We discuss the physical layer security scheme in the Full-Duplex(FD)MIMO point-to-point two-way communication system with residual self-interference,in which legitimate nodes send confidential information and null spa...We discuss the physical layer security scheme in the Full-Duplex(FD)MIMO point-to-point two-way communication system with residual self-interference,in which legitimate nodes send confidential information and null space Artificial Noise(AN)while receiving information.Because the Channel State Information(CSI)of the eavesdropper is unavailable,we optimize the covariance matrices of the information signal as well as the allocation of the antenna for transmitting and receiving to minimize the signal power consumption under the target rate constraint.As a result,the power of AN is maximized within the limit of total power,so the interception capability of the eavesdropper is suppressed as much as possible.Since self-interference cannot be completely eliminated,the optimization process of one legitimate node depends on the optimization result of the other.By substituting self-interference power in the secrecy rate formula with its average value,the joint optimization process at the two nodes is transformed into two separate and solvable optimization processes.Then,the Water-Filling Algorithm(WFA)and bisection algorithm are used to get the optimal covariance matrices of the signal.Furthermore,we derive the theoretical lower bound of ergodic achievable secrecy rate under rayleigh channels to evaluate the performance of the scheme.The simulation results show that the theoretical derivation is correct,and the actual achievable rate is very close to the target rate,which means that the approximation in the optimization is feasible.The results also show that secrecy transmission can be realized because a considerable secrecy rate can be achieved.展开更多
Medical image segmentation is of critical importance in the domain of contemporary medical imaging.However,U-Net and its variants exhibit limitations in capturing complex nonlinear patterns and global contextual infor...Medical image segmentation is of critical importance in the domain of contemporary medical imaging.However,U-Net and its variants exhibit limitations in capturing complex nonlinear patterns and global contextual information.Although the subsequent U-KAN model enhances nonlinear representation capabilities,it still faces challenges such as gradient vanishing during deep network training and spatial detail loss during feature downsampling,resulting in insufficient segmentation accuracy for edge structures and minute lesions.To address these challenges,this paper proposes the RE-UKAN model,which innovatively improves upon U-KAN.Firstly,a residual network is introduced into the encoder to effectively mitigate gradient vanishing through cross-layer identity mappings,thus enhancing modelling capabilities for complex pathological structures.Secondly,Efficient Local Attention(ELA)is integrated to suppress spatial detail loss during downsampling,thereby improving the perception of edge structures and minute lesions.Experimental results on four public datasets demonstrate that RE-UKAN outperforms existing medical image segmentation methods across multiple evaluation metrics,with particularly outstanding performance on the TN-SCUI 2020 dataset,achieving IoU of 88.18%and Dice of 93.57%.Compared to the baseline model,it achieves improvements of 3.05%and 1.72%,respectively.These results fully demonstrate RE-UKAN’s superior detail retention capability and boundary recognition accuracy in complex medical image segmentation tasks,providing a reliable solution for clinical precision segmentation.展开更多
Recent advances in deep learning have significantly improved image deblurring;however,existing approaches still suffer from limited global context modeling,inadequate detail restoration,and poor texture or edge percep...Recent advances in deep learning have significantly improved image deblurring;however,existing approaches still suffer from limited global context modeling,inadequate detail restoration,and poor texture or edge perception,especially under complex dynamic blur.To address these challenges,we propose the Multi-Resolution Fusion Network(MRFNet),a blind multi-scale deblurring framework that integrates progressive residual connectivity for hierarchical feature fusion.The network employs a three-stage design:(1)TransformerBlocks capture long-range dependencies and reconstruct coarse global structures;(2)Nonlinear Activation Free Blocks(NAFBlocks)enhance local detail representation and mid-level feature fusion;and(3)an optimized residual subnetwork based on gated feature modulation refines texture and edge details for high-fidelity restoration.Extensive experiments demonstrate that MRFNet achieves superior performance compared to state-of-the-art methods.On GoPro,it attains 32.52 dB Peak Signal-to-Noise Ratio(PSNR)and 0.071 Learned Perceptual Image Patch Similarity(LPIPS),outperforming MIMOWNet(32.50 dB,0.075).On HIDE,it achieves 30.25 dB PSNR and 0.945 Structural Similarity Index Measure(SSIM),representing gains of+0.26 dB and+0.015 SSIM over MIMO-UNet(29.99 dB,0.930).On RealBlur-J,it reaches 28.82 dB PSNR and 0.872 SSIM,surpassing MIMO-UNet by+1.19 dB and+0.035 SSIM(27.63 dB,0.837).These results validate the effectiveness of the proposed progressive residual fusion and hybrid attention mechanisms in balancing global context understanding and local detail recovery for blind image deblurring.展开更多
The curing behavior of composites significantly influences their performance,making it crucial to understand the curing process.This study experimentally measured specific heat capacity,thermal conductivity,glass tran...The curing behavior of composites significantly influences their performance,making it crucial to understand the curing process.This study experimentally measured specific heat capacity,thermal conductivity,glass transition temperature,coefficient of thermal expansion,and cure shrinkage of materials.A simulation model of its curing deformation was established and validated against strain data obtained from fiber Bragg grating experiments.The effects of thickness,heating rate,and cooling rate on the curing temperature field and residual stress field during the molding of thick-section composite plates were analyzed.展开更多
Aiming at the problem of potential information noise introduced during the generation of ghost feature maps in GhostNet,this paper proposes a novel lightweight neural network model called ResghostNet.This model constr...Aiming at the problem of potential information noise introduced during the generation of ghost feature maps in GhostNet,this paper proposes a novel lightweight neural network model called ResghostNet.This model constructs the Resghost Module by combining residual connections and Adaptive-SE Blocks,which enhances the quality of generated feature maps through direct propagation of original input information and selection of important channels before cheap operations.Specifically,ResghostNet introduces residual connections on the basis of the Ghost Module to optimize the information flow,and designs a weight self-attention mechanism combined with SE blocks to enhance feature expression capabilities in cheap operations.Experimental results on the ImageNet dataset show that,compared to GhostNet,ResghostNet achieves higher accuracy while reducing the number of parameters by 52%.Although the computational complexity increases,by optimizing the usage strategy of GPU cachememory,themodel’s inference speed becomes faster.The ResghostNet is optimized in terms of classification accuracy and the number of model parameters,and shows great potential in edge computing devices.展开更多
In daily life,keyword spotting plays an important role in human-computer interaction.However,noise often interferes with the extraction of time-frequency information,and achieving both computational efficiency and rec...In daily life,keyword spotting plays an important role in human-computer interaction.However,noise often interferes with the extraction of time-frequency information,and achieving both computational efficiency and recognition accuracy on resource-constrained devices such as mobile terminals remains a major challenge.To address this,we propose a novel time-frequency dual-branch parallel residual network,which integrates a Dual-Branch Broadcast Residual module and a Time-Frequency Coordinate Attention module.The time-domain and frequency-domain branches are designed in parallel to independently extract temporal and spectral features,effectively avoiding the potential information loss caused by serial stacking,while enhancing information flow and multi-scale feature fusion.In terms of training strategy,a curriculum learning approach is introduced to progressively improve model robustness fromeasy to difficult tasks.Experimental results demonstrate that the proposed method consistently outperforms existing lightweight models under various signal-to-noise ratio(SNR)conditions,achieving superior far-field recognition performance on the Google Speech Commands V2 dataset.Notably,the model maintains stable performance even in low-SNR environments such as–10 dB,and generalizes well to unseen SNR conditions during training,validating its robustness to novel noise scenarios.Furthermore,the proposed model exhibits significantly fewer parameters,making it highly suitable for deployment on resource-limited devices.Overall,the model achieves a favorable balance between performance and parameter efficiency,demonstrating strong potential for practical applications.展开更多
High-resolution remote sensing imagery is essential for critical applications such as precision agriculture,urban management planning,and military reconnaissance.Although significant progress has been made in singleim...High-resolution remote sensing imagery is essential for critical applications such as precision agriculture,urban management planning,and military reconnaissance.Although significant progress has been made in singleimage super-resolution(SISR)using generative adversarial networks(GANs),existing approaches still face challenges in recovering high-frequency details,effectively utilizing features,maintaining structural integrity,and ensuring training stability—particularly when dealing with the complex textures characteristic of remote sensing imagery.To address these limitations,this paper proposes the Improved ResidualModule and AttentionMechanism Network(IRMANet),a novel architecture specifically designed for remote sensing image reconstruction.IRMANet builds upon the Super-Resolution Generative Adversarial Network(SRGAN)framework and introduces several key innovations.First,the Enhanced Residual Unit(ERU)enhances feature reuse and stabilizes training through deep residual connections.Second,the Self-Attention Residual Block(SARB)incorporates a self-attentionmechanism into the Improved Residual Module(IRM)to effectivelymodel long-range dependencies and automatically emphasize salient features.Additionally,the IRM adopts amulti-scale feature fusion strategy to facilitate synergistic interactions between local detail and global semantic information.The effectiveness of each component is validated through ablation studies,while comprehensive comparative experiments on standard remote sensing datasets demonstrate that IRMANet significantly outperforms both the baseline and state-of-the-art methods in terms of perceptual quality and quantitative metrics.Specifically,compared to the baseline model,at a magnification factor of 2,IRMANet achieves an improvement of 0.24 dB in peak signal-to-noise ratio(PSNR)and 0.54 in structural similarity index(SSIM);at a magnification factor of 4,it achieves gains of 0.22 dB in PSNR and 0.51 in SSIM.These results confirm that the proposedmethod effectively enhances detail representation and structural reconstruction accuracy in complex remote sensing scenarios,offering robust technical support for high-precision detection and identification of both military and civilian aircraft.展开更多
The neutron diffusion equation plays a pivotal role in nuclear reactor analysis.Nevertheless,employing the physics-informed neural network(PINN)method for its solution entails certain limitations.Conventional PINN app...The neutron diffusion equation plays a pivotal role in nuclear reactor analysis.Nevertheless,employing the physics-informed neural network(PINN)method for its solution entails certain limitations.Conventional PINN approaches generally utilize a fully connected network(FCN)architecture that is susceptible to overfitting,training instability,and gradient vanishing as the network depth increases.These challenges result in accuracy bottlenecks in the solution.In response to these issues,the residual-based resample physics-informed neural network(R2-PINN)is proposed.It is an improved PINN architecture that replaces the FCN with a convolutional neural network with a shortcut(S-CNN).It incorporates skip connections to facilitate gradient propagation between network layers.Additionally,the incorporation of the residual adaptive resampling(RAR)mechanism dynamically increases the number of sampling points.This,in turn,enhances the spatial representation capabilities and overall predictive accuracy of the model.The experimental results illustrate that our approach significantly improves the convergence capability of the model and achieves high-precision predictions of the physical fields.Compared with conventional FCN-based PINN methods,R 2-PINN effectively overcomes the limitations inherent in current methods.Thus,it provides more accurate and robust solutions for neutron diffusion equations.展开更多
Statistical distribution of residual fatigue life(RFL)of railway axles under given loading was computed using the Monte Carlo method by considering random variation of the selected input parameters.Experimental data f...Statistical distribution of residual fatigue life(RFL)of railway axles under given loading was computed using the Monte Carlo method by considering random variation of the selected input parameters.Experimental data for the EA4T railway axle steel,the loading spectrum,the press fit loading and the residual stress induced by surface hardening were considered in the crack propagation simulations.Usually,the material properties measured by tensile tests are considered to be the most informative source of material data.Under fatigue loading,however,the crack growth rates near the threshold are the most critical data.Two important influencing factors on these crack growth rates are presented:first,the air humidity and,second,the near-surface residual stress.The typical variation of these parameters in operation may change the RFL by one or two orders of magnitude.Experimentally obtained crack growth thresholds and residual stress profiles are highly affected by the used methodology.Therefore,the obtained input data may be located anywhere within a large scatter,while the experimenters are completely unaware of it.This can lead to dangerously non-conservative situations,e.g.when the thresholds are measured in a laboratory under humid air conditions and then applied to predictions of RFLs of axles operated in winter in low air humidity.This is significant for the topic of inspection interval optimisation.The results of experiments done on real 1:1 railway axles were close to the most frequent value found in the histogram of the numerically computed RFLs.展开更多
This study proposes a multi-scale simplified residual convolutional neural network(MS-SRCNN)for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope(SEM)images.A multi-scale data...This study proposes a multi-scale simplified residual convolutional neural network(MS-SRCNN)for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope(SEM)images.A multi-scale data structure is established by spatially aligning and stacking SEM images at different magnifications.The MS-SRCNN significantly reduces computational runtime by over 90%compared to traditional architectures like ResNet50,VGG16,and VGG19,without compromising prediction accuracy.The model demonstrates more excellent predictive performance,achieving a>5%increase in R^(2) compared to single-scale models.Furthermore,the MS-SRCNN exhibits robust composition prediction capability across other Mg-based binary alloys,including Mg-La,Mg-Sn,Mg-Ce,Mg-Sm,Mg-Ag,and Mg-Y,thereby emphasizing its generalization and extrapolation potential.This research establishes a non-destructive,microstructure-informed composition analysis framework,reduces characterization time compared to traditional experiment methods and provides insights into the composition-microstructure relationship in diverse material systems.展开更多
This letter introduces the novel concept of Painlevé solitons—waves arising from the interaction between Painlevé waves and solitons in integrable systems.Painlevé solitons can also be viewed as solito...This letter introduces the novel concept of Painlevé solitons—waves arising from the interaction between Painlevé waves and solitons in integrable systems.Painlevé solitons can also be viewed as solitons propagating against a Painlevé wave background,in analogy to the established notion of elliptic solitons,which refers to solitons on an elliptic wave background.By employing a novel symmetry decomposition method aided by nonlocal residual symmetries,we explicitly construct (extended) Painlevé Ⅱ solitons for the Korteweg-de Vries equation and (extended) Painlevé Ⅳ solitons for the Boussinesq equation.展开更多
Despite the superior advantages of specific emitter identification in extracting emitter features from in-phase and quadrature(I/Q)signals,challenges persist due to signal-type confusion and background noise interfere...Despite the superior advantages of specific emitter identification in extracting emitter features from in-phase and quadrature(I/Q)signals,challenges persist due to signal-type confusion and background noise interference.To address those limitations,this paper proposes a multi-channel contrast prediction coding and complex-valued residuals network(MCPC-MCVResNet)framework.This model employs contrast prediction techniques to directly extract discriminative features from electromagnetic signal sequences,effectively capturing both amplitude and phase information within I/Q data.A core innovation of this approach is the sphere space softmax(SS-softmax)loss,which optimizes intra-class clustering density of while establishing well-defined boundaries between distinct emitters.The SS-softmax mechanism significantly enhances the model's capacity to discern subtle variations among radiation emitters.Experimental results demonstrate superior identification accuracy,rapid convergence,and exceptional robustness in low signal-to-noise ratio environments.展开更多
The biodegradable polybutylene succinate(PBS)material offers a sustainable solution for a circular economy to address the global issue of marine plastic waste.Its cross-linkage with non-biodegradable xanthan gum(XG)bi...The biodegradable polybutylene succinate(PBS)material offers a sustainable solution for a circular economy to address the global issue of marine plastic waste.Its cross-linkage with non-biodegradable xanthan gum(XG)biopolymer to ameliorate residual granitic soil(RGS)in arid and semiarid regions can significantly mitigate soil erosion.This study investigates the enhancement of RGS by cross-linking the PBS and XG biopolymers.Employing a multitude of geotechnical tests(liquid limit,linear shrinkage,specific gravity,compaction,and UCS tests)at 3 d,28 d,and 90 d of steam-curing at a controlled temperature of 16℃,the outcomes were validated through scanning electron microscopy(SEM),thermogravimetric analysis(TGA),Fourier transform infrared spectroscopy(FTIR),and Brunauer-Emmett-Teller(BET)analyses.In addition,a comprehensive experimental database of 150 tests and nine parameters from the current study was utilized to model the UCS90-d(i.e.unconfined compressive strength after 90 d of curing)of the PBS-XG-treated RGS mixtures by deploying the random forest(RF)and eXtreme Gradient Boost(XGBoost)methods.The results found that the two biopolymers significantly improve the mechanical properties of RGS,with optimal UCS achieved at specific dosages(0.4PBS,1.5XG,and 0.2PBS+1.5XG dosage levels)and curing times.The UCS of PBS-XG-treated RGS showed up to a 57%increase after 90 d of curing.Furthermore,SEM and FTIR analyses revealed the formation of stronger microstructures and chemical bonds,respectively,whereas BET analysis indicated that pore volume and diameter are critical in affecting UCS.The proposed RF model outperformed XGBoost in predictive accuracy and generalization,demonstrating robustness and versatility.Moreover,SHAP values highlighted the significant impact of input parameters on UCS90-d,with curing time and specific material properties being key determinants.The study concludes with the proposal of a novel PyCharm intuitive graphical user interface as a"UCS Prediction App"for engineers and practitioners to forecast the UCS90-d of granitic residual soil.展开更多
BACKGROUND At present,the influencing factors of social function in patients with residual depressive symptoms are still unclear.Residual depressive symptoms are highly harmful,leading to low mood in patients,affectin...BACKGROUND At present,the influencing factors of social function in patients with residual depressive symptoms are still unclear.Residual depressive symptoms are highly harmful,leading to low mood in patients,affecting work and interpersonal communication,increasing the risk of recurrence,and adding to the burden on families.Studying the influencing factors of their social function is of great significance.AIM To explore the social function score and its influencing factors in patients with residual depressive symptoms.METHODS This observational study surveyed patients with residual depressive symptoms(case group)and healthy patients undergoing physical examinations(control group).Participants were admitted between January 2022 and December 2023.Social functioning was assessed using the Sheehan Disability Scale(SDS),and scores were compared between groups.Factors influencing SDS scores in patients with residual depressive symptoms were analyzed by applying multiple linear regression while using the receiver operating characteristic curve,and these RESULTS The SDS scores of the 158 patients with depressive symptoms were 11.48±3.26.Compared with the control group,the SDS scores and all items in the case group were higher.SDS scores were higher in patients with relapse,discon-tinuous medication,drug therapy alone,severe somatic symptoms,obvious residual symptoms,and anxiety scores≥8.Disease history,medication compliance,therapy method,and residual symptoms correlated positively with SDS scores(r=0.354,0.414,0.602,and 0.456,respectively).Independent influencing factors included disease history,medication compliance,therapy method,somatic symptoms,residual symptoms,and anxiety scores(P<0.05).The areas under the curve for predicting social functional impairment using these factors were 0.713,0.559,0.684,0.729,0.668,and 0.628,respectively,with sensitivities of 79.2%,61.8%,76.8%,81.7%,63.6%,and 65.5%and specificities of 83.3%,87.5%,82.6%,83.3%,86.7%,and 92.1%,respectively.CONCLUSION The social function scores of patients with residual symptoms of depression are high.They are affected by disease history,medication compliance,therapy method,degree of somatic symptoms,residual symptoms,and anxiety.展开更多
Producing steel requires large amounts of energy to convert iron ores into steel,which often comes from fossil fuels,leading to carbon emissions and other pollutants.Increasing scrap usage emerges as one of the most e...Producing steel requires large amounts of energy to convert iron ores into steel,which often comes from fossil fuels,leading to carbon emissions and other pollutants.Increasing scrap usage emerges as one of the most effective strategies for addressing these issues.However,typical residual elements(Cu,As,Sn,Sb,Bi,etc.)inherited from scrap could significantly influence the mechanical properties of steel.In this work,we investigate the effects of residual elements on the microstructure evolution and mechanical properties of a quenching and partitioning(Q&P)steel by comparing a commercial QP1180 steel(referred to as QP)to the one containing typical residual elements(Cu+As+Sn+Sb+Bi<0.3wt%)(referred to as QP-R).The results demonstrate that in comparison with the QP steel,the residual elements significantly refine the prior austenite grain(9.7μm vs.14.6μm)due to their strong solute drag effect,leading to a higher volume fraction(13.0%vs.11.8%),a smaller size(473 nm vs.790 nm)and a higher average carbon content(1.26 wt%vs.0.99 wt%)of retained austenite in the QP-R steel.As a result,the QP-R steel exhibits a sustained transformation-induced plasticity(TRIP)effect,leading to an enhanced strain hardening effect and a simultaneous improvement of strength and ductility.Grain boundary segregation of residual elements was not observed at prior austenite grain boundaries in the QP-R steel,primarily due to continuous interface migration during austenitization.This study demonstrates that the residual elements with concentrations comparable to that in scrap result in significant microstructural refinement,causing retained austenite with relatively higher stability and thus offering promising mechanical properties and potential applications.展开更多
The dominated contradiction in optimizing the performance of magnesium-air battery anode lies in the difficulty of achieving a good balance between activation and passivation during discharge process.To further reconci...The dominated contradiction in optimizing the performance of magnesium-air battery anode lies in the difficulty of achieving a good balance between activation and passivation during discharge process.To further reconcile this contradiction,two Mg-0.1Sc-0.1Y-0.1Ag anodes with different residual strain distribution through extrusion with/without annealing are fabricated.The results indicate that annealing can significantly lessen the“pseudo-anode”regions,thereby changing the dissolution mode of the matrix and achieving an effective dissolution during discharge.Additionally,p-type semiconductor characteristic of discharge productfilm could suppress the self-corrosion reaction without reducing the polarization of anode.The magnesium-air battery utilizing annealed Mg-0.1Sc-0.1Y-0.1Ag as anode achieves a synergistic improvement in specific capacity(1388.89 mA h g^(-1))and energy density(1960.42 mW h g^(-1)).This anode modification method accelerates the advancement of high efficiency and long lifespan magnesium-air batteries,offering renewable and cost-effective energy solutions for electronics and emergency equipment.展开更多
Laser-welded Ti-6Al-4 V is prone to severe residual stresses,microstructural variation,and structural de-fects which are known detrimental to the mechanical properties of weld joints.Residual stress removal is typical...Laser-welded Ti-6Al-4 V is prone to severe residual stresses,microstructural variation,and structural de-fects which are known detrimental to the mechanical properties of weld joints.Residual stress removal is typically applied to weld joints for engineering purposes via heat treatment,in order to avoid prema-ture failure and performance degradation.In the present work,we found that proper welding residual stresses in laser-welded Ti-6Al-4 V sheets can maintain better ductility during uniaxial tension,as op-posed to the stress-relieved counterparts.A detailed experimental investigation has been performed on the deformation behaviours of Ti-6Al-4 V butt welds,including residual stress distribution characteriza-tions by focused ion beam ring-coring coupled with digital image correlation(FIB-DIC),X-ray comput-erized tomography(CT)for internal voids,and in-situ DIC analysis of the subregional strain evolutions.It was found that the pores preferentially distributed near the fusion zone(FZ)boundary,where the compressive residual stress was up to-330 MPa.The removal of residual stress resulted in a changed failure initiation site from the base material to the FZ boundary,the former with ductile and the latter with brittle fracture characteristics under tensile deformation.The combined effects of residual stresses,microstructures,and internal pores on the mechanical responses are discussed in detail.This work high-lights the importance of inevitable residual stress and pores in laser weld pieces,leading to key insights for post-welding treatment and service performance evaluations.展开更多
This study investigates the effect of high current density electropulsing on the material in a rapid stress relaxation process.An AISI 1020 steel was shot-peened to induce surface compressive residual stresses in a co...This study investigates the effect of high current density electropulsing on the material in a rapid stress relaxation process.An AISI 1020 steel was shot-peened to induce surface compressive residual stresses in a controlled manner and subsequently electropulsed to investigate the changes in microstructure and defect configuration.AISI 1020 steel was chosen as it has a simple microstructure(plain ferritic)and composition with low alloying conditions.It is an appropriate material to study the effect of trans-mitting electric pulses on the microstructural defect evolution.A combination of electron-backscattered diffraction and transmission electron microscopy proved to be an effective tool in characterizing the post-electropulsing effects critically.By application of electropulsing,a reduction in the surface residual stress layer was noticed.Also,reductions in misorientation and dislocation density together with the disentan-glement of dislocations within the cold-worked layer were observed after electropulsing.Additionally,the annihilation of shot-peening-induced deformation bands beyond the residual layer depth was observed.These effects have been rationalised by taking into account the various possibilities of athermal effects of electropulsing.展开更多
To investigate the residual stress distribution and its influence on machining deformation in 6061-T651 aluminum alloy plates,this paper uses the crack compliance method to study the residual stress characteristics of...To investigate the residual stress distribution and its influence on machining deformation in 6061-T651 aluminum alloy plates,this paper uses the crack compliance method to study the residual stress characteristics of 6061-T651 aluminum alloy plates with a thickness of 75 mm produced by two domestic manufacturers in China.The results indicate that both types of plates exhibit highly consistent and symmetrical M-shaped residual stress profile along the thickness direction,manifested as surface layer compression and core tension.The strain energy density across all specimens ranges from 1.27 kJ/m^(3)to 1.43 kJ/m^(3).Machining deformation simulations of an aerospace component incorporating these measured stresses showed minimal final deformation difference between the material sources,with a maximum deviation of only 0.009 mm across specimens.These findings provide critical data for material selection and deformation control in aerospace manufacturing.展开更多
基金This work was supported by the National Nature Science Foundation of China(No.61971080,No.61471076)Chongqing Research Program of Basic Research and Frontier Exploration(No.cstc2018jcyjAX0432)the Key Project of Science and Technology Research of Chongqing Education Commission(No.KJZD-K201800603,No.KJZD-M201900602).
文摘We discuss the physical layer security scheme in the Full-Duplex(FD)MIMO point-to-point two-way communication system with residual self-interference,in which legitimate nodes send confidential information and null space Artificial Noise(AN)while receiving information.Because the Channel State Information(CSI)of the eavesdropper is unavailable,we optimize the covariance matrices of the information signal as well as the allocation of the antenna for transmitting and receiving to minimize the signal power consumption under the target rate constraint.As a result,the power of AN is maximized within the limit of total power,so the interception capability of the eavesdropper is suppressed as much as possible.Since self-interference cannot be completely eliminated,the optimization process of one legitimate node depends on the optimization result of the other.By substituting self-interference power in the secrecy rate formula with its average value,the joint optimization process at the two nodes is transformed into two separate and solvable optimization processes.Then,the Water-Filling Algorithm(WFA)and bisection algorithm are used to get the optimal covariance matrices of the signal.Furthermore,we derive the theoretical lower bound of ergodic achievable secrecy rate under rayleigh channels to evaluate the performance of the scheme.The simulation results show that the theoretical derivation is correct,and the actual achievable rate is very close to the target rate,which means that the approximation in the optimization is feasible.The results also show that secrecy transmission can be realized because a considerable secrecy rate can be achieved.
文摘Medical image segmentation is of critical importance in the domain of contemporary medical imaging.However,U-Net and its variants exhibit limitations in capturing complex nonlinear patterns and global contextual information.Although the subsequent U-KAN model enhances nonlinear representation capabilities,it still faces challenges such as gradient vanishing during deep network training and spatial detail loss during feature downsampling,resulting in insufficient segmentation accuracy for edge structures and minute lesions.To address these challenges,this paper proposes the RE-UKAN model,which innovatively improves upon U-KAN.Firstly,a residual network is introduced into the encoder to effectively mitigate gradient vanishing through cross-layer identity mappings,thus enhancing modelling capabilities for complex pathological structures.Secondly,Efficient Local Attention(ELA)is integrated to suppress spatial detail loss during downsampling,thereby improving the perception of edge structures and minute lesions.Experimental results on four public datasets demonstrate that RE-UKAN outperforms existing medical image segmentation methods across multiple evaluation metrics,with particularly outstanding performance on the TN-SCUI 2020 dataset,achieving IoU of 88.18%and Dice of 93.57%.Compared to the baseline model,it achieves improvements of 3.05%and 1.72%,respectively.These results fully demonstrate RE-UKAN’s superior detail retention capability and boundary recognition accuracy in complex medical image segmentation tasks,providing a reliable solution for clinical precision segmentation.
基金funded by Qinghai University Postgraduate Research and Practice Innovation Program of Funder,grant number 2025-GMKY-42.
文摘Recent advances in deep learning have significantly improved image deblurring;however,existing approaches still suffer from limited global context modeling,inadequate detail restoration,and poor texture or edge perception,especially under complex dynamic blur.To address these challenges,we propose the Multi-Resolution Fusion Network(MRFNet),a blind multi-scale deblurring framework that integrates progressive residual connectivity for hierarchical feature fusion.The network employs a three-stage design:(1)TransformerBlocks capture long-range dependencies and reconstruct coarse global structures;(2)Nonlinear Activation Free Blocks(NAFBlocks)enhance local detail representation and mid-level feature fusion;and(3)an optimized residual subnetwork based on gated feature modulation refines texture and edge details for high-fidelity restoration.Extensive experiments demonstrate that MRFNet achieves superior performance compared to state-of-the-art methods.On GoPro,it attains 32.52 dB Peak Signal-to-Noise Ratio(PSNR)and 0.071 Learned Perceptual Image Patch Similarity(LPIPS),outperforming MIMOWNet(32.50 dB,0.075).On HIDE,it achieves 30.25 dB PSNR and 0.945 Structural Similarity Index Measure(SSIM),representing gains of+0.26 dB and+0.015 SSIM over MIMO-UNet(29.99 dB,0.930).On RealBlur-J,it reaches 28.82 dB PSNR and 0.872 SSIM,surpassing MIMO-UNet by+1.19 dB and+0.035 SSIM(27.63 dB,0.837).These results validate the effectiveness of the proposed progressive residual fusion and hybrid attention mechanisms in balancing global context understanding and local detail recovery for blind image deblurring.
基金supported by the National Natural Science Foundation of China(Grant Nos.12172045,U2241240,and 12221002)the National Program on Key Basic Research Project,China(Grant No.2019-JCJQ-ZD-308-00).
文摘The curing behavior of composites significantly influences their performance,making it crucial to understand the curing process.This study experimentally measured specific heat capacity,thermal conductivity,glass transition temperature,coefficient of thermal expansion,and cure shrinkage of materials.A simulation model of its curing deformation was established and validated against strain data obtained from fiber Bragg grating experiments.The effects of thickness,heating rate,and cooling rate on the curing temperature field and residual stress field during the molding of thick-section composite plates were analyzed.
基金funded by Science and Technology Innovation Project grant No.ZZKY20222304.
文摘Aiming at the problem of potential information noise introduced during the generation of ghost feature maps in GhostNet,this paper proposes a novel lightweight neural network model called ResghostNet.This model constructs the Resghost Module by combining residual connections and Adaptive-SE Blocks,which enhances the quality of generated feature maps through direct propagation of original input information and selection of important channels before cheap operations.Specifically,ResghostNet introduces residual connections on the basis of the Ghost Module to optimize the information flow,and designs a weight self-attention mechanism combined with SE blocks to enhance feature expression capabilities in cheap operations.Experimental results on the ImageNet dataset show that,compared to GhostNet,ResghostNet achieves higher accuracy while reducing the number of parameters by 52%.Although the computational complexity increases,by optimizing the usage strategy of GPU cachememory,themodel’s inference speed becomes faster.The ResghostNet is optimized in terms of classification accuracy and the number of model parameters,and shows great potential in edge computing devices.
文摘In daily life,keyword spotting plays an important role in human-computer interaction.However,noise often interferes with the extraction of time-frequency information,and achieving both computational efficiency and recognition accuracy on resource-constrained devices such as mobile terminals remains a major challenge.To address this,we propose a novel time-frequency dual-branch parallel residual network,which integrates a Dual-Branch Broadcast Residual module and a Time-Frequency Coordinate Attention module.The time-domain and frequency-domain branches are designed in parallel to independently extract temporal and spectral features,effectively avoiding the potential information loss caused by serial stacking,while enhancing information flow and multi-scale feature fusion.In terms of training strategy,a curriculum learning approach is introduced to progressively improve model robustness fromeasy to difficult tasks.Experimental results demonstrate that the proposed method consistently outperforms existing lightweight models under various signal-to-noise ratio(SNR)conditions,achieving superior far-field recognition performance on the Google Speech Commands V2 dataset.Notably,the model maintains stable performance even in low-SNR environments such as–10 dB,and generalizes well to unseen SNR conditions during training,validating its robustness to novel noise scenarios.Furthermore,the proposed model exhibits significantly fewer parameters,making it highly suitable for deployment on resource-limited devices.Overall,the model achieves a favorable balance between performance and parameter efficiency,demonstrating strong potential for practical applications.
基金funded by the Henan Province Key R&D Program Project,“Research and Application Demonstration of Class Ⅱ Superlattice Medium Wave High Temperature Infrared Detector Technology”,grant number 231111210400.
文摘High-resolution remote sensing imagery is essential for critical applications such as precision agriculture,urban management planning,and military reconnaissance.Although significant progress has been made in singleimage super-resolution(SISR)using generative adversarial networks(GANs),existing approaches still face challenges in recovering high-frequency details,effectively utilizing features,maintaining structural integrity,and ensuring training stability—particularly when dealing with the complex textures characteristic of remote sensing imagery.To address these limitations,this paper proposes the Improved ResidualModule and AttentionMechanism Network(IRMANet),a novel architecture specifically designed for remote sensing image reconstruction.IRMANet builds upon the Super-Resolution Generative Adversarial Network(SRGAN)framework and introduces several key innovations.First,the Enhanced Residual Unit(ERU)enhances feature reuse and stabilizes training through deep residual connections.Second,the Self-Attention Residual Block(SARB)incorporates a self-attentionmechanism into the Improved Residual Module(IRM)to effectivelymodel long-range dependencies and automatically emphasize salient features.Additionally,the IRM adopts amulti-scale feature fusion strategy to facilitate synergistic interactions between local detail and global semantic information.The effectiveness of each component is validated through ablation studies,while comprehensive comparative experiments on standard remote sensing datasets demonstrate that IRMANet significantly outperforms both the baseline and state-of-the-art methods in terms of perceptual quality and quantitative metrics.Specifically,compared to the baseline model,at a magnification factor of 2,IRMANet achieves an improvement of 0.24 dB in peak signal-to-noise ratio(PSNR)and 0.54 in structural similarity index(SSIM);at a magnification factor of 4,it achieves gains of 0.22 dB in PSNR and 0.51 in SSIM.These results confirm that the proposedmethod effectively enhances detail representation and structural reconstruction accuracy in complex remote sensing scenarios,offering robust technical support for high-precision detection and identification of both military and civilian aircraft.
基金supported by the Science and Technology on Reactor System Design Technology Laboratory(No.LRSDT12023108)supported in part by the Chongqing Postdoctoral Science Foundation(No.cstc2021jcyj-bsh0252)+2 种基金the National Natural Science Foundation of China(No.12005030)Sichuan Province to unveil the list of marshal industry common technology research projects(No.23jBGOV0001)Special Program for Stabilizing Support to Basic Research of National Basic Research Institutes(No.WDZC-2023-05-03-05).
文摘The neutron diffusion equation plays a pivotal role in nuclear reactor analysis.Nevertheless,employing the physics-informed neural network(PINN)method for its solution entails certain limitations.Conventional PINN approaches generally utilize a fully connected network(FCN)architecture that is susceptible to overfitting,training instability,and gradient vanishing as the network depth increases.These challenges result in accuracy bottlenecks in the solution.In response to these issues,the residual-based resample physics-informed neural network(R2-PINN)is proposed.It is an improved PINN architecture that replaces the FCN with a convolutional neural network with a shortcut(S-CNN).It incorporates skip connections to facilitate gradient propagation between network layers.Additionally,the incorporation of the residual adaptive resampling(RAR)mechanism dynamically increases the number of sampling points.This,in turn,enhances the spatial representation capabilities and overall predictive accuracy of the model.The experimental results illustrate that our approach significantly improves the convergence capability of the model and achieves high-precision predictions of the physical fields.Compared with conventional FCN-based PINN methods,R 2-PINN effectively overcomes the limitations inherent in current methods.Thus,it provides more accurate and robust solutions for neutron diffusion equations.
基金financially supported by the Czech Science Foundation in the frame of the project No.22-28283Sby the Technology Agency of the Czech Republic through the project No.CK03000060.
文摘Statistical distribution of residual fatigue life(RFL)of railway axles under given loading was computed using the Monte Carlo method by considering random variation of the selected input parameters.Experimental data for the EA4T railway axle steel,the loading spectrum,the press fit loading and the residual stress induced by surface hardening were considered in the crack propagation simulations.Usually,the material properties measured by tensile tests are considered to be the most informative source of material data.Under fatigue loading,however,the crack growth rates near the threshold are the most critical data.Two important influencing factors on these crack growth rates are presented:first,the air humidity and,second,the near-surface residual stress.The typical variation of these parameters in operation may change the RFL by one or two orders of magnitude.Experimentally obtained crack growth thresholds and residual stress profiles are highly affected by the used methodology.Therefore,the obtained input data may be located anywhere within a large scatter,while the experimenters are completely unaware of it.This can lead to dangerously non-conservative situations,e.g.when the thresholds are measured in a laboratory under humid air conditions and then applied to predictions of RFLs of axles operated in winter in low air humidity.This is significant for the topic of inspection interval optimisation.The results of experiments done on real 1:1 railway axles were close to the most frequent value found in the histogram of the numerically computed RFLs.
基金funded by the National Natural Science Foundation of China(No.52204407)the Natural Science Foundation of Jiangsu Province(No.BK20220595)the China Postdoctoral Science Foundation(No.2022M723689).
文摘This study proposes a multi-scale simplified residual convolutional neural network(MS-SRCNN)for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope(SEM)images.A multi-scale data structure is established by spatially aligning and stacking SEM images at different magnifications.The MS-SRCNN significantly reduces computational runtime by over 90%compared to traditional architectures like ResNet50,VGG16,and VGG19,without compromising prediction accuracy.The model demonstrates more excellent predictive performance,achieving a>5%increase in R^(2) compared to single-scale models.Furthermore,the MS-SRCNN exhibits robust composition prediction capability across other Mg-based binary alloys,including Mg-La,Mg-Sn,Mg-Ce,Mg-Sm,Mg-Ag,and Mg-Y,thereby emphasizing its generalization and extrapolation potential.This research establishes a non-destructive,microstructure-informed composition analysis framework,reduces characterization time compared to traditional experiment methods and provides insights into the composition-microstructure relationship in diverse material systems.
基金supported by the National Natural Science Foundations of China (Grant Nos.12235007,12001424,12271324,and 12501333)the Natural Science Basic research program of Shaanxi Province (Grant Nos.2021JZ-21 and 2024JC-YBQN-0069)+3 种基金the China Postdoctoral Science Foundation (Grant Nos.2020M673332 and 2024M751921)the Fundamental Research Funds for the Central Universities (Grant No.GK202304028)the 2023 Shaanxi Province Postdoctoral Research Project (Grant No.2023BSHEDZZ186)Xi’an University,Xi’an Science and Technology Plan Wutongshu Technology Transfer Action Innovation Team(Grant No.25WTZD07)。
文摘This letter introduces the novel concept of Painlevé solitons—waves arising from the interaction between Painlevé waves and solitons in integrable systems.Painlevé solitons can also be viewed as solitons propagating against a Painlevé wave background,in analogy to the established notion of elliptic solitons,which refers to solitons on an elliptic wave background.By employing a novel symmetry decomposition method aided by nonlocal residual symmetries,we explicitly construct (extended) Painlevé Ⅱ solitons for the Korteweg-de Vries equation and (extended) Painlevé Ⅳ solitons for the Boussinesq equation.
基金supported by the National Natural Science Foundation of China(62201602)。
文摘Despite the superior advantages of specific emitter identification in extracting emitter features from in-phase and quadrature(I/Q)signals,challenges persist due to signal-type confusion and background noise interference.To address those limitations,this paper proposes a multi-channel contrast prediction coding and complex-valued residuals network(MCPC-MCVResNet)framework.This model employs contrast prediction techniques to directly extract discriminative features from electromagnetic signal sequences,effectively capturing both amplitude and phase information within I/Q data.A core innovation of this approach is the sphere space softmax(SS-softmax)loss,which optimizes intra-class clustering density of while establishing well-defined boundaries between distinct emitters.The SS-softmax mechanism significantly enhances the model's capacity to discern subtle variations among radiation emitters.Experimental results demonstrate superior identification accuracy,rapid convergence,and exceptional robustness in low signal-to-noise ratio environments.
基金supported by the National Natural Science Foundation of China(Grant Nos.52379104 and 52090084).
文摘The biodegradable polybutylene succinate(PBS)material offers a sustainable solution for a circular economy to address the global issue of marine plastic waste.Its cross-linkage with non-biodegradable xanthan gum(XG)biopolymer to ameliorate residual granitic soil(RGS)in arid and semiarid regions can significantly mitigate soil erosion.This study investigates the enhancement of RGS by cross-linking the PBS and XG biopolymers.Employing a multitude of geotechnical tests(liquid limit,linear shrinkage,specific gravity,compaction,and UCS tests)at 3 d,28 d,and 90 d of steam-curing at a controlled temperature of 16℃,the outcomes were validated through scanning electron microscopy(SEM),thermogravimetric analysis(TGA),Fourier transform infrared spectroscopy(FTIR),and Brunauer-Emmett-Teller(BET)analyses.In addition,a comprehensive experimental database of 150 tests and nine parameters from the current study was utilized to model the UCS90-d(i.e.unconfined compressive strength after 90 d of curing)of the PBS-XG-treated RGS mixtures by deploying the random forest(RF)and eXtreme Gradient Boost(XGBoost)methods.The results found that the two biopolymers significantly improve the mechanical properties of RGS,with optimal UCS achieved at specific dosages(0.4PBS,1.5XG,and 0.2PBS+1.5XG dosage levels)and curing times.The UCS of PBS-XG-treated RGS showed up to a 57%increase after 90 d of curing.Furthermore,SEM and FTIR analyses revealed the formation of stronger microstructures and chemical bonds,respectively,whereas BET analysis indicated that pore volume and diameter are critical in affecting UCS.The proposed RF model outperformed XGBoost in predictive accuracy and generalization,demonstrating robustness and versatility.Moreover,SHAP values highlighted the significant impact of input parameters on UCS90-d,with curing time and specific material properties being key determinants.The study concludes with the proposal of a novel PyCharm intuitive graphical user interface as a"UCS Prediction App"for engineers and practitioners to forecast the UCS90-d of granitic residual soil.
文摘BACKGROUND At present,the influencing factors of social function in patients with residual depressive symptoms are still unclear.Residual depressive symptoms are highly harmful,leading to low mood in patients,affecting work and interpersonal communication,increasing the risk of recurrence,and adding to the burden on families.Studying the influencing factors of their social function is of great significance.AIM To explore the social function score and its influencing factors in patients with residual depressive symptoms.METHODS This observational study surveyed patients with residual depressive symptoms(case group)and healthy patients undergoing physical examinations(control group).Participants were admitted between January 2022 and December 2023.Social functioning was assessed using the Sheehan Disability Scale(SDS),and scores were compared between groups.Factors influencing SDS scores in patients with residual depressive symptoms were analyzed by applying multiple linear regression while using the receiver operating characteristic curve,and these RESULTS The SDS scores of the 158 patients with depressive symptoms were 11.48±3.26.Compared with the control group,the SDS scores and all items in the case group were higher.SDS scores were higher in patients with relapse,discon-tinuous medication,drug therapy alone,severe somatic symptoms,obvious residual symptoms,and anxiety scores≥8.Disease history,medication compliance,therapy method,and residual symptoms correlated positively with SDS scores(r=0.354,0.414,0.602,and 0.456,respectively).Independent influencing factors included disease history,medication compliance,therapy method,somatic symptoms,residual symptoms,and anxiety scores(P<0.05).The areas under the curve for predicting social functional impairment using these factors were 0.713,0.559,0.684,0.729,0.668,and 0.628,respectively,with sensitivities of 79.2%,61.8%,76.8%,81.7%,63.6%,and 65.5%and specificities of 83.3%,87.5%,82.6%,83.3%,86.7%,and 92.1%,respectively.CONCLUSION The social function scores of patients with residual symptoms of depression are high.They are affected by disease history,medication compliance,therapy method,degree of somatic symptoms,residual symptoms,and anxiety.
基金financially supported by the National Natural Science Foundation of China(Nos.52293395 and 52293393)the Xiongan Science and Technology Innovation Talent Project of MOST,China(No.2022XACX0500).
文摘Producing steel requires large amounts of energy to convert iron ores into steel,which often comes from fossil fuels,leading to carbon emissions and other pollutants.Increasing scrap usage emerges as one of the most effective strategies for addressing these issues.However,typical residual elements(Cu,As,Sn,Sb,Bi,etc.)inherited from scrap could significantly influence the mechanical properties of steel.In this work,we investigate the effects of residual elements on the microstructure evolution and mechanical properties of a quenching and partitioning(Q&P)steel by comparing a commercial QP1180 steel(referred to as QP)to the one containing typical residual elements(Cu+As+Sn+Sb+Bi<0.3wt%)(referred to as QP-R).The results demonstrate that in comparison with the QP steel,the residual elements significantly refine the prior austenite grain(9.7μm vs.14.6μm)due to their strong solute drag effect,leading to a higher volume fraction(13.0%vs.11.8%),a smaller size(473 nm vs.790 nm)and a higher average carbon content(1.26 wt%vs.0.99 wt%)of retained austenite in the QP-R steel.As a result,the QP-R steel exhibits a sustained transformation-induced plasticity(TRIP)effect,leading to an enhanced strain hardening effect and a simultaneous improvement of strength and ductility.Grain boundary segregation of residual elements was not observed at prior austenite grain boundaries in the QP-R steel,primarily due to continuous interface migration during austenitization.This study demonstrates that the residual elements with concentrations comparable to that in scrap result in significant microstructural refinement,causing retained austenite with relatively higher stability and thus offering promising mechanical properties and potential applications.
基金the National Natural Science:Foundation of China(52375370)the Open Project of Salt Lake Chemical Engineering Research Complex,Qinghai University(2023-DXSSKF-Z02)+2 种基金the Nat-ural Science Foundation of Shanxi(202103021224049)GDAS Projects of International cooperation platform of Sci-ence and Technology(2022GDASZH-2022010203-003)Guangdong province Science and Technology Plan Projects(2023B1212060045).
文摘The dominated contradiction in optimizing the performance of magnesium-air battery anode lies in the difficulty of achieving a good balance between activation and passivation during discharge process.To further reconcile this contradiction,two Mg-0.1Sc-0.1Y-0.1Ag anodes with different residual strain distribution through extrusion with/without annealing are fabricated.The results indicate that annealing can significantly lessen the“pseudo-anode”regions,thereby changing the dissolution mode of the matrix and achieving an effective dissolution during discharge.Additionally,p-type semiconductor characteristic of discharge productfilm could suppress the self-corrosion reaction without reducing the polarization of anode.The magnesium-air battery utilizing annealed Mg-0.1Sc-0.1Y-0.1Ag as anode achieves a synergistic improvement in specific capacity(1388.89 mA h g^(-1))and energy density(1960.42 mW h g^(-1)).This anode modification method accelerates the advancement of high efficiency and long lifespan magnesium-air batteries,offering renewable and cost-effective energy solutions for electronics and emergency equipment.
基金supported by the National Key Re-search&Development Plan of China(No.2020YFA0405900)the Major Research Plan of the National Natural Science Foundation of China(No.92263201)Y.P.Xia would like to thank the support by the Jiangsu Funding Program for Excellent Postdoctoral Talent.All authors thank the Advanced Material Research Institute of Jiangsu Industrial Technology Research Institute(JITRI,Suzhou,China)for the experimental support.
文摘Laser-welded Ti-6Al-4 V is prone to severe residual stresses,microstructural variation,and structural de-fects which are known detrimental to the mechanical properties of weld joints.Residual stress removal is typically applied to weld joints for engineering purposes via heat treatment,in order to avoid prema-ture failure and performance degradation.In the present work,we found that proper welding residual stresses in laser-welded Ti-6Al-4 V sheets can maintain better ductility during uniaxial tension,as op-posed to the stress-relieved counterparts.A detailed experimental investigation has been performed on the deformation behaviours of Ti-6Al-4 V butt welds,including residual stress distribution characteriza-tions by focused ion beam ring-coring coupled with digital image correlation(FIB-DIC),X-ray comput-erized tomography(CT)for internal voids,and in-situ DIC analysis of the subregional strain evolutions.It was found that the pores preferentially distributed near the fusion zone(FZ)boundary,where the compressive residual stress was up to-330 MPa.The removal of residual stress resulted in a changed failure initiation site from the base material to the FZ boundary,the former with ductile and the latter with brittle fracture characteristics under tensile deformation.The combined effects of residual stresses,microstructures,and internal pores on the mechanical responses are discussed in detail.This work high-lights the importance of inevitable residual stress and pores in laser weld pieces,leading to key insights for post-welding treatment and service performance evaluations.
基金supported by the National Research Foundation of Singapore,Rolls-Royce Singapore Pte.Ltd.,and Nanyang Technological University through grants#002123-00009 and #002124-00009.
文摘This study investigates the effect of high current density electropulsing on the material in a rapid stress relaxation process.An AISI 1020 steel was shot-peened to induce surface compressive residual stresses in a controlled manner and subsequently electropulsed to investigate the changes in microstructure and defect configuration.AISI 1020 steel was chosen as it has a simple microstructure(plain ferritic)and composition with low alloying conditions.It is an appropriate material to study the effect of trans-mitting electric pulses on the microstructural defect evolution.A combination of electron-backscattered diffraction and transmission electron microscopy proved to be an effective tool in characterizing the post-electropulsing effects critically.By application of electropulsing,a reduction in the surface residual stress layer was noticed.Also,reductions in misorientation and dislocation density together with the disentan-glement of dislocations within the cold-worked layer were observed after electropulsing.Additionally,the annihilation of shot-peening-induced deformation bands beyond the residual layer depth was observed.These effects have been rationalised by taking into account the various possibilities of athermal effects of electropulsing.
基金supported in part by the National Natural Science Foundation of China(Nos.61201048,61107063)the National Science and Technology Major Project(No.2017-VI-001-0094).
文摘To investigate the residual stress distribution and its influence on machining deformation in 6061-T651 aluminum alloy plates,this paper uses the crack compliance method to study the residual stress characteristics of 6061-T651 aluminum alloy plates with a thickness of 75 mm produced by two domestic manufacturers in China.The results indicate that both types of plates exhibit highly consistent and symmetrical M-shaped residual stress profile along the thickness direction,manifested as surface layer compression and core tension.The strain energy density across all specimens ranges from 1.27 kJ/m^(3)to 1.43 kJ/m^(3).Machining deformation simulations of an aerospace component incorporating these measured stresses showed minimal final deformation difference between the material sources,with a maximum deviation of only 0.009 mm across specimens.These findings provide critical data for material selection and deformation control in aerospace manufacturing.
