba-based Model with Boundary-Aware and Normalized Active Contour Loss for Skin Lesion Segmentation.

In the field of medical science, skin segmentation has gained significant importance, particularly in dermatology and skin cancer research. This domain demands high precision in distinguishing critical regions (such as lesions or moles) from healthy skin in medical images. With growing technological advancements, deep learning models have emerged as indispensable tools in addressing these challenges. One of the state-of-the-art modules revealed in recent years, the 2D Selective Scan (SS2D), based on state-space models that have already seen great success in natural language processing, has been increasingly adopted and is gradually replacing Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). Leveraging the strength of this module, this paper introduces LiteMamba-Bound, a lightweight model with approximately 957K parameters, designed for skin image segmentation tasks. Notably, the Channel Attention Dual Mamba (CAD-Mamba) block is proposed within both the encoder and decoder alongside the Mix Convolution with Simple Attention bottleneck block to emphasize key features. Additionally, we propose the Reverse Attention Boundary Module to highlight challenging boundary features. Also, the Normalized Active Contour loss function presented in this paper significantly improves the model's performance compared to other loss functions. To validate performance, we conducted tests on two skin image datasets, ISIC2018 and PH2, with results consistently showing superior performance compared to other models. Our code will be made publicly available at: urlhttps://github.com/kwanghwi242/A-new-segmentation-model.

Region Growth Method Based on Boundary Characteristics for an Improved Poisson Surface Reconstruction Algorithm

Region Growth Method Based on Boundary Characteristics for an Improved Poisson Surface Reconstruction Algorithm

Positive Effects of Morphological and Perceptual Boundary Factors on Vitality in the Static Activity Streets: Syntactic and Fractal Approaches

Street vitality is a concept that makes the city more liveable with its economic and sociological gains. Static activities on the street contribute to vitality by supporting social interaction. This study investigates the positive effects of the perceptual and morphological boundary characteristics of static activity streets on the user. Using Space Syntax and fractal method, this research consists of two phases and the use of two different scales. The first step involves analysing the connectivity and enclosure factors of 13 static activity streets in Balıkesir city center at the street scale. By correlating the analysis results with the number of users determined by the observation method, the effect of street-scale boundary factors on user behaviour was determined. As a result of the first step, Aygıt Street, which has the highest boundary factor value at the street scale, was selected for the second step of the research, which is the building-scale analysis. The surrounding and permeability factors that create boundary effects at the building scale were analysed through street facades and sections, and the value was evaluated together with the areas preferred by people engaged in static activities. As a result of the correlation analysis between the boundary factor value and user behaviour, it has been determined that the connectivity, which creates a morphological boundary effect at the street scale, and the enclosure factor, which creates a perceptual boundary effect, positively affect the users. It was determined that elements such as awnings, eaves, flower beds, and signboards, which determine the surrounding factor that creates a morphological boundary effect at the building scale, increase the number of users. It is concluded that the wall surfaces on the street facades, which determine the permeability factor that creates a perceptual boundary effect at the building scale, positively affect user behaviour. The model created in this study, which brings together two different approaches, namely urban and architectural scales and morphological and perceptual boundary factors, is intended to form a basis for the design processes of spaces and streets that support static activities in cities.

PCB CT image element segmentation model based on boundary-attention-guided finetuning

Background Computed Tomography (CT) technology is commonly used to realize non-destructive testing of Printed Circuit Board (PCB), and element segmentation is the key link in the process. Although the pretraining and finetuning paradigm alleviates the problem of labeling, PCB CT images are easily affected by uneven grayscale and layer penetration. This leads to difficult segmentation of boundaries and affect semantic understanding, resulting in jagged boundaries and even missing elements. Objective This paper aims to solve the problem of poor boundary segmentation in PCB CT image element segmentation. Methods To this end, we propose PCB CT image element segmentation model based on boundary-attention-guided finetuning. An improved boundary detection algorithm is proposed to enhance boundary sensing ability. In order to achieve non-fixed weight feature fusion, the Attention Feature Fusion module is designed to help boundary features better assist segmentation through attention mechanism. Results Experiments show that BAG-FTseg can achieve 89.5% mIoU on our PCB CT dataset, exceeding the baseline model by 0.9%, and the boundary-mIoU reaches 69.5%, 5.3% higher than the baseline model. Conclusion This method improves the accuracy of boundary segmentation of PCB elements and the efficiency of feature fusion through attention mechanism, which has practical significance.

Evolution of Ferrite Morphology and Ferrite-Ferrite Grain Boundary Characteristics with Cooling Rate in a Continuously Cooled 0.09C-0.08V-0.02N Microalloyed Steel

Continuously cooled HSLA steels produce mixed ferritic and bainitic microstructures with multiple constituents present within the same prior-austenite grain. Conventional methods of distinguishing these constituents have relied on size and morphology characteristics, which have inherent subjectivity in identifying constituents. In this study, grain boundary misorientation distributions were utilized to more objectively identify different quasi-polygonal ferritic, acicular ferritic, bainitic, and martensitic microstructures within a continuously cooled V-microalloyed HSLA steel. It was determined that the slower-cooled morphology of acicular ferrite commonly observed in line-pipe steels produced approximately equal 55 and 60 deg peaks in the misorientation distribution, while the more lenticular faster-cooled acicular ferrite produced at a cooling rate of 25 °C/s had a stronger peak at the 60 deg misorientation. The P2/P1 ratio, which quantified the ratio of these two misorientation peaks, was employed to differentiate these two acicular ferrite morphologies. These constituents also exhibited different axis/angle misorientation distributions in the high-angle grain boundary misorientation regime than both quasi-polygonal ferrite and martensitic/bainitic microstructures.

The Influence of Microstructure on Corrosion Resistance of B10 Copper Tube

To pinpoint the relationship between the microstructure and corrosion resistance of the B10 copper tube, the copper tubes were annealed at 780°C and 810°C, respectively. Then the simulated seawater full immersion experiment was conducted. The corrosion film, grain size, boundary characteristics, and intragranular microstructure of the alloy were analyzed by OM, SEM, EBSD, and TEM. The results implied that the corrosion rate of the 810°C annealed copper tube is about 0.028 mm/a, which is 1.9 times that of the 780°C annealed copper tube. The average grain size of 810°C annealed copper tube is about 38.85 μm and the low ΣCSL account for 64.8 %, which is 1.5 times and 1.4 times that of 780°C annealed copper tube, respectively. There is a complete spinodal decomposition structure within the grain in an 810°C annealed copper tube, but there is an incomplete spinodal decomposition structure in a 780°C annealed copper tube. Theoretical analysis indicated that the large-sized grain clusters could be formed by numerous low-layer fault energy twin boundaries Σ3, and low ΣCSL combination Σ3, Σ9, Σ27, which can block the large crystal boundaries network, inhibit the phase precipitation and prevent invading of corrosive elements along the large crystal boundaries. The intragranular spinodal decomposition structure can improve the strength and toughness of the B10 copper tube, reduce the initiation of surface microcracks during service, and thus reduce pitting and crevice corrosion.

Annealing Effects on Microstructure and Texture in KOBO-Processed LPBF AlSi10Mg Alloy: Elucidating CSL Boundary Formation

This study introduces a strain-annealing approach to tailor the grain boundary characteristics of additively manufactured AlSi10Mg alloy produced by Laser Powder Bed Fusion (LPBF). By combining KOBO extrusion and subsequent annealing treatments, we aim to increase the proportion of low-Σ coincident site lattice (CSL) grain boundaries, particularly Σ3 boundaries. Through grain boundary engineering (GBE), specifically focused on inducing a high fraction of symmetrical CSL boundaries, our approach allows for the optimization of microstructural features that inhibit defect propagation and improve material stability. Microstructural analysis using electron backscatter diffraction (EBSD) revealed a substantial increase in Σ3 boundaries (60° &lt;111&gt; twin relationship) in the early recrystallization stages of the KOBO-processed LPBF AlSi10Mg alloy, demonstrating the effectiveness of this method. The findings presented in this manuscript highlight a new strategy for advancing the microstructural characteristics of LPBF AlSi10Mg alloy, with promising implications for applications requiring high-performance materials, such as in the aerospace, nuclear, and automotive industries.

A Model for Defining the Boundaries of Complex Sentences and Clauses in English Translation Based on the Huffman Tree and Objective Function

To address the issues of high redundancy in sentence features and poor semantic similarity analysis in English translation, a boundary definition model for complex sentence clauses in English translation based on the Huffman tree and objective function is proposed. This model analyzes the boundary structure of complex sentences and clauses in English translation, determines the mathematical expected value of the probability distribution of boundary features, introduces the maximum entropy model to assess the key degree of different features, and utilizes conditional random fields to mine hidden features. Additionally, by employing a hierarchical clustering algorithm to analyze the distance between boundary features of complex sentences and clauses in English translation, similar images are merged based on the minimum distance between data points. Feature redundancy scores are obtained through an attention mechanism, and the weight of boundary features of complex sentences and clauses in English translation is calculated. By using the edit distance in semantic similarity to determine the boundary distance of clauses, and then using cosine similarity to calculate the similarity between the boundary features of complex clauses in English translation, a Huffman tree and objective function are introduced to construct a model for defining the boundary of complex clauses in English translation. Input the boundary feature values of complex sentences and clauses in English translation to complete the final definition. The experimental results show that the proposed method performs well in the task of defining the boundaries of complex sentences and clauses in English translation, with semantic similarity analysis results remaining above 95% and reaching up to 99%, significantly better than the comparative methods. Meanwhile, the recall curve obtained by the proposed method is closest to the ideal curve and has a small fluctuation range, stable between 90% and 98%, further verifying its accuracy and robustness in boundary delineation. In addition, when the sample data size is 1000, the confidence level of the proposed method is as high as 99.6%, which is higher than the 95.6% and 95.1% of the comparison methods. As the sample size increased to 2500, the confidence level of the proposed method remained at a high level of 99.4%, while the confidence level of the comparative method decreased to 94.2% and 94.1%, respectively. These data results fully demonstrate the effectiveness of the proposed method in reducing redundant interference and improving confidence. In summary, the proposed method has improved the performance of defining the boundaries of complex sentences and clauses in English translation.

Study on the Microstructure and Corrosion Behavior of Dissimilar Aluminum Alloy Welded Joints Formed Using Laser Welding.

This study investigates the evolution mechanisms and electrochemical corrosion behavior of laser-welded joints (WJs) between 6063 and 6082 dissimilar aluminum alloys under varying welding powers. The analysis focused on the microstructure of the weld metal zone (WMZ), its grain boundary (GB) features, and its electrochemical corrosion properties. Data from the experiments indicate that a higher laser power (LP) leads to an increase in grain size within the WMZ. At an LP of 1750 W, the weld surface exhibits the poorest corrosion resistance, while other parameters show a relatively better resistance. Additionally, electron backscatter diffraction tests indicate that the high-angle GB fraction on the 6063-T6 side of the heat-affected zone exhibits a substantially reduced measurement compared to the 6082-T6 side. The corrosion form in the WMZ is intergranular, with energy-dispersive spectroscopy (EDS) scans revealing that the poor corrosion resistance is primarily due to the presence of a large amount of Mg2Si phase.

Grain Boundaries Control Lithiation of Solid Solution Substrates in Lithium Metal Batteries.

The development of sustainable transportation and communication systems requires an increase in both energy density and capacity retention of Li-batteries. Using substrates forming a solid solution with body-centered cubic Li enhances the cycle stability of anode-less batteries. However, it remains unclear how the substrate microstructure affects the lithiation behavior. Here, a correlative, near-atomic scale probing approach is deployed through combined ion- and electron-microscopy to examine the distribution of Li in Li-Ag diffusion couples as model system mimicking high current densities. It is revealed that Li regions with over 93.8% at.% nucleate within Ag at random high-angle grain boundaries, whereas grain interiors are not lithiated. The role of kinetics and mechanical constraint from the microstructure over equilibrium thermodynamics in dictating the lithiation process is evidenced. The findings suggest that grain size and grain boundary character are critical to enhance the electrochemical performance of interlayers/electrodes, particularly for improving lithiation kinetics and hence reducing dendrite formation.

Superpixel-based C-SVC for Brain Tissue Classification in MRI Scans

Accurate identification of brain tissue is an ill-posed problem due to the inhomogeneous intensity and the extremely complicated and irregular border between endocrine tissues. This study introduces a superpixel-based approach to brain tissue classification in MRI scans. The proposed approach starts with image smoothing and feature highlighting, followed by image splitting based on the SLIC superpixel and merging strategy. Then, distinct superpixel-based appearance and boundary features are extracted and refined by minimizing redundancy and maximizing relevance technique before sending to the C-support vector classifier. Finally, a refinement step is adopted based on morphological characteristics and the distance regularized level set evolution model to modify the matter contour. The proposed approach was evaluated and compared with ten existing algorithms using the publicly accessible IBSR dataset. The experimental results show the better efficiency of the proposed approach in delimiting the contour of each matter than the other approaches in the literature.

The influence of grain boundary character distribution on the high-temperature creep behavior and damage mechanism of Inconel 718

Thermomechanical processing as the main method of grain boundary engineering (GBE), can modulate the grain boundary character distribution (GBCD) in turn affects the mechanical properties and stability of alloys at high temperature. In this study, the microstructural evolution and creep behaviour of different GBCD alloys were investigated during the creep process at 660 °C and 690 MPa, and the effect of GBCD on high temperature creep behaviour and associated damage mechanism were analyzed from the viewpoints of grain size, recrystallisation and dislocation motion. The findings indicate that the larger grain size prolongs the steady-state creep duration, which is beneficial to the increase of creep life. During the creep process, the coherent twin boundary is susceptible to transformation into non-coherent twin boundary, resulting in the loss of its difficult-to-migrate properties and increased susceptibility to becoming recrystallisation nucleation sites. Furthermore, the formation of twins is conducive to the stimulation of dynamic recrystallisation. Discontinuous dynamic recrystallisation, characterised by the nucleation and growth of grains, occurs concurrently with continuous dynamic recrystallisation with strain gradients and orientations. The combined effect of these processes is grain boundary slip and restricted dislocation migration, which in turn alter the creep properties of the alloy.

The mechanism for the self-accommodation microstructure of α variants during phase transformation of the Zr–2.5Nb Alloy

Although variant selection during the phase transformation of zirconium (Zr) alloys has been studied extensively, studies on the formation mechanism of microstructural characteristics related to α variant selection remain limited. The formation mechanisms of the self-accommodation morphology and inter-variant boundary characteristics of α variants in homogenized Zr–2.5Nb alloy cooled by water quenching (WQ), furnace cooling (FC), and air cooling (AC) were systematically investigated from the perspective of local strain during phase transformation. The α variants exhibited triangular morphologies in both the WQ and AC samples, and a colony morphology in the FC sample. Further, there were five types of inter-variant boundaries: Type I <0 0 0 1>/10.53°, Type II 〈112¯0〉/60°, Type III 〈1.377‾1¯2.3770.359〉/60.83°, Type IV 〈10‾553¯〉/63.26°, and Type V 〈12.38‾1.380〉/90°. The proportion of Type II is up to 98% in the AC sample and 57.9% in the WQ sample; the Type I was very low in all three samples; and a high proportion of the Type V was observed in the FC sample (23.6%). The self-accommodation morphology of α variants is closely related to the equivalent strain (εVM) during the variant selection. Theoretical calculations indicated that, for a specific 2-variant combinations, there were always one or more 3-variant combinations with a lower εVM than the 2-variant combinations. A lower εVM contributes to the presence of 3-variant combinations, which forms a triangle morphology. The formation of inter-variant boundaries is determined by the type and frequency of variants as well as the εVM of the 2-variant combinations. The order of the mean values of εVM for the five types of boundaries was Type II (0.0757), Type III (0.0859), Type IV (0.1012), Type V (0.1112), and Type I (0.1307). That is, Type II is the easiest and Type I is the most difficult, which resulted in a very high fraction of Type II and a very low fraction of Type I in the WQ, AC, and FC samples. The presence of a high fraction of Type V in the FC sample was related to the type and fraction of each variant.

EBFA-6D: End-to-End Transparent Object 6D Pose Estimation Based on a Boundary Feature Augmented Mechanism.

Transparent objects, commonly encountered in everyday environments, present significant challenges for 6D pose estimation due to their unique optical properties. The lack of inherent texture and color complicates traditional vision methods, while the transparency prevents depth sensors from accurately capturing geometric details. We propose EBFA-6D, a novel end-to-end 6D pose estimation framework that directly predicts the 6D poses of transparent objects from a single RGB image. To overcome the challenges introduced by transparency, we leverage the high contrast at object boundaries inherent to transparent objects by proposing a boundary feature augmented mechanism. We further conduct a bottom-up feature fusion to enhance the location capability of EBFA-6D. EBFA-6D is evaluated on the ClearPose dataset, outperforming the existing methods in accuracy while achieving an inference speed near real-time. The results demonstrate that EBFA-6D provides an efficient and effective solution for accurate 6D pose estimation of transparent objects.