Due to the lack of long-range association and spatial location information,fine details and accurate boundaries of complex clothing images cannot always be obtained by using the existing deep learning-based methods.Th...Due to the lack of long-range association and spatial location information,fine details and accurate boundaries of complex clothing images cannot always be obtained by using the existing deep learning-based methods.This paper presents a convolutional structure with multi-scale fusion to optimize the step of clothing feature extraction and a self-attention module to capture long-range association information.The structure enables the self-attention mechanism to directly participate in the process of information exchange through the down-scaling projection operation of the multi-scale framework.In addition,the improved self-attention module introduces the extraction of 2-dimensional relative position information to make up for its lack of ability to extract spatial position features from clothing images.The experimental results based on the colorful fashion parsing dataset(CFPD)show that the proposed network structure achieves 53.68%mean intersection over union(mIoU)and has better performance on the clothing parsing task.展开更多
Objective To propose a Light-Atten-Pose-based algorithm for classifying abnormal morphology in traditional Chinese medicine(TCM)inspection to solve the problem of relying on manual labor or expensive equipment with pe...Objective To propose a Light-Atten-Pose-based algorithm for classifying abnormal morphology in traditional Chinese medicine(TCM)inspection to solve the problem of relying on manual labor or expensive equipment with personal subjectivity or high cost.Methods First,this paper establishes a dataset of abnormal morphology for Chinese medicine diagnosis,with images from public resources and labeled with category labels by several Chinese medicine experts,including three categories:normal,shoulder abnormality,and leg abnormality.Second,the key points of human body are extracted by Light-Atten-Pose algo-rithm.Light-Atten-Pose algorithm uses lightweight EfficientNet network and polarized self-attention(PSA)mechanism on the basis of AlphaPose,which reduces the computation amount by using EfficientNet network,and the data is finely processed by using PSA mecha-nism in spatial and channel dimensions.Finally,according to the theory of TCM inspection,the abnormal morphology standard based on the joint angle difference is defined,and the classification of abnormal morphology of Chinese medical diagnosis is realized by calculat-ing the angle between key points.Accuracy,frames per second(FPS),model size,parameter set(Params),and giga floating-point operations per second(GFLOPs)are chosen as the eval-uation indexes for lightweighting.Results Validation of the Light-Atten-Pose algorithm on the dataset showed a classification accuracy of 96.23%,which is close to the original AlphaPose model.However,the FPS of the improved model reaches 41.6 fps from 16.5 fps,the model size is reduced from 155.11 MB to 33.67 MB,the Params decreases from 40.5 M to 8.6 M,and the GFLOPs reduces from 11.93 to 2.10.Conclusion The Light-Atten-Pose algorithm achieves lightweight while maintaining high ro-bustness,resulting in lower complexity and resource consumption and higher classification accuracy,and the experiments prove that the Light-Atten-Pose algorithm has a better overall performance and has practical application in the pose estimation task.展开更多
This paper aims to develop a nonrigid registration method of preoperative and intraoperative thoracoabdominal CT images in computer-assisted interventional surgeries for accurate tumor localization and tissue visualiz...This paper aims to develop a nonrigid registration method of preoperative and intraoperative thoracoabdominal CT images in computer-assisted interventional surgeries for accurate tumor localization and tissue visualization enhancement.However,fine structure registration of complex thoracoabdominal organs and large deformation registration caused by respiratory motion is challenging.To deal with this problem,we propose a 3D multi-scale attention VoxelMorph(MAVoxelMorph)registration network.To alleviate the large deformation problem,a multi-scale axial attention mechanism is utilized by using a residual dilated pyramid pooling for multi-scale feature extraction,and position-aware axial attention for long-distance dependencies between pixels capture.To further improve the large deformation and fine structure registration results,a multi-scale context channel attention mechanism is employed utilizing content information via adjacent encoding layers.Our method was evaluated on four public lung datasets(DIR-Lab dataset,Creatis dataset,Learn2Reg dataset,OASIS dataset)and a local dataset.Results proved that the proposed method achieved better registration performance than current state-of-the-art methods,especially in handling the registration of large deformations and fine structures.It also proved to be fast in 3D image registration,using about 1.5 s,and faster than most methods.Qualitative and quantitative assessments proved that the proposed MA-VoxelMorph has the potential to realize precise and fast tumor localization in clinical interventional surgeries.展开更多
To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network mo...To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network model is proposed by fusing multi-scale feature information.Firstly,a multi-scale feature extraction module is designed to obtain multi-scale information on feature images by using different scales of convolution kernels.Meanwhile,the channel attention mechanism is used to increase the global information acquisition of the network.Secondly,the feature images processed by the multi-scale feature extraction module are fused with the deep feature images through short links to guide the full learning of the network,thus reducing the loss of texture details of the deep network feature images,and improving network generalization ability and recognition accuracy.Finally,the validity of the MSFResNet model is verified using public datasets and applied to wild mushroom identification.Experimental results show that compared with ResNeXt50 network model,the accuracy of the MSFResNet model is improved by 6.01%on the FGVC-Aircraft common dataset.It achieves 99.13%classification accuracy on the wild mushroom dataset,which is 0.47%higher than ResNeXt50.Furthermore,the experimental results of the thermal map show that the MSFResNet model significantly reduces the interference of background information,making the network focus on the location of the main body of wild mushroom,which can effectively improve the accuracy of wild mushroom identification.展开更多
Effective bearing fault diagnosis is vital for the safe and reliable operation of rotating machinery.In practical applications,bearings often work at various rotational speeds as well as load conditions.Yet,the bearin...Effective bearing fault diagnosis is vital for the safe and reliable operation of rotating machinery.In practical applications,bearings often work at various rotational speeds as well as load conditions.Yet,the bearing fault diagnosis under multiple conditions is a new subject,which needs to be further explored.Therefore,a multi-scale deep belief network(DBN)method integrated with attention mechanism is proposed for the purpose of extracting the multi-scale core features from vibration signals,containing four primary steps:preprocessing of multi-scale data,feature extraction,feature fusion,and fault classification.The key novelties include multi-scale feature extraction using multi-scale DBN algorithm,and feature fusion using attention mecha-nism.The benchmark dataset from University of Ottawa is applied to validate the effectiveness as well as advantages of this method.Furthermore,the aforementioned method is compared with four classical fault diagnosis methods reported in the literature,and the comparison results show that our pro-posed method has higher diagnostic accuracy and better robustness.展开更多
With the development of short video industry,video and bullet screen have become important ways to spread public opinions.Public attitudes can be timely obtained through emotional analysis on bullet screen,which can a...With the development of short video industry,video and bullet screen have become important ways to spread public opinions.Public attitudes can be timely obtained through emotional analysis on bullet screen,which can also reduce difficulties in management of online public opinions.A convolutional neural network model based on multi-head attention is proposed to solve the problem of how to effectively model relations among words and identify key words in emotion classification tasks with short text contents and lack of complete context information.Firstly,encode word positions so that order information of input sequences can be used by the model.Secondly,use a multi-head attention mechanism to obtain semantic expressions in different subspaces,effectively capture internal relevance and enhance dependent relationships among words,as well as highlight emotional weights of key emotional words.Then a dilated convolution is used to increase the receptive field and extract more features.On this basis,the above multi-attention mechanism is combined with a convolutional neural network to model and analyze the seven emotional categories of bullet screens.Testing from perspectives of model and dataset,experimental results can validate effectiveness of our approach.Finally,emotions of bullet screens are visualized to provide data supports for hot event controls and other fields.展开更多
Keyphrase greatly provides summarized and valuable information.This information can help us not only understand text semantics,but also organize and retrieve text content effectively.The task of automatically generati...Keyphrase greatly provides summarized and valuable information.This information can help us not only understand text semantics,but also organize and retrieve text content effectively.The task of automatically generating it has received considerable attention in recent decades.From the previous studies,we can see many workable solutions for obtaining keyphrases.One method is to divide the content to be summarized into multiple blocks of text,then we rank and select the most important content.The disadvantage of this method is that it cannot identify keyphrase that does not include in the text,let alone get the real semantic meaning hidden in the text.Another approach uses recurrent neural networks to generate keyphrases from the semantic aspects of the text,but the inherently sequential nature precludes parallelization within training examples,and distances have limitations on context dependencies.Previous works have demonstrated the benefits of the self-attention mechanism,which can learn global text dependency features and can be parallelized.Inspired by the above observation,we propose a keyphrase generation model,which is based entirely on the self-attention mechanism.It is an encoder-decoder model that can make up the above disadvantage effectively.In addition,we also consider the semantic similarity between keyphrases,and add semantic similarity processing module into the model.This proposed model,which is demonstrated by empirical analysis on five datasets,can achieve competitive performance compared to baseline methods.展开更多
Fault diagnosis is important for maintaining the safety and effectiveness of chemical process.Considering the multivariate,nonlinear,and dynamic characteristic of chemical process,many time-series-based data-driven fa...Fault diagnosis is important for maintaining the safety and effectiveness of chemical process.Considering the multivariate,nonlinear,and dynamic characteristic of chemical process,many time-series-based data-driven fault diagnosis methods have been developed in recent years.However,the existing methods have the problem of long-term dependency and are difficult to train due to the sequential way of training.To overcome these problems,a novel fault diagnosis method based on time-series and the hierarchical multihead self-attention(HMSAN)is proposed for chemical process.First,a sliding window strategy is adopted to construct the normalized time-series dataset.Second,the HMSAN is developed to extract the time-relevant features from the time-series process data.It improves the basic self-attention model in both width and depth.With the multihead structure,the HMSAN can pay attention to different aspects of the complicated chemical process and obtain the global dynamic features.However,the multiple heads in parallel lead to redundant information,which cannot improve the diagnosis performance.With the hierarchical structure,the redundant information is reduced and the deep local time-related features are further extracted.Besides,a novel many-to-one training strategy is introduced for HMSAN to simplify the training procedure and capture the long-term dependency.Finally,the effectiveness of the proposed method is demonstrated by two chemical cases.The experimental results show that the proposed method achieves a great performance on time-series industrial data and outperforms the state-of-the-art approaches.展开更多
Rock fracture mechanisms can be inferred from moment tensors(MT)inverted from microseismic events.However,MT can only be inverted for events whose waveforms are acquired across a network of sensors.This is limiting fo...Rock fracture mechanisms can be inferred from moment tensors(MT)inverted from microseismic events.However,MT can only be inverted for events whose waveforms are acquired across a network of sensors.This is limiting for underground mines where the microseismic stations often lack azimuthal coverage.Thus,there is a need for a method to invert fracture mechanisms using waveforms acquired by a sparse microseismic network.Here,we present a novel,multi-scale framework to classify whether a rock crack contracts or dilates based on a single waveform.The framework consists of a deep learning model that is initially trained on 2400000+manually labelled field-scale seismic and microseismic waveforms acquired across 692 stations.Transfer learning is then applied to fine-tune the model on 300000+MT-labelled labscale acoustic emission waveforms from 39 individual experiments instrumented with different sensor layouts,loading,and rock types in training.The optimal model achieves over 86%F-score on unseen waveforms at both the lab-and field-scale.This model outperforms existing empirical methods in classification of rock fracture mechanisms monitored by a sparse microseismic network.This facilitates rapid assessment of,and early warning against,various rock engineering hazard such as induced earthquakes and rock bursts.展开更多
Successful modeling and/or design of engineering systems often requires one to address the impact of multiple ＂design variables＂ on the prescribed outcome.There are often multiple,competing objectives based on which...Successful modeling and/or design of engineering systems often requires one to address the impact of multiple ＂design variables＂ on the prescribed outcome.There are often multiple,competing objectives based on which we assess the outcome of optimization.Since accurate,high fidelity models are typically time consuming and computationally expensive,comprehensive evaluations can be conducted only if an efficient framework is available.Furthermore,informed decisions of the model/hardware＇s overall performance rely on an adequate understanding of the global,not local,sensitivity of the individual design variables on the objectives.The surrogate-based approach,which involves approximating the objectives as continuous functions of design variables from limited data,offers a rational framework to reduce the number of important input variables,i.e.,the dimension of a design or modeling space.In this paper,we review the fundamental issues that arise in surrogate-based analysis and optimization,highlighting concepts,methods,techniques,as well as modeling implications for mechanics problems.To aid the discussions of the issues involved,we summarize recent efforts in investigating cryogenic cavitating flows,active flow control based on dielectric barrier discharge concepts,and lithium（Li）-ion batteries.It is also stressed that many multi-scale mechanics problems can naturally benefit from the surrogate approach for ＂scale bridging.＂展开更多
False data injection attack(FDIA)can affect the state estimation of the power grid by tampering with the measured value of the power grid data,and then destroying the stable operation of the smart grid.Existing work u...False data injection attack(FDIA)can affect the state estimation of the power grid by tampering with the measured value of the power grid data,and then destroying the stable operation of the smart grid.Existing work usually trains a detection model by fusing the data-driven features from diverse power data streams.Data-driven features,however,cannot effectively capture the differences between noisy data and attack samples.As a result,slight noise disturbances in the power grid may cause a large number of false detections for FDIA attacks.To address this problem,this paper designs a deep collaborative self-attention network to achieve robust FDIA detection,in which the spatio-temporal features of cascaded FDIA attacks are fully integrated.Firstly,a high-order Chebyshev polynomials-based graph convolution module is designed to effectively aggregate the spatio information between grid nodes,and the spatial self-attention mechanism is involved to dynamically assign attention weights to each node,which guides the network to pay more attention to the node information that is conducive to FDIA detection.Furthermore,the bi-directional Long Short-Term Memory(LSTM)network is introduced to conduct time series modeling and long-term dependence analysis for power grid data and utilizes the temporal self-attention mechanism to describe the time correlation of data and assign different weights to different time steps.Our designed deep collaborative network can effectively mine subtle perturbations from spatiotemporal feature information,efficiently distinguish power grid noise from FDIA attacks,and adapt to diverse attack intensities.Extensive experiments demonstrate that our method can obtain an efficient detection performance over actual load data from New York Independent System Operator(NYISO)in IEEE 14,IEEE 39,and IEEE 118 bus systems,and outperforms state-of-the-art FDIA detection schemes in terms of detection accuracy and robustness.展开更多
Remote sensing imagery,due to its high altitude,presents inherent challenges characterized by multiple scales,limited target areas,and intricate backgrounds.These inherent traits often lead to increased miss and false...Remote sensing imagery,due to its high altitude,presents inherent challenges characterized by multiple scales,limited target areas,and intricate backgrounds.These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery.Additionally,these complexities contribute to inaccuracies in target localization and hinder precise target categorization.This paper addresses these challenges by proposing a solution:The YOLO-MFD model(YOLO-MFD:Remote Sensing Image Object Detection withMulti-scale Fusion Dynamic Head).Before presenting our method,we delve into the prevalent issues faced in remote sensing imagery analysis.Specifically,we emphasize the struggles of existing object recognition algorithms in comprehensively capturing critical image features amidst varying scales and complex backgrounds.To resolve these issues,we introduce a novel approach.First,we propose the implementation of a lightweight multi-scale module called CEF.This module significantly improves the model’s ability to comprehensively capture important image features by merging multi-scale feature information.It effectively addresses the issues of missed detection and mistaken alarms that are common in remote sensing imagery.Second,an additional layer of small target detection heads is added,and a residual link is established with the higher-level feature extraction module in the backbone section.This allows the model to incorporate shallower information,significantly improving the accuracy of target localization in remotely sensed images.Finally,a dynamic head attentionmechanism is introduced.This allows themodel to exhibit greater flexibility and accuracy in recognizing shapes and targets of different sizes.Consequently,the precision of object detection is significantly improved.The trial results show that the YOLO-MFD model shows improvements of 6.3%,3.5%,and 2.5%over the original YOLOv8 model in Precision,map@0.5 and map@0.5:0.95,separately.These results illustrate the clear advantages of the method.展开更多
Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often...Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often handpicked and need more delicate operations in intelligent picking machines.Compared with traditional image processing techniques,deep learning models have stronger feature extraction capabilities,and better generalization and are more suitable for practical tea shoot harvesting.However,current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks.We propose a tea shoot instance segmentation model based on multi-scale mixed attention(Mask2FusionNet)using a dataset from the tea garden in Hangzhou.We further analyzed the characteristics of the tea shoot dataset,where the proportion of small to medium-sized targets is 89.9%.Our algorithm is compared with several mainstream object segmentation algorithms,and the results demonstrate that our model achieves an accuracy of 82%in recognizing the tea shoots,showing a better performance compared to other models.Through ablation experiments,we found that ResNet50,PointRend strategy,and the Feature Pyramid Network(FPN)architecture can improve performance by 1.6%,1.4%,and 2.4%,respectively.These experiments demonstrated that our proposed multi-scale and point selection strategy optimizes the feature extraction capability for overlapping small targets.The results indicate that the proposed Mask2FusionNet model can perform the shoot segmentation in unstructured environments,realizing the individual distinction of tea shoots,and complete extraction of the shoot edge contours with a segmentation accuracy of 82.0%.The research results can provide algorithmic support for the segmentation and intelligent harvesting of premium tea shoots at different scales.展开更多
Microstructural characteristics and mechanical behavior of hot extruded Al5083/B4C nanocomposites were studied.Al5083and Al5083/B4C powders were milled for50h under argon atmosphere in attrition mill with rotational s...Microstructural characteristics and mechanical behavior of hot extruded Al5083/B4C nanocomposites were studied.Al5083and Al5083/B4C powders were milled for50h under argon atmosphere in attrition mill with rotational speed of400r/min.For increasing the elongation,milled powders were mixed with30%and50%unmilled aluminum powder(mass fraction)with meanparticle size of>100μm and<100μm and then consolidated by hot pressing and hot extrusion with9:1extrusion ratio.Hot extrudedsamples were studied by optical microscopy,scanning electron microscopy(SEM),energy dispersive spectroscopy(EDS),transmission electron microscopy(TEM),tensile and hardness tests.The results showed that mechanical milling process andpresence of B4C particles increase the yield strength of Al5083alloy from130to566MPa but strongly decrease elongation(from11.3%to0.49%).Adding<100μm unmilled particles enhanced the ductility and reduced tensile strength and hardness,but usingthe>100μm unmilled particles reduced the tensile strength and ductility at the same time.By increasing the content of unmilledparticles failure mechanism changed from brittle to ductile.展开更多
As the complexity of scientific satellite missions increases,the requirements for their magnetic fields,magnetic field fluctuations,and even magnetic field gradients and variations become increasingly stringent.Additi...As the complexity of scientific satellite missions increases,the requirements for their magnetic fields,magnetic field fluctuations,and even magnetic field gradients and variations become increasingly stringent.Additionally,there is a growing need to address the alternating magnetic fields produced by the spacecraft itself.This paper introduces a novel modeling method for spacecraft magnetic dipoles using an integrated self-attention mechanism and a transformer combined with Kolmogorov-Arnold Networks.The self-attention mechanism captures correlations among globally sparse data,establishing dependencies b.etween sparse magnetometer readings.Concurrently,the Kolmogorov-Arnold Network,proficient in modeling implicit numerical relationships between data features,enhances the ability to learn subtle patterns.Comparative experiments validate the capability of the proposed method to precisely model magnetic dipoles,achieving maximum Root Mean Square Errors of 24.06 mA·m^(2)and 0.32 cm for size and location modeling,respectively.The spacecraft magnetic model established using this method accurately computes magnetic fields and alternating magnetic fields at designated surfaces or points.This approach facilitates the rapid and precise construction of individual and complete spacecraft magnetic models,enabling the verification of magnetic specifications from the spacecraft design phase.展开更多
Previous failure analyses of bridges typically focus on substructure failure or superstructure failure separately. However, in an actual bridge, the seismic induced substructure failure and superstructure failure may ...Previous failure analyses of bridges typically focus on substructure failure or superstructure failure separately. However, in an actual bridge, the seismic induced substructure failure and superstructure failure may influence each other. Moreover, previous studies typically use simplified models to analyze the bridge failure; however, there are inherent defects in the calculation accuracy compared with using a detailed three-dimensional （3D） finite element （FE） model. Conversely, a detailed 3D FE model requires more computational costs, and a proper erosion criterion of the 3D elements is necessary. In this paper, a multi-scale FE model, including a corresponding erosion criterion, is proposed and validated that can significantly reduce computational costs with high precision by modelling a pseudo-dynamic test of an reinforced concrete （RC） pier. Numerical simulations of the seismic failures of a continuous RC bridge based on the multi-scale FE modeling method using LS-DYNA are performed. The nonlinear properties of the bridge, various connection strengths and bidirectional excitations are considered. The numerical results demonstrate that the failure of the connections will induce large pounding responses of the girders. The nonlinear deformation of the piers will aggravate the pounding damages. Furthermore, bidirectional earthquakes will induce eccentric poundingsto the girders and different failure modes to the adjacent piers.展开更多
As a surface functional material,super-hydrophobic coating has great application potential in wind turbine blade anti-icing,self-cleaning and drag reduction.In this study,ZnO and SiO2 multi-scale superhydrophobic coat...As a surface functional material,super-hydrophobic coating has great application potential in wind turbine blade anti-icing,self-cleaning and drag reduction.In this study,ZnO and SiO2 multi-scale superhydrophobic coatings with mechanical flexibility were prepared by embedding modified ZnO and SiO2 nanoparticles in PDMS.The prepared coating has a higher static water contact angle(CA is 153°)and a lower rolling angle(SA is 3.3°),showing excellent super-hydrophobicity.Because of its excellent superhydrophobic ability and micro-nano structure,the coating has good anti-icing ability.Under the conditions of−10C and 60%relative humidity,the coating can delay the freezing time by 1511S,which is 10.7 times slower than the normal freezing time.More importantly,due to the mechanical properties provided by SiO2 and the synergistic effect of micro-nano particles,the coating has excellent mechanical durability.After 10 wear tests,the contact angle of the coating is still as high as 141°and the rolling angle is 6.8°.This research provides a theoretical reference for the preparation of a mechanically stable coating with a simple preparation process,as well as a basic research on the anti-icing behavior of the coating.展开更多
An advanced ceramic cutting tool material Al2O3/TiC/TiN （LTN） is developed by incorporation and dispersion of micro-scale TiC particle and nano-scale TiN particle in alumina matrix. With the optimal dispersing and f...An advanced ceramic cutting tool material Al2O3/TiC/TiN （LTN） is developed by incorporation and dispersion of micro-scale TiC particle and nano-scale TiN particle in alumina matrix. With the optimal dispersing and fabricating technology, this multi-scale and multi-phase nanocomposite ceramic tool material can get both higher flexural strength and fracture toughness than that of A1203/TiC （LZ） ceramic tool material without nano-scale TiN particle, especially the fracture toughness can reach to 7.8 MPa . m^0.5. The nano-scale TiN can lead to the grain fining effect and promote the sintering process to get a higher density. The coexisting transgranular and intergranular fracture mode induced by micro-scale TiC and nano-scale TiN, and the homogeneous and densified microstructure can result in a remarkable strengthening and toughening effect. The cutting performance and wear mechanisms of the advanced multi-scale and multi-phase nanocomposite ceramic cutting tool are researched.展开更多
An ultrafine grained microstructure was obtained for 304 stainless steel(304SS)sheets by using surface nanocrystallization and warm-rolling.The microstructure and mechanical properties were determined by X-ray diffrac...An ultrafine grained microstructure was obtained for 304 stainless steel(304SS)sheets by using surface nanocrystallization and warm-rolling.The microstructure and mechanical properties were determined by X-ray diffraction(XRD),transmission electron microscope(TEM)and a test on microhardness.Experimental results were shown that the microstructure was featured by a continuous distribution from the nanocrystalline on the surface to micro-grains in the center,in which the volume fraction of the micro-sized grains is about 40% in the surface layer.This multi-scale grained microstructure was composed of austenite and martensite phases with a gradient increasing volume fraction of austenite from the surface to the centre.The microhardness of the resultant steel was higher than 150% of that as received,due to the refined grains and strain-induced martensitic transformation.The hardness distribution was consistent with the microstructural variation,suggesting a good combination of high strength and improved ductility.展开更多
Superalloy thin-walled structures are achieved mainly by brazing,but the deformation process of brazed joints is non-uniform,making it a challenging research task.This paper records a thorough investigation of the eff...Superalloy thin-walled structures are achieved mainly by brazing,but the deformation process of brazed joints is non-uniform,making it a challenging research task.This paper records a thorough investigation of the effect of brazing parameters on the microstructure of joints and its mechanical properties,which mainly inquires into the deformation and fracture mechanisms in the shearing process of GH99/BNi-5a/GH99 joints.The macroscopic-microscopic deformation mechanism of the brazing interface during shearing was studied by Crystal Plasticity(CP)and Molecular Dynamics(MD)on the basis of the optimal brazing parameters.The experimental results show that the brazing interface is mainly formed by(Ni,Cr,Co)(s,s)and possesses a shear strength of approximately 546 MPa.The shearing fracture of the brazed joint occurs along the brazing seam,displaying the characteristics of intergranular fracture.MD simulations show that dislocations disassociate and transform into fine twinning with increased strain.CP simulated the shear deformation process of the brazed joint.The multiscale simulation results are consistent with the experimental results.The mechanical properties of thin-walled materials for brazing are predicted using MD and CP methods.展开更多
文摘Due to the lack of long-range association and spatial location information,fine details and accurate boundaries of complex clothing images cannot always be obtained by using the existing deep learning-based methods.This paper presents a convolutional structure with multi-scale fusion to optimize the step of clothing feature extraction and a self-attention module to capture long-range association information.The structure enables the self-attention mechanism to directly participate in the process of information exchange through the down-scaling projection operation of the multi-scale framework.In addition,the improved self-attention module introduces the extraction of 2-dimensional relative position information to make up for its lack of ability to extract spatial position features from clothing images.The experimental results based on the colorful fashion parsing dataset(CFPD)show that the proposed network structure achieves 53.68%mean intersection over union(mIoU)and has better performance on the clothing parsing task.
基金National Key Research and Development Program of China(2022YFC3502302)。
文摘Objective To propose a Light-Atten-Pose-based algorithm for classifying abnormal morphology in traditional Chinese medicine(TCM)inspection to solve the problem of relying on manual labor or expensive equipment with personal subjectivity or high cost.Methods First,this paper establishes a dataset of abnormal morphology for Chinese medicine diagnosis,with images from public resources and labeled with category labels by several Chinese medicine experts,including three categories:normal,shoulder abnormality,and leg abnormality.Second,the key points of human body are extracted by Light-Atten-Pose algo-rithm.Light-Atten-Pose algorithm uses lightweight EfficientNet network and polarized self-attention(PSA)mechanism on the basis of AlphaPose,which reduces the computation amount by using EfficientNet network,and the data is finely processed by using PSA mecha-nism in spatial and channel dimensions.Finally,according to the theory of TCM inspection,the abnormal morphology standard based on the joint angle difference is defined,and the classification of abnormal morphology of Chinese medical diagnosis is realized by calculat-ing the angle between key points.Accuracy,frames per second(FPS),model size,parameter set(Params),and giga floating-point operations per second(GFLOPs)are chosen as the eval-uation indexes for lightweighting.Results Validation of the Light-Atten-Pose algorithm on the dataset showed a classification accuracy of 96.23%,which is close to the original AlphaPose model.However,the FPS of the improved model reaches 41.6 fps from 16.5 fps,the model size is reduced from 155.11 MB to 33.67 MB,the Params decreases from 40.5 M to 8.6 M,and the GFLOPs reduces from 11.93 to 2.10.Conclusion The Light-Atten-Pose algorithm achieves lightweight while maintaining high ro-bustness,resulting in lower complexity and resource consumption and higher classification accuracy,and the experiments prove that the Light-Atten-Pose algorithm has a better overall performance and has practical application in the pose estimation task.
基金supported in part by the National Natural Science Foundation of China[62301374]Hubei Provincial Natural Science Foundation of China[2022CFB804]+2 种基金Hubei Provincial Education Research Project[B2022057]the Youths Science Foundation of Wuhan Institute of Technology[K202240]the 15th Graduate Education Innovation Fund of Wuhan Institute of Technology[CX2023295].
文摘This paper aims to develop a nonrigid registration method of preoperative and intraoperative thoracoabdominal CT images in computer-assisted interventional surgeries for accurate tumor localization and tissue visualization enhancement.However,fine structure registration of complex thoracoabdominal organs and large deformation registration caused by respiratory motion is challenging.To deal with this problem,we propose a 3D multi-scale attention VoxelMorph(MAVoxelMorph)registration network.To alleviate the large deformation problem,a multi-scale axial attention mechanism is utilized by using a residual dilated pyramid pooling for multi-scale feature extraction,and position-aware axial attention for long-distance dependencies between pixels capture.To further improve the large deformation and fine structure registration results,a multi-scale context channel attention mechanism is employed utilizing content information via adjacent encoding layers.Our method was evaluated on four public lung datasets(DIR-Lab dataset,Creatis dataset,Learn2Reg dataset,OASIS dataset)and a local dataset.Results proved that the proposed method achieved better registration performance than current state-of-the-art methods,especially in handling the registration of large deformations and fine structures.It also proved to be fast in 3D image registration,using about 1.5 s,and faster than most methods.Qualitative and quantitative assessments proved that the proposed MA-VoxelMorph has the potential to realize precise and fast tumor localization in clinical interventional surgeries.
基金supported by National Natural Science Foundation of China(No.61862037)Lanzhou Jiaotong University Tianyou Innovation Team Project(No.TY202002)。
文摘To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network model is proposed by fusing multi-scale feature information.Firstly,a multi-scale feature extraction module is designed to obtain multi-scale information on feature images by using different scales of convolution kernels.Meanwhile,the channel attention mechanism is used to increase the global information acquisition of the network.Secondly,the feature images processed by the multi-scale feature extraction module are fused with the deep feature images through short links to guide the full learning of the network,thus reducing the loss of texture details of the deep network feature images,and improving network generalization ability and recognition accuracy.Finally,the validity of the MSFResNet model is verified using public datasets and applied to wild mushroom identification.Experimental results show that compared with ResNeXt50 network model,the accuracy of the MSFResNet model is improved by 6.01%on the FGVC-Aircraft common dataset.It achieves 99.13%classification accuracy on the wild mushroom dataset,which is 0.47%higher than ResNeXt50.Furthermore,the experimental results of the thermal map show that the MSFResNet model significantly reduces the interference of background information,making the network focus on the location of the main body of wild mushroom,which can effectively improve the accuracy of wild mushroom identification.
基金supported by the National Natural Science Foundation of China(62020106003,61873122,62303217)Aero Engine Corporation of China Industry-university-research Cooperation Project(HFZL2020CXY011)the Research Fund of State Key Laboratory of Mechanics and Control of Mechanical Structures(Nanjing University of Aeronautics and Astronautics)(MCMS-I-0121G03).
文摘Effective bearing fault diagnosis is vital for the safe and reliable operation of rotating machinery.In practical applications,bearings often work at various rotational speeds as well as load conditions.Yet,the bearing fault diagnosis under multiple conditions is a new subject,which needs to be further explored.Therefore,a multi-scale deep belief network(DBN)method integrated with attention mechanism is proposed for the purpose of extracting the multi-scale core features from vibration signals,containing four primary steps:preprocessing of multi-scale data,feature extraction,feature fusion,and fault classification.The key novelties include multi-scale feature extraction using multi-scale DBN algorithm,and feature fusion using attention mecha-nism.The benchmark dataset from University of Ottawa is applied to validate the effectiveness as well as advantages of this method.Furthermore,the aforementioned method is compared with four classical fault diagnosis methods reported in the literature,and the comparison results show that our pro-posed method has higher diagnostic accuracy and better robustness.
基金National Natural Science Foundation of China(No.61562057)Gansu Science and Technology Plan Project(No.18JR3RA104)。
文摘With the development of short video industry,video and bullet screen have become important ways to spread public opinions.Public attitudes can be timely obtained through emotional analysis on bullet screen,which can also reduce difficulties in management of online public opinions.A convolutional neural network model based on multi-head attention is proposed to solve the problem of how to effectively model relations among words and identify key words in emotion classification tasks with short text contents and lack of complete context information.Firstly,encode word positions so that order information of input sequences can be used by the model.Secondly,use a multi-head attention mechanism to obtain semantic expressions in different subspaces,effectively capture internal relevance and enhance dependent relationships among words,as well as highlight emotional weights of key emotional words.Then a dilated convolution is used to increase the receptive field and extract more features.On this basis,the above multi-attention mechanism is combined with a convolutional neural network to model and analyze the seven emotional categories of bullet screens.Testing from perspectives of model and dataset,experimental results can validate effectiveness of our approach.Finally,emotions of bullet screens are visualized to provide data supports for hot event controls and other fields.
文摘Keyphrase greatly provides summarized and valuable information.This information can help us not only understand text semantics,but also organize and retrieve text content effectively.The task of automatically generating it has received considerable attention in recent decades.From the previous studies,we can see many workable solutions for obtaining keyphrases.One method is to divide the content to be summarized into multiple blocks of text,then we rank and select the most important content.The disadvantage of this method is that it cannot identify keyphrase that does not include in the text,let alone get the real semantic meaning hidden in the text.Another approach uses recurrent neural networks to generate keyphrases from the semantic aspects of the text,but the inherently sequential nature precludes parallelization within training examples,and distances have limitations on context dependencies.Previous works have demonstrated the benefits of the self-attention mechanism,which can learn global text dependency features and can be parallelized.Inspired by the above observation,we propose a keyphrase generation model,which is based entirely on the self-attention mechanism.It is an encoder-decoder model that can make up the above disadvantage effectively.In addition,we also consider the semantic similarity between keyphrases,and add semantic similarity processing module into the model.This proposed model,which is demonstrated by empirical analysis on five datasets,can achieve competitive performance compared to baseline methods.
基金supported by the National Natural Science Foundation of China(62073140,62073141)the Shanghai Rising-Star Program(21QA1401800).
文摘Fault diagnosis is important for maintaining the safety and effectiveness of chemical process.Considering the multivariate,nonlinear,and dynamic characteristic of chemical process,many time-series-based data-driven fault diagnosis methods have been developed in recent years.However,the existing methods have the problem of long-term dependency and are difficult to train due to the sequential way of training.To overcome these problems,a novel fault diagnosis method based on time-series and the hierarchical multihead self-attention(HMSAN)is proposed for chemical process.First,a sliding window strategy is adopted to construct the normalized time-series dataset.Second,the HMSAN is developed to extract the time-relevant features from the time-series process data.It improves the basic self-attention model in both width and depth.With the multihead structure,the HMSAN can pay attention to different aspects of the complicated chemical process and obtain the global dynamic features.However,the multiple heads in parallel lead to redundant information,which cannot improve the diagnosis performance.With the hierarchical structure,the redundant information is reduced and the deep local time-related features are further extracted.Besides,a novel many-to-one training strategy is introduced for HMSAN to simplify the training procedure and capture the long-term dependency.Finally,the effectiveness of the proposed method is demonstrated by two chemical cases.The experimental results show that the proposed method achieves a great performance on time-series industrial data and outperforms the state-of-the-art approaches.
基金supported by Western Research Interdisciplinary Initiative R6259A03.
文摘Rock fracture mechanisms can be inferred from moment tensors(MT)inverted from microseismic events.However,MT can only be inverted for events whose waveforms are acquired across a network of sensors.This is limiting for underground mines where the microseismic stations often lack azimuthal coverage.Thus,there is a need for a method to invert fracture mechanisms using waveforms acquired by a sparse microseismic network.Here,we present a novel,multi-scale framework to classify whether a rock crack contracts or dilates based on a single waveform.The framework consists of a deep learning model that is initially trained on 2400000+manually labelled field-scale seismic and microseismic waveforms acquired across 692 stations.Transfer learning is then applied to fine-tune the model on 300000+MT-labelled labscale acoustic emission waveforms from 39 individual experiments instrumented with different sensor layouts,loading,and rock types in training.The optimal model achieves over 86%F-score on unseen waveforms at both the lab-and field-scale.This model outperforms existing empirical methods in classification of rock fracture mechanisms monitored by a sparse microseismic network.This facilitates rapid assessment of,and early warning against,various rock engineering hazard such as induced earthquakes and rock bursts.
文摘Successful modeling and/or design of engineering systems often requires one to address the impact of multiple ＂design variables＂ on the prescribed outcome.There are often multiple,competing objectives based on which we assess the outcome of optimization.Since accurate,high fidelity models are typically time consuming and computationally expensive,comprehensive evaluations can be conducted only if an efficient framework is available.Furthermore,informed decisions of the model/hardware＇s overall performance rely on an adequate understanding of the global,not local,sensitivity of the individual design variables on the objectives.The surrogate-based approach,which involves approximating the objectives as continuous functions of design variables from limited data,offers a rational framework to reduce the number of important input variables,i.e.,the dimension of a design or modeling space.In this paper,we review the fundamental issues that arise in surrogate-based analysis and optimization,highlighting concepts,methods,techniques,as well as modeling implications for mechanics problems.To aid the discussions of the issues involved,we summarize recent efforts in investigating cryogenic cavitating flows,active flow control based on dielectric barrier discharge concepts,and lithium（Li）-ion batteries.It is also stressed that many multi-scale mechanics problems can naturally benefit from the surrogate approach for ＂scale bridging.＂
基金supported in part by the Research Fund of Guangxi Key Lab of Multi-Source Information Mining&Security(MIMS21-M-02).
文摘False data injection attack(FDIA)can affect the state estimation of the power grid by tampering with the measured value of the power grid data,and then destroying the stable operation of the smart grid.Existing work usually trains a detection model by fusing the data-driven features from diverse power data streams.Data-driven features,however,cannot effectively capture the differences between noisy data and attack samples.As a result,slight noise disturbances in the power grid may cause a large number of false detections for FDIA attacks.To address this problem,this paper designs a deep collaborative self-attention network to achieve robust FDIA detection,in which the spatio-temporal features of cascaded FDIA attacks are fully integrated.Firstly,a high-order Chebyshev polynomials-based graph convolution module is designed to effectively aggregate the spatio information between grid nodes,and the spatial self-attention mechanism is involved to dynamically assign attention weights to each node,which guides the network to pay more attention to the node information that is conducive to FDIA detection.Furthermore,the bi-directional Long Short-Term Memory(LSTM)network is introduced to conduct time series modeling and long-term dependence analysis for power grid data and utilizes the temporal self-attention mechanism to describe the time correlation of data and assign different weights to different time steps.Our designed deep collaborative network can effectively mine subtle perturbations from spatiotemporal feature information,efficiently distinguish power grid noise from FDIA attacks,and adapt to diverse attack intensities.Extensive experiments demonstrate that our method can obtain an efficient detection performance over actual load data from New York Independent System Operator(NYISO)in IEEE 14,IEEE 39,and IEEE 118 bus systems,and outperforms state-of-the-art FDIA detection schemes in terms of detection accuracy and robustness.
基金the Scientific Research Fund of Hunan Provincial Education Department(23A0423).
文摘Remote sensing imagery,due to its high altitude,presents inherent challenges characterized by multiple scales,limited target areas,and intricate backgrounds.These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery.Additionally,these complexities contribute to inaccuracies in target localization and hinder precise target categorization.This paper addresses these challenges by proposing a solution:The YOLO-MFD model(YOLO-MFD:Remote Sensing Image Object Detection withMulti-scale Fusion Dynamic Head).Before presenting our method,we delve into the prevalent issues faced in remote sensing imagery analysis.Specifically,we emphasize the struggles of existing object recognition algorithms in comprehensively capturing critical image features amidst varying scales and complex backgrounds.To resolve these issues,we introduce a novel approach.First,we propose the implementation of a lightweight multi-scale module called CEF.This module significantly improves the model’s ability to comprehensively capture important image features by merging multi-scale feature information.It effectively addresses the issues of missed detection and mistaken alarms that are common in remote sensing imagery.Second,an additional layer of small target detection heads is added,and a residual link is established with the higher-level feature extraction module in the backbone section.This allows the model to incorporate shallower information,significantly improving the accuracy of target localization in remotely sensed images.Finally,a dynamic head attentionmechanism is introduced.This allows themodel to exhibit greater flexibility and accuracy in recognizing shapes and targets of different sizes.Consequently,the precision of object detection is significantly improved.The trial results show that the YOLO-MFD model shows improvements of 6.3%,3.5%,and 2.5%over the original YOLOv8 model in Precision,map@0.5 and map@0.5:0.95,separately.These results illustrate the clear advantages of the method.
基金This research was supported by the National Natural Science Foundation of China No.62276086the National Key R&D Program of China No.2022YFD2000100Zhejiang Provincial Natural Science Foundation of China under Grant No.LTGN23D010002.
文摘Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often handpicked and need more delicate operations in intelligent picking machines.Compared with traditional image processing techniques,deep learning models have stronger feature extraction capabilities,and better generalization and are more suitable for practical tea shoot harvesting.However,current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks.We propose a tea shoot instance segmentation model based on multi-scale mixed attention(Mask2FusionNet)using a dataset from the tea garden in Hangzhou.We further analyzed the characteristics of the tea shoot dataset,where the proportion of small to medium-sized targets is 89.9%.Our algorithm is compared with several mainstream object segmentation algorithms,and the results demonstrate that our model achieves an accuracy of 82%in recognizing the tea shoots,showing a better performance compared to other models.Through ablation experiments,we found that ResNet50,PointRend strategy,and the Feature Pyramid Network(FPN)architecture can improve performance by 1.6%,1.4%,and 2.4%,respectively.These experiments demonstrated that our proposed multi-scale and point selection strategy optimizes the feature extraction capability for overlapping small targets.The results indicate that the proposed Mask2FusionNet model can perform the shoot segmentation in unstructured environments,realizing the individual distinction of tea shoots,and complete extraction of the shoot edge contours with a segmentation accuracy of 82.0%.The research results can provide algorithmic support for the segmentation and intelligent harvesting of premium tea shoots at different scales.
文摘Microstructural characteristics and mechanical behavior of hot extruded Al5083/B4C nanocomposites were studied.Al5083and Al5083/B4C powders were milled for50h under argon atmosphere in attrition mill with rotational speed of400r/min.For increasing the elongation,milled powders were mixed with30%and50%unmilled aluminum powder(mass fraction)with meanparticle size of>100μm and<100μm and then consolidated by hot pressing and hot extrusion with9:1extrusion ratio.Hot extrudedsamples were studied by optical microscopy,scanning electron microscopy(SEM),energy dispersive spectroscopy(EDS),transmission electron microscopy(TEM),tensile and hardness tests.The results showed that mechanical milling process andpresence of B4C particles increase the yield strength of Al5083alloy from130to566MPa but strongly decrease elongation(from11.3%to0.49%).Adding<100μm unmilled particles enhanced the ductility and reduced tensile strength and hardness,but usingthe>100μm unmilled particles reduced the tensile strength and ductility at the same time.By increasing the content of unmilledparticles failure mechanism changed from brittle to ductile.
基金supported by the National Key Research and Development Program of China(2020YFC2200901)。
文摘As the complexity of scientific satellite missions increases,the requirements for their magnetic fields,magnetic field fluctuations,and even magnetic field gradients and variations become increasingly stringent.Additionally,there is a growing need to address the alternating magnetic fields produced by the spacecraft itself.This paper introduces a novel modeling method for spacecraft magnetic dipoles using an integrated self-attention mechanism and a transformer combined with Kolmogorov-Arnold Networks.The self-attention mechanism captures correlations among globally sparse data,establishing dependencies b.etween sparse magnetometer readings.Concurrently,the Kolmogorov-Arnold Network,proficient in modeling implicit numerical relationships between data features,enhances the ability to learn subtle patterns.Comparative experiments validate the capability of the proposed method to precisely model magnetic dipoles,achieving maximum Root Mean Square Errors of 24.06 mA·m^(2)and 0.32 cm for size and location modeling,respectively.The spacecraft magnetic model established using this method accurately computes magnetic fields and alternating magnetic fields at designated surfaces or points.This approach facilitates the rapid and precise construction of individual and complete spacecraft magnetic models,enabling the verification of magnetic specifications from the spacecraft design phase.
基金National Program on Key Basic Research Project of China(973) under Grant No.2011CB013603the National Natural Science Foundation of China under Grant Nos.51427901,91315301 and 51408410the Natural Science Foundation of Tianjin,China under Grant No.15JCQNJC07200
文摘Previous failure analyses of bridges typically focus on substructure failure or superstructure failure separately. However, in an actual bridge, the seismic induced substructure failure and superstructure failure may influence each other. Moreover, previous studies typically use simplified models to analyze the bridge failure; however, there are inherent defects in the calculation accuracy compared with using a detailed three-dimensional （3D） finite element （FE） model. Conversely, a detailed 3D FE model requires more computational costs, and a proper erosion criterion of the 3D elements is necessary. In this paper, a multi-scale FE model, including a corresponding erosion criterion, is proposed and validated that can significantly reduce computational costs with high precision by modelling a pseudo-dynamic test of an reinforced concrete （RC） pier. Numerical simulations of the seismic failures of a continuous RC bridge based on the multi-scale FE modeling method using LS-DYNA are performed. The nonlinear properties of the bridge, various connection strengths and bidirectional excitations are considered. The numerical results demonstrate that the failure of the connections will induce large pounding responses of the girders. The nonlinear deformation of the piers will aggravate the pounding damages. Furthermore, bidirectional earthquakes will induce eccentric poundingsto the girders and different failure modes to the adjacent piers.
基金funded by the Changsha University of Science and Technology Research and Innovation Project(CX2019SS21)the National Energy Group Technology Innovation Project(HJLFD-QTHT-2019-09).
文摘As a surface functional material,super-hydrophobic coating has great application potential in wind turbine blade anti-icing,self-cleaning and drag reduction.In this study,ZnO and SiO2 multi-scale superhydrophobic coatings with mechanical flexibility were prepared by embedding modified ZnO and SiO2 nanoparticles in PDMS.The prepared coating has a higher static water contact angle(CA is 153°)and a lower rolling angle(SA is 3.3°),showing excellent super-hydrophobicity.Because of its excellent superhydrophobic ability and micro-nano structure,the coating has good anti-icing ability.Under the conditions of−10C and 60%relative humidity,the coating can delay the freezing time by 1511S,which is 10.7 times slower than the normal freezing time.More importantly,due to the mechanical properties provided by SiO2 and the synergistic effect of micro-nano particles,the coating has excellent mechanical durability.After 10 wear tests,the contact angle of the coating is still as high as 141°and the rolling angle is 6.8°.This research provides a theoretical reference for the preparation of a mechanically stable coating with a simple preparation process,as well as a basic research on the anti-icing behavior of the coating.
基金Selected from Proceedings of the 7th International Conference on Frontiers of DesignManufacturing(ICFDM'2006)This project is supported by National Natural Science Foundation of China(No.50275086)the University of New South Wales Visiting Professorship Scheme,Australia.
文摘An advanced ceramic cutting tool material Al2O3/TiC/TiN （LTN） is developed by incorporation and dispersion of micro-scale TiC particle and nano-scale TiN particle in alumina matrix. With the optimal dispersing and fabricating technology, this multi-scale and multi-phase nanocomposite ceramic tool material can get both higher flexural strength and fracture toughness than that of A1203/TiC （LZ） ceramic tool material without nano-scale TiN particle, especially the fracture toughness can reach to 7.8 MPa . m^0.5. The nano-scale TiN can lead to the grain fining effect and promote the sintering process to get a higher density. The coexisting transgranular and intergranular fracture mode induced by micro-scale TiC and nano-scale TiN, and the homogeneous and densified microstructure can result in a remarkable strengthening and toughening effect. The cutting performance and wear mechanisms of the advanced multi-scale and multi-phase nanocomposite ceramic cutting tool are researched.
基金supported by the National High-Tech.R&D Programo f China(the National 863 plans projects,Grant No.2007AA03Z352)
文摘An ultrafine grained microstructure was obtained for 304 stainless steel(304SS)sheets by using surface nanocrystallization and warm-rolling.The microstructure and mechanical properties were determined by X-ray diffraction(XRD),transmission electron microscope(TEM)and a test on microhardness.Experimental results were shown that the microstructure was featured by a continuous distribution from the nanocrystalline on the surface to micro-grains in the center,in which the volume fraction of the micro-sized grains is about 40% in the surface layer.This multi-scale grained microstructure was composed of austenite and martensite phases with a gradient increasing volume fraction of austenite from the surface to the centre.The microhardness of the resultant steel was higher than 150% of that as received,due to the refined grains and strain-induced martensitic transformation.The hardness distribution was consistent with the microstructural variation,suggesting a good combination of high strength and improved ductility.
基金support from the National Natural Science Foundation of China(Grant Nos.52175307)the Taishan Scholars Foundation of Shandong Province(No.tsqn201812128)+1 种基金the Natural Science Foundation of Shandong Province(No.ZR2023JQ021No.ZR2020QE175).
文摘Superalloy thin-walled structures are achieved mainly by brazing,but the deformation process of brazed joints is non-uniform,making it a challenging research task.This paper records a thorough investigation of the effect of brazing parameters on the microstructure of joints and its mechanical properties,which mainly inquires into the deformation and fracture mechanisms in the shearing process of GH99/BNi-5a/GH99 joints.The macroscopic-microscopic deformation mechanism of the brazing interface during shearing was studied by Crystal Plasticity(CP)and Molecular Dynamics(MD)on the basis of the optimal brazing parameters.The experimental results show that the brazing interface is mainly formed by(Ni,Cr,Co)(s,s)and possesses a shear strength of approximately 546 MPa.The shearing fracture of the brazed joint occurs along the brazing seam,displaying the characteristics of intergranular fracture.MD simulations show that dislocations disassociate and transform into fine twinning with increased strain.CP simulated the shear deformation process of the brazed joint.The multiscale simulation results are consistent with the experimental results.The mechanical properties of thin-walled materials for brazing are predicted using MD and CP methods.
