While single-modal visible light images or infrared images provide limited information,infrared light captures significant thermal radiation data,whereas visible light excels in presenting detailed texture information...While single-modal visible light images or infrared images provide limited information,infrared light captures significant thermal radiation data,whereas visible light excels in presenting detailed texture information.Com-bining images obtained from both modalities allows for leveraging their respective strengths and mitigating individual limitations,resulting in high-quality images with enhanced contrast and rich texture details.Such capabilities hold promising applications in advanced visual tasks including target detection,instance segmentation,military surveillance,pedestrian detection,among others.This paper introduces a novel approach,a dual-branch decomposition fusion network based on AutoEncoder(AE),which decomposes multi-modal features into intensity and texture information for enhanced fusion.Local contrast enhancement module(CEM)and texture detail enhancement module(DEM)are devised to process the decomposed images,followed by image fusion through the decoder.The proposed loss function ensures effective retention of key information from the source images of both modalities.Extensive comparisons and generalization experiments demonstrate the superior performance of our network in preserving pixel intensity distribution and retaining texture details.From the qualitative results,we can see the advantages of fusion details and local contrast.In the quantitative experiments,entropy(EN),mutual information(MI),structural similarity(SSIM)and other results have improved and exceeded the SOTA(State of the Art)model as a whole.展开更多
In this paper, a novel efficient energy absorber with free inversion of a metal foam-filled circular tube(MFFCT) is designed, and the axial compressive behavior of the MFFCT under free inversion is studied analyticall...In this paper, a novel efficient energy absorber with free inversion of a metal foam-filled circular tube(MFFCT) is designed, and the axial compressive behavior of the MFFCT under free inversion is studied analytically and numerically. The theoretical analysis reveals that the energy is mainly dissipated through the radial bending of the metal circular tube, the circumferential expansion of the metal circular tube, and the metal filled-foam compression. The principle of energy conservation is used to derive the theoretical formula for the minimum compressive force of the MFFCT over free inversion under axial loading. Furthermore, the free inversion deformation characteristics of the MFFCT are analyzed numerically. The theoretical steady values are found to be in good agreement with the results of the finite element(FE) analysis. The effects of the average diameter of the metal tube, the wall thickness of the metal tube, and the filled-foam strength on the free inversion deformation of the MFFCT are considered. It is observed that in the steady deformation stage, the load-carrying and energy-absorbing capacities of the MFFCT increase with the increase in the average diameter of the metal tube, the wall thickness of the metal tube, or the filled-foam strength. The specific energy absorption(SEA) of free inversion of the MFFCT is significantly higher than that of the metal tube alone.展开更多
Rail fasteners are a crucial component of the railway transportation safety system.These fasteners,distinguished by their high length-to-width ratio,frequently encounter elevated failure rates,necessitating manual ins...Rail fasteners are a crucial component of the railway transportation safety system.These fasteners,distinguished by their high length-to-width ratio,frequently encounter elevated failure rates,necessitating manual inspection and maintenance.Manual inspection not only consumes time but also poses the risk of potential oversights.With the advancement of deep learning technology in rail fasteners,challenges such as the complex background of rail fasteners and the similarity in their states are addressed.We have proposed an efficient and high-precision rail fastener detection algorithm,named YOLO-O2E(you only look once-O2E).Firstly,we propose the EFOV(Enhanced Field of View)structure,aiming to adjust the effective receptive field size of convolutional kernels to enhance insensitivity to small spatial variations.Additionally,The OD_MP(ODConv and MP_2)and EMA(EfficientMulti-Scale Attention)modules mentioned in the algorithm can acquire a wider spectrum of contextual information,enhancing the model’s ability to recognize and locate objectives.Additionally,we collected and prepared the GKA dataset,sourced from real train tracks.Through testing on the GKA dataset and the publicly available NUE-DET dataset,our method outperforms general-purpose object detection algorithms.On the GKA dataset,our model achieved a mAP 0.5 value of 97.6%and a mAP 0.5:0.95 value of 83.9%,demonstrating excellent inference speed.YOLO-O2E is an algorithm for detecting anomalies in railway fasteners that is applicable in practical industrial settings,addressing the industry gap in rail fastener detection.展开更多
Semantic segmentation of driving scene images is crucial for autonomous driving.While deep learning technology has significantly improved daytime image semantic segmentation,nighttime images pose challenges due to fac...Semantic segmentation of driving scene images is crucial for autonomous driving.While deep learning technology has significantly improved daytime image semantic segmentation,nighttime images pose challenges due to factors like poor lighting and overexposure,making it difficult to recognize small objects.To address this,we propose an Image Adaptive Enhancement(IAEN)module comprising a parameter predictor(Edip),multiple image processing filters(Mdif),and a Detail Processing Module(DPM).Edip combines image processing filters to predict parameters like exposure and hue,optimizing image quality.We adopt a novel image encoder to enhance parameter prediction accuracy by enabling Edip to handle features at different scales.DPM strengthens overlooked image details,extending the IAEN module’s functionality.After the segmentation network,we integrate a Depth Guided Filter(DGF)to refine segmentation outputs.The entire network is trained end-to-end,with segmentation results guiding parameter prediction optimization,promoting self-learning and network improvement.This lightweight and efficient network architecture is particularly suitable for addressing challenges in nighttime image segmentation.Extensive experiments validate significant performance improvements of our approach on the ACDC-night and Nightcity datasets.展开更多
Text perception is crucial for understanding the semantics of outdoor scenes,making it a key requirement for building intelligent systems for driver assistance or autonomous driving.Text information in car-mounted vid...Text perception is crucial for understanding the semantics of outdoor scenes,making it a key requirement for building intelligent systems for driver assistance or autonomous driving.Text information in car-mounted videos can assist drivers in making decisions.However,Car-mounted video text images pose challenges such as complex backgrounds,small fonts,and the need for real-time detection.We proposed a robust Car-mounted Video Text Detector(CVTD).It is a lightweight text detection model based on ResNet18 for feature extraction,capable of detecting text in arbitrary shapes.Our model efficiently extracted global text positions through the Coordinate Attention Threshold Activation(CATA)and enhanced the representation capability through stacking two Feature Pyramid Enhancement Fusion Modules(FPEFM),strengthening feature representation,and integrating text local features and global position information,reinforcing the representation capability of the CVTD model.The enhanced feature maps,when acted upon by Text Activation Maps(TAM),effectively distinguished text foreground from non-text regions.Additionally,we collected and annotated a dataset containing 2200 images of Car-mounted Video Text(CVT)under various road conditions for training and evaluating our model’s performance.We further tested our model on four other challenging public natural scene text detection benchmark datasets,demonstrating its strong generalization ability and real-time detection speed.This model holds potential for practical applications in real-world scenarios.展开更多
The effectiveness of facial expression recognition(FER)algorithms hinges on the model’s quality and the availability of a substantial amount of labeled expression data.However,labeling large datasets demands signific...The effectiveness of facial expression recognition(FER)algorithms hinges on the model’s quality and the availability of a substantial amount of labeled expression data.However,labeling large datasets demands significant human,time,and financial resources.Although active learning methods have mitigated the dependency on extensive labeled data,a cold-start problem persists in small to medium-sized expression recognition datasets.This issue arises because the initial labeled data often fails to represent the full spectrum of facial expression characteristics.This paper introduces an active learning approach that integrates uncertainty estimation,aiming to improve the precision of facial expression recognition regardless of dataset scale variations.The method is divided into two primary phases.First,the model undergoes self-supervised pre-training using contrastive learning and uncertainty estimation to bolster its feature extraction capabilities.Second,the model is fine-tuned using the prior knowledge obtained from the pre-training phase to significantly improve recognition accuracy.In the pretraining phase,the model employs contrastive learning to extract fundamental feature representations from the complete unlabeled dataset.These features are then weighted through a self-attention mechanism with rank regularization.Subsequently,data from the low-weighted set is relabeled to further refine the model’s feature extraction ability.The pre-trained model is then utilized in active learning to select and label information-rich samples more efficiently.Experimental results demonstrate that the proposed method significantly outperforms existing approaches,achieving an improvement in recognition accuracy of 5.09%and 3.82%over the best existing active learning methods,Margin,and Least Confidence methods,respectively,and a 1.61%improvement compared to the conventional segmented active learning method.展开更多
With the improvement of image editing technology,the threshold of image tampering technology decreases,which leads to a decrease in the authenticity of image content.This has also driven research on image forgery dete...With the improvement of image editing technology,the threshold of image tampering technology decreases,which leads to a decrease in the authenticity of image content.This has also driven research on image forgery detection techniques.In this paper,a U-Net with multiple sensory field feature extraction(MSCU-Net)for image forgery detection is proposed.The proposed MSCU-Net is an end-to-end image essential attribute segmentation network that can perform image forgery detection without any pre-processing or post-processing.MSCU-Net replaces the single-scale convolution module in the original network with an improved multiple perceptual field convolution module so that the decoder can synthesize the features of different perceptual fields use residual propagation and residual feedback to recall the input feature information and consolidate the input feature information to make the difference in image attributes between the untampered and tampered regions more obvious,and introduce the channel coordinate confusion attention mechanism(CCCA)in skip-connection to further improve the segmentation accuracy of the network.In this paper,extensive experiments are conducted on various mainstream datasets,and the results verify the effectiveness of the proposed method,which outperforms the state-of-the-art image forgery detection methods.展开更多
Through theoretical analysis and finite element simulation,the low-velocity impact of rectangular foam-filled fiber metal laminate(FML)tubes is studied in this paper.According to the rigid-plastic material approximati...Through theoretical analysis and finite element simulation,the low-velocity impact of rectangular foam-filled fiber metal laminate(FML)tubes is studied in this paper.According to the rigid-plastic material approximation with modifications,simple analytical solutions are obtained for the dynamic response of rectangular foam-filled FML tubes.The numerical calculations for low-velocity impact of rectangular foam-filled FML tubes are conducted.The accuracy of analytical solutions and numerical results is verified by each other.Finally,the effects of the metal volume fraction of FMLs,the number of the metal layers in FMLs,and the foam strength on the dynamic response of foam-filled tubes are discussed through the analytical model in details.It is shown that the force increases with the increase in the metal volume fraction in FMLs,the number of the metal layers in FML,and the foam strength for the given deflection.展开更多
High-entropy alloys(HEAs)are considered alternatives to traditional structural materials because of their superior mechanical,physical,and chemical properties.However,alloy composition combinations are too numerous to...High-entropy alloys(HEAs)are considered alternatives to traditional structural materials because of their superior mechanical,physical,and chemical properties.However,alloy composition combinations are too numerous to explore.Finding a rapid synthesis method to accelerate the development of HEA bulks is imperative.Existing in situ synthesis methods based on additive manufacturing are insufficient for efficiently controlling the uniformity and accuracy of components.In this work,laser powder bed fusion(L-PBF)is adopted for the in situ synthesis of equiatomic CoCrFeMnNi HEA from elemental powder mixtures.High composition accuracy is achieved in parallel with ensuring internal density.The L-PBF-based process parameters are optimized;and two different methods,namely,a multi-melting process and homogenization heat treatment,are adopted to address the problem of incompletely melted Cr particles in the single-melted samples.X-ray diffraction indicates that HEA microstructure can be obtained from elemental powders via L-PBF.In the triple-melted samples,a strong crystallographic texture can be observed through electron backscatter diffraction,with a maximum polar density of 9.92 and a high ultimate tensile strength(UTS)of(735.3±14.1)MPa.The homogenization heat-treated samples appear more like coarse equiaxed grains,with a UTS of(650.8±16.1)MPa and an elongation of(40.2%±1.3%).Cellular substructures are also observed in the triple-melted samples,but not in the homogenization heat-treated samples.The differences in mechanical properties primarily originate from the changes in strengthening mechanism.The even and flat fractographic morphologies of the homogenization heat-treated samples represent a more uniform internal microstructure that is different from the complex morphologies of the triple-melted samples.Relative to the multi-melted samples,the homogenization heat-treated samples exhibit better processability,with a smaller composition deviation,i.e.,≤0.32 at.%.The two methods presented in this study are expected to have considerable potential for developing HEAs with high composition accuracy and composition flexibility.展开更多
A new concept of lightweight structure,namely amorphous-alloy-reinforced perforated armor(ARPA)consisting of the amorphous alloy coating and perforated metal substrate plate,is proposed.The ballistic performance of th...A new concept of lightweight structure,namely amorphous-alloy-reinforced perforated armor(ARPA)consisting of the amorphous alloy coating and perforated metal substrate plate,is proposed.The ballistic performance of the ARPA is investigated numerically.The failure modes of ARPA and projectiles are identified,and the defeating mechanism of the ARPA is explored.It is shown that the amorphous alloy coating is helpful for enhancing the target’s ballistic performance by seriously eroding and fracturing the penetrators.The effects of coating thickness,initial impact velocity and impact angle are also discussed for the target’s ballistic performance.The optimal design of coating thickness may be necessary for enhancing the ballistic resistance of ARPAs.展开更多
基金supported in part by the National Natural Science Foundation of China(Grant No.61971078)Chongqing Education Commission Science and Technology Major Project(No.KJZD-M202301901).
文摘While single-modal visible light images or infrared images provide limited information,infrared light captures significant thermal radiation data,whereas visible light excels in presenting detailed texture information.Com-bining images obtained from both modalities allows for leveraging their respective strengths and mitigating individual limitations,resulting in high-quality images with enhanced contrast and rich texture details.Such capabilities hold promising applications in advanced visual tasks including target detection,instance segmentation,military surveillance,pedestrian detection,among others.This paper introduces a novel approach,a dual-branch decomposition fusion network based on AutoEncoder(AE),which decomposes multi-modal features into intensity and texture information for enhanced fusion.Local contrast enhancement module(CEM)and texture detail enhancement module(DEM)are devised to process the decomposed images,followed by image fusion through the decoder.The proposed loss function ensures effective retention of key information from the source images of both modalities.Extensive comparisons and generalization experiments demonstrate the superior performance of our network in preserving pixel intensity distribution and retaining texture details.From the qualitative results,we can see the advantages of fusion details and local contrast.In the quantitative experiments,entropy(EN),mutual information(MI),structural similarity(SSIM)and other results have improved and exceeded the SOTA(State of the Art)model as a whole.
基金Project supported by the National Natural Science Foundation of China (Nos. 12272290 and11872291)the State Key Laboratory of Automotive Safety and Energy of China (No. KFY2202)。
文摘In this paper, a novel efficient energy absorber with free inversion of a metal foam-filled circular tube(MFFCT) is designed, and the axial compressive behavior of the MFFCT under free inversion is studied analytically and numerically. The theoretical analysis reveals that the energy is mainly dissipated through the radial bending of the metal circular tube, the circumferential expansion of the metal circular tube, and the metal filled-foam compression. The principle of energy conservation is used to derive the theoretical formula for the minimum compressive force of the MFFCT over free inversion under axial loading. Furthermore, the free inversion deformation characteristics of the MFFCT are analyzed numerically. The theoretical steady values are found to be in good agreement with the results of the finite element(FE) analysis. The effects of the average diameter of the metal tube, the wall thickness of the metal tube, and the filled-foam strength on the free inversion deformation of the MFFCT are considered. It is observed that in the steady deformation stage, the load-carrying and energy-absorbing capacities of the MFFCT increase with the increase in the average diameter of the metal tube, the wall thickness of the metal tube, or the filled-foam strength. The specific energy absorption(SEA) of free inversion of the MFFCT is significantly higher than that of the metal tube alone.
基金supported in part by the National Natural Science Foundation of China(Grant Number 61971078)supported by Chongqing Municipal Education Commission Grants for Major Science and Technology Project(KJZD-M202301901)the Chongqing University of Technology Graduate Innovation Foundation(Grant No.gzlcx20223222).
文摘Rail fasteners are a crucial component of the railway transportation safety system.These fasteners,distinguished by their high length-to-width ratio,frequently encounter elevated failure rates,necessitating manual inspection and maintenance.Manual inspection not only consumes time but also poses the risk of potential oversights.With the advancement of deep learning technology in rail fasteners,challenges such as the complex background of rail fasteners and the similarity in their states are addressed.We have proposed an efficient and high-precision rail fastener detection algorithm,named YOLO-O2E(you only look once-O2E).Firstly,we propose the EFOV(Enhanced Field of View)structure,aiming to adjust the effective receptive field size of convolutional kernels to enhance insensitivity to small spatial variations.Additionally,The OD_MP(ODConv and MP_2)and EMA(EfficientMulti-Scale Attention)modules mentioned in the algorithm can acquire a wider spectrum of contextual information,enhancing the model’s ability to recognize and locate objectives.Additionally,we collected and prepared the GKA dataset,sourced from real train tracks.Through testing on the GKA dataset and the publicly available NUE-DET dataset,our method outperforms general-purpose object detection algorithms.On the GKA dataset,our model achieved a mAP 0.5 value of 97.6%and a mAP 0.5:0.95 value of 83.9%,demonstrating excellent inference speed.YOLO-O2E is an algorithm for detecting anomalies in railway fasteners that is applicable in practical industrial settings,addressing the industry gap in rail fastener detection.
基金This work is supported in part by The National Natural Science Foundation of China(Grant Number 61971078),which provided domain expertise and computational power that greatly assisted the activityThis work was financially supported by Chongqing Municipal Education Commission Grants for-Major Science and Technology Project(Grant Number gzlcx20243175).
文摘Semantic segmentation of driving scene images is crucial for autonomous driving.While deep learning technology has significantly improved daytime image semantic segmentation,nighttime images pose challenges due to factors like poor lighting and overexposure,making it difficult to recognize small objects.To address this,we propose an Image Adaptive Enhancement(IAEN)module comprising a parameter predictor(Edip),multiple image processing filters(Mdif),and a Detail Processing Module(DPM).Edip combines image processing filters to predict parameters like exposure and hue,optimizing image quality.We adopt a novel image encoder to enhance parameter prediction accuracy by enabling Edip to handle features at different scales.DPM strengthens overlooked image details,extending the IAEN module’s functionality.After the segmentation network,we integrate a Depth Guided Filter(DGF)to refine segmentation outputs.The entire network is trained end-to-end,with segmentation results guiding parameter prediction optimization,promoting self-learning and network improvement.This lightweight and efficient network architecture is particularly suitable for addressing challenges in nighttime image segmentation.Extensive experiments validate significant performance improvements of our approach on the ACDC-night and Nightcity datasets.
基金This work is supported in part by the National Natural Science Foundation of China(Grant Number 61971078)which provided domain expertise and computational power that greatly assisted the activity+1 种基金This work was financially supported by Chongqing Municipal Education Commission Grants forMajor Science and Technology Project(KJZD-M202301901)the Science and Technology Research Project of Jiangxi Department of Education(GJJ2201049).
文摘Text perception is crucial for understanding the semantics of outdoor scenes,making it a key requirement for building intelligent systems for driver assistance or autonomous driving.Text information in car-mounted videos can assist drivers in making decisions.However,Car-mounted video text images pose challenges such as complex backgrounds,small fonts,and the need for real-time detection.We proposed a robust Car-mounted Video Text Detector(CVTD).It is a lightweight text detection model based on ResNet18 for feature extraction,capable of detecting text in arbitrary shapes.Our model efficiently extracted global text positions through the Coordinate Attention Threshold Activation(CATA)and enhanced the representation capability through stacking two Feature Pyramid Enhancement Fusion Modules(FPEFM),strengthening feature representation,and integrating text local features and global position information,reinforcing the representation capability of the CVTD model.The enhanced feature maps,when acted upon by Text Activation Maps(TAM),effectively distinguished text foreground from non-text regions.Additionally,we collected and annotated a dataset containing 2200 images of Car-mounted Video Text(CVT)under various road conditions for training and evaluating our model’s performance.We further tested our model on four other challenging public natural scene text detection benchmark datasets,demonstrating its strong generalization ability and real-time detection speed.This model holds potential for practical applications in real-world scenarios.
基金supported by National Science Foundation of China(61971078)Chongqing Municipal Education Commission Science and Technology Major Project(KJZDM202301901).
文摘The effectiveness of facial expression recognition(FER)algorithms hinges on the model’s quality and the availability of a substantial amount of labeled expression data.However,labeling large datasets demands significant human,time,and financial resources.Although active learning methods have mitigated the dependency on extensive labeled data,a cold-start problem persists in small to medium-sized expression recognition datasets.This issue arises because the initial labeled data often fails to represent the full spectrum of facial expression characteristics.This paper introduces an active learning approach that integrates uncertainty estimation,aiming to improve the precision of facial expression recognition regardless of dataset scale variations.The method is divided into two primary phases.First,the model undergoes self-supervised pre-training using contrastive learning and uncertainty estimation to bolster its feature extraction capabilities.Second,the model is fine-tuned using the prior knowledge obtained from the pre-training phase to significantly improve recognition accuracy.In the pretraining phase,the model employs contrastive learning to extract fundamental feature representations from the complete unlabeled dataset.These features are then weighted through a self-attention mechanism with rank regularization.Subsequently,data from the low-weighted set is relabeled to further refine the model’s feature extraction ability.The pre-trained model is then utilized in active learning to select and label information-rich samples more efficiently.Experimental results demonstrate that the proposed method significantly outperforms existing approaches,achieving an improvement in recognition accuracy of 5.09%and 3.82%over the best existing active learning methods,Margin,and Least Confidence methods,respectively,and a 1.61%improvement compared to the conventional segmented active learning method.
基金supported in part by the National Natural Science Foundation of China(Grant Number 61971078)Chongqing University of Technology Graduate Innovation Foundation(Grant Number gzlcx20222064).
文摘With the improvement of image editing technology,the threshold of image tampering technology decreases,which leads to a decrease in the authenticity of image content.This has also driven research on image forgery detection techniques.In this paper,a U-Net with multiple sensory field feature extraction(MSCU-Net)for image forgery detection is proposed.The proposed MSCU-Net is an end-to-end image essential attribute segmentation network that can perform image forgery detection without any pre-processing or post-processing.MSCU-Net replaces the single-scale convolution module in the original network with an improved multiple perceptual field convolution module so that the decoder can synthesize the features of different perceptual fields use residual propagation and residual feedback to recall the input feature information and consolidate the input feature information to make the difference in image attributes between the untampered and tampered regions more obvious,and introduce the channel coordinate confusion attention mechanism(CCCA)in skip-connection to further improve the segmentation accuracy of the network.In this paper,extensive experiments are conducted on various mainstream datasets,and the results verify the effectiveness of the proposed method,which outperforms the state-of-the-art image forgery detection methods.
基金the National Natural Science Foundation of China(Nos.11872291 and11972281)the Jiangsu Key Laboratory of Engineering Mechanics,Southeast University+2 种基金the Fundamental Research Funds for the Central Universities(No.LEM21B01)the Key Laboratory of Impact and Safety Engineering(Ningbo University),Ministry of Education(No.cj202002)the Natural Science Basic Research Plan in Shaanxi Province of China(No.2020JM-034)。
文摘Through theoretical analysis and finite element simulation,the low-velocity impact of rectangular foam-filled fiber metal laminate(FML)tubes is studied in this paper.According to the rigid-plastic material approximation with modifications,simple analytical solutions are obtained for the dynamic response of rectangular foam-filled FML tubes.The numerical calculations for low-velocity impact of rectangular foam-filled FML tubes are conducted.The accuracy of analytical solutions and numerical results is verified by each other.Finally,the effects of the metal volume fraction of FMLs,the number of the metal layers in FMLs,and the foam strength on the dynamic response of foam-filled tubes are discussed through the analytical model in details.It is shown that the force increases with the increase in the metal volume fraction in FMLs,the number of the metal layers in FML,and the foam strength for the given deflection.
文摘High-entropy alloys(HEAs)are considered alternatives to traditional structural materials because of their superior mechanical,physical,and chemical properties.However,alloy composition combinations are too numerous to explore.Finding a rapid synthesis method to accelerate the development of HEA bulks is imperative.Existing in situ synthesis methods based on additive manufacturing are insufficient for efficiently controlling the uniformity and accuracy of components.In this work,laser powder bed fusion(L-PBF)is adopted for the in situ synthesis of equiatomic CoCrFeMnNi HEA from elemental powder mixtures.High composition accuracy is achieved in parallel with ensuring internal density.The L-PBF-based process parameters are optimized;and two different methods,namely,a multi-melting process and homogenization heat treatment,are adopted to address the problem of incompletely melted Cr particles in the single-melted samples.X-ray diffraction indicates that HEA microstructure can be obtained from elemental powders via L-PBF.In the triple-melted samples,a strong crystallographic texture can be observed through electron backscatter diffraction,with a maximum polar density of 9.92 and a high ultimate tensile strength(UTS)of(735.3±14.1)MPa.The homogenization heat-treated samples appear more like coarse equiaxed grains,with a UTS of(650.8±16.1)MPa and an elongation of(40.2%±1.3%).Cellular substructures are also observed in the triple-melted samples,but not in the homogenization heat-treated samples.The differences in mechanical properties primarily originate from the changes in strengthening mechanism.The even and flat fractographic morphologies of the homogenization heat-treated samples represent a more uniform internal microstructure that is different from the complex morphologies of the triple-melted samples.Relative to the multi-melted samples,the homogenization heat-treated samples exhibit better processability,with a smaller composition deviation,i.e.,≤0.32 at.%.The two methods presented in this study are expected to have considerable potential for developing HEAs with high composition accuracy and composition flexibility.
基金The authors gratefully acknowledge the financial support of NSFC(11972281,11872291,11572234,11502189)Opening Project of Science and Technology on Transient Impact Laboratory(614260601010117)+1 种基金Natural Science Basic Research Plan in Shaanxi Province of China(2020JM-034)China Postdoctoral Science Foundation funded project(2018M643621).
文摘A new concept of lightweight structure,namely amorphous-alloy-reinforced perforated armor(ARPA)consisting of the amorphous alloy coating and perforated metal substrate plate,is proposed.The ballistic performance of the ARPA is investigated numerically.The failure modes of ARPA and projectiles are identified,and the defeating mechanism of the ARPA is explored.It is shown that the amorphous alloy coating is helpful for enhancing the target’s ballistic performance by seriously eroding and fracturing the penetrators.The effects of coating thickness,initial impact velocity and impact angle are also discussed for the target’s ballistic performance.The optimal design of coating thickness may be necessary for enhancing the ballistic resistance of ARPAs.
