Role of vacancy-type（N vacancy（VN） and Ga vacancy（VGa）） defects in magnetism of GaMnN is investigated by first-principle calculation.Theoretical results show that both the VNand VGainfluence the ferromagnetic st...Role of vacancy-type（N vacancy（VN） and Ga vacancy（VGa）） defects in magnetism of GaMnN is investigated by first-principle calculation.Theoretical results show that both the VNand VGainfluence the ferromagnetic state of a system.The VNcan induce antiferromagnetic state and the VGaindirectly modify the stability of the ferromagnetic state by depopulating the Mn levels in GaMnN.The transfer of electrons between the vacancy defects and Mn ions results in converting Mn3＋（d4） into Mn2＋（d5）.The introduced VNand the ferromagnetism become stronger and then gradually weaker with Mn concentration increasing,as well as the coexistence of Mn3＋（d4） and Mn2＋（d5） are found in GaMnN films grown by metal–organic chemical vapor deposition.The analysis suggests that a big proportion of Mn3＋changing into Mn2＋will reduce the exchange interaction and magnetic correlation of Mn atoms and lead to the reduction of ferromagnetism of material.展开更多
The positron annihilation lifetimes and the Doppler broadening by slow positron beam are measured in thin Fe films with thickness 500 nm, a thin Hf film with thickness 100 nm, and the bilayer Fe (50 nm)/Hf (50 nm) on ...The positron annihilation lifetimes and the Doppler broadening by slow positron beam are measured in thin Fe films with thickness 500 nm, a thin Hf film with thickness 100 nm, and the bilayer Fe (50 nm)/Hf (50 nm) on quartz glass substrate. We have analyzed the behavior in vacancy-type defects in each layer through some deposition temperatures and annealing. It is observed that the thin Fe film, the thin Hf film, and the bilayer Fe (50 nm)/Hf (50 nm) already contain many vacancy-type defects. We have investigated the change of densities of the vacancy-carbon complex and the small vacancy-cluster with carbons, through solid-state amorphization of Fe (50 nm)/Hf (50 nm) bilayer.展开更多
The structural materials applied in future advanced nuclear energy systems will be exposed to high-energy and high-fux neutron irradiation,which puts forward high requirements for the neutron irradiation resistance of...The structural materials applied in future advanced nuclear energy systems will be exposed to high-energy and high-fux neutron irradiation,which puts forward high requirements for the neutron irradiation resistance of materials[1,2].展开更多
By thermal neutron irradiation particle tracking autoradiography(PTA)technique,the development of boron segregation at grain boundaries in ultra-low carbon micro-alloy steels was investigated during cooling from 1150&...By thermal neutron irradiation particle tracking autoradiography(PTA)technique,the development of boron segregation at grain boundaries in ultra-low carbon micro-alloy steels was investigated during cooling from 1150°C to 850°C,and the effect of Cu on boron segregation at grain boundaries was discussed.By positron annihilation lifetime(PAL)technique,the changes of vacancy-type defects with temperatures and the effect of Cu on vacancy-type defects in the cooling process were discussed.Results show that,the concentration of boron at grain boundaries increases rapidly at the beginning of the cooling;after that,it begins to decrease;and then,it increases gradually again.The addition of Cu not only increases the concentration of boron at grain boundaries but also speeds up the development process of boron segregation at grain boundaries.During the continuous cooling process,the addition of Cu significantly affects the change of vacancy-type defects with temperatures in ultra-low carbon micro-alloy steels.展开更多
Background:Rats are often used to prepare skin defect models.However,the skin defect sizes of the models prepared by researchers are different,and the lack of consensus on the critical-size defect makes it difficult t...Background:Rats are often used to prepare skin defect models.However,the skin defect sizes of the models prepared by researchers are different,and the lack of consensus on the critical-size defect makes it difficult to compare their research results.Methods:The time for wound closure was evaluated and recorded through gross observation.The regression equation between the healing time and the diameter of skin defect was established,which can be used to predict the healing time for a certain skin defect size in rats.Histochemical and immunohistochemical staining was used to observe the regeneration and reconstruction of skin appendages,and the functional skin repair was quantitatively scored.Results:The critical-size defect of rats was determined based on the maximum capacity of structural skin repair,and the functional skin repair was quantitatively scored based on the regeneration and reconstruction of skin appendages.The allowable range of critical-size skin defect of SD rats lies between 45 and 50 mm in diameter.The concept of structural repair and the category of functional repair of injured skin are put forward.The regression equation between the structural skin healing time and defect diameters is established.Conclusion:The allowable range of skin critical-size defect of SD rats lies between 45 and 50 mm in diameter.The regression equation between the structural skin healing time and defect diameters can be used to predict the healing time for a certain skin defect size in rats.展开更多
Manual inspection of onba earing casting defects is not realistic and unreliable,particularly in the case of some micro-level anomalies which lead to major defects on a large scale.To address these challenges,we propo...Manual inspection of onba earing casting defects is not realistic and unreliable,particularly in the case of some micro-level anomalies which lead to major defects on a large scale.To address these challenges,we propose BearFusionNet,an attention-based deep learning architecture with multi-stream,which merges both DenseNet201 and MobileNetV2 for feature extraction with a classification head inspired by VGG19.This hybrid design,figuratively beaming from one layer to another,extracts the enormity of representations on different scales,backed by a prepreprocessing pipeline that brings defect saliency to the fore through contrast adjustment,denoising,and edge detection.The use of multi-head self-attention enhances feature fusion,enabling the model to capture both large and small spatial features.BearFusionNet achieves an accuracy of 99.66%and Cohen’s kappa score of 0.9929 in Kaggle’s Real-life Industrial Casting Defects dataset.Both McNemar’s and Wilcoxon signed-rank statistical tests,as well as fivefold cross-validation,are employed to assess the robustness of our proposed model.To interpret the model,we adopt Grad-Cam visualizations,which are the state of the art standard.Furthermore,we deploy BearFusionNet as a webbased system for near real-time inference(5-6 s per prediction),which enables the quickest yet accurate detection with visual explanations.Overall,BearFusionNet is an interpretable,accurate,and deployable solution that can automatically detect casting defects,leading to significant advances in the innovative industrial environment.展开更多
The original online version of this article was revised:The layout update for Article 758 has impacted the page range in the published issue,but did not affect the scholarly content.To ensure consistency with the orig...The original online version of this article was revised:The layout update for Article 758 has impacted the page range in the published issue,but did not affect the scholarly content.To ensure consistency with the originally assigned pages(2595-2614),we will need to publish an erratum to correct the article and restore the original page range.The original article has been corrected.展开更多
The lack of macro-continuity and mechanical strength of covalent organic frameworks(COFs)has significantly limited their practical applications.Here,we propose an“alcohol-triggered defect cleavage”strategy to precis...The lack of macro-continuity and mechanical strength of covalent organic frameworks(COFs)has significantly limited their practical applications.Here,we propose an“alcohol-triggered defect cleavage”strategy to precisely regulate the growth and stacking of COF grains through a moderate reversed Schiff base reaction,realizing the direct synthesis of COF nanofibers(CNFs)with high aspect ratio(L/D=103.05)and long length(>20μm).An individual CNF exhibits a biomimetic scale-like architecture,achieving superior flexibility and fatigue resistance under dynamic bending via a multiscale stress dissipation mechanism.Taking advantages of these structural features,we engineer CNF aerogels(CNF-As)with programmable porous structures(e.g.,honeycomb,lamellar,isotropic)via directional ice-template methodology.CNF-As demonstrate 100%COF content,high specific surface area(396.15 m^(2)g^(-1))and superelasticity(~0%elastic deformation after 500 compression cycles at 50%strain),outperforming most COF-based counterparts.Compared with the conventional COF aerogels,the unique structural features of CNF-A enable it to perform outstandingly in uranium extraction,with an 11.72-fold increment in adsorption capacity(920.12 mg g^(-1))and adsorption rate(89.9%),and a 2.48-fold improvement in selectivity(U/V=2.31).This study provides a direct strategy for the development of next-generation COF materials with outstanding functionality and structural robustness.展开更多
The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermo...The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.展开更多
In industrial manufacturing,efficient surface defect detection is crucial for ensuring product quality and production safety.Traditional inspectionmethods are often slow,subjective,and prone to errors,while classicalm...In industrial manufacturing,efficient surface defect detection is crucial for ensuring product quality and production safety.Traditional inspectionmethods are often slow,subjective,and prone to errors,while classicalmachine vision techniques strugglewith complex backgrounds and small defects.To address these challenges,this study proposes an improved YOLOv11 model for detecting defects on hot-rolled steel strips using the NEU-DET dataset.Three key improvements are introduced in the proposed model.First,a lightweight Guided Attention Feature Module(GAFM)is incorporated to enhance multi-scale feature fusion,allowing the model to better capture and integrate semantic and spatial information across different layers,which improves its ability to detect defects of varying sizes.Second,an Aggregated Attention(AA)mechanism is employed to strengthen the representation of critical defect features while effectively suppressing irrelevant background information,particularly enhancing the detection of small,low-contrast,or complex defects.Third,Ghost Dynamic Convolution(GDC)is applied to reduce computational cost by generating low-cost ghost features and dynamically reweighting convolutional kernels,enabling faster inference without sacrificing feature quality or detection accuracy.Extensive experiments demonstrate that the proposed model achieves a mean Average Precision(mAP)of 87.2%,compared to 81.5%for the baseline,while lowering computational cost from6.3Giga Floating-point Operations Per Second(GFLOPs)to 5.1 GFLOPs.These results indicate that the improved YOLOv11 is both accurate and computationally efficient,making it suitable for real-time industrial surface defect detection and contributing to the development of practical,high-performance inspection systems.展开更多
To solve the false detection and missed detection problems caused by various types and sizes of defects in the detection of steel surface defects,similar defects and background features,and similarities between differ...To solve the false detection and missed detection problems caused by various types and sizes of defects in the detection of steel surface defects,similar defects and background features,and similarities between different defects,this paper proposes a lightweight detection model named multiscale edge and squeeze-and-excitation attention detection network(MSESE),which is built upon the You Only Look Once version 11 nano(YOLOv11n).To address the difficulty of locating defect edges,we first propose an edge enhancement module(EEM),apply it to the process of multiscale feature extraction,and then propose a multiscale edge enhancement module(MSEEM).By obtaining defect features from different scales and enhancing their edge contours,the module uses the dual-domain selection mechanism to effectively focus on the important areas in the image to ensure that the feature images have richer information and clearer contour features.By fusing the squeeze-and-excitation attention mechanism with the EEM,we obtain a lighter module that can enhance the representation of edge features,which is named the edge enhancement module with squeeze-and-excitation attention(EEMSE).This module was subsequently integrated into the detection head.The enhanced detection head achieves improved edge feature enhancement with reduced computational overhead,while effectively adjusting channel-wise importance and further refining feature representation.Experiments on the NEU-DET dataset show that,compared with the original YOLOv11n,the improved model achieves improvements of 4.1%and 2.2%in terms of mAP@0.5 and mAP@0.5:0.95,respectively,and the GFLOPs value decreases from the original value of 6.4 to 6.2.Furthermore,when compared to current mainstream models,Mamba-YOLOT and RTDETR-R34,our method achieves superior performance with 6.5%and 8.9%higher mAP@0.5,respectively,while maintaining a more compact parameter footprint.These results collectively validate the effectiveness and efficiency of our proposed approach.展开更多
With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-...With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-consuming and labor-intensive,but they also struggle to provide consistent,high-precision detection and realtime monitoring of pavement surface defects.To overcome these limitations,we propose an Automatic Recognition of PavementDefect(ARPD)algorithm,which leverages unmanned aerial vehicle(UAV)-based aerial imagery to automate the inspection process.The ARPD framework incorporates a backbone network based on the Selective State Space Model(S3M),which is designed to capture long-range temporal dependencies.This enables effective modeling of dynamic correlations among redundant and often repetitive structures commonly found in road imagery.Furthermore,a neck structure based on Semantics and Detail Infusion(SDI)is introduced to guide cross-scale feature fusion.The SDI module enhances the integration of low-level spatial details with high-level semantic cues,thereby improving feature expressiveness and defect localization accuracy.Experimental evaluations demonstrate that theARPDalgorithm achieves a mean average precision(mAP)of 86.1%on a custom-labeled pavement defect dataset,outperforming the state-of-the-art YOLOv11 segmentation model.The algorithm also maintains strong generalization ability on public datasets.These results confirm that ARPD is well-suited for diverse real-world applications in intelligent,large-scale highway defect monitoring and maintenance planning.展开更多
Vacancy defects,as fundamental disruptions in metallic lattices,play an important role in shaping the mechanical and electronic properties of aluminum crystals.However,the influence of vacancy position under coupled t...Vacancy defects,as fundamental disruptions in metallic lattices,play an important role in shaping the mechanical and electronic properties of aluminum crystals.However,the influence of vacancy position under coupled thermomechanical fields remains insufficiently understood.In this study,transmission and scanning electron microscopy were employed to observe dislocation structures and grain boundary heterogeneities in processed aluminum alloys,suggesting stress concentrations and microstructural inhomogeneities associated with vacancy accumulation.To complement these observations,first-principles calculations and molecular dynamics simulations were conducted for seven single-vacancy configurations in face-centered cubic aluminum.The stress response,total energy,density of states(DOS),and differential charge density were examined under varying compressive strain(ε=0–0.1)and temperature(0–600 K).The results indicate that face-centered vacancies tend to reduce mechanical strength and perturb electronic states near the Fermi level,whereas corner and edge vacancies appear to have weaker effects.Elevated temperatures may partially restore electronic uniformity through thermal excitation.Overall,these findings suggest that vacancy position exerts a critical but position-dependent influence on coupled structure-property relationships,offering theoretical insights and preliminary experimental support for defect-engineered aluminum alloy design.展开更多
To address the challenges of high-precision optical surface defect detection,we propose a novel design for a wide-field and broadband light field camera in this work.The proposed system can achieve a 50°field of ...To address the challenges of high-precision optical surface defect detection,we propose a novel design for a wide-field and broadband light field camera in this work.The proposed system can achieve a 50°field of view and operates at both visible and near-infrared wavelengths.Using the principles of light field imaging,the proposed design enables 3D reconstruction of optical surfaces,thus enabling vertical surface height measurements with enhanced accuracy.Using Zemax-based simulations,we evaluate the system’s modulation transfer function,its optical aberrations,and its tolerance to shape variations through Zernike coefficient adjustments.The results demonstrate that this camera can achieve the required spatial resolution while also maintaining high imaging quality and thus offers a promising solution for advanced optical surface defect inspection.展开更多
Conversion-type electrode materials hold significant promise for potassium-ion batteries(PIBs)due to their high theoretical capacities,yet their practical deployment is hindered by sluggish kinetics and irreversible s...Conversion-type electrode materials hold significant promise for potassium-ion batteries(PIBs)due to their high theoretical capacities,yet their practical deployment is hindered by sluggish kinetics and irreversible structural degradation.To overcome these limitations,we propose a rationally engineered nanoreactor architecture that stabilizes defect-rich MoS_(2)via interlayer incorporation of a carbon monolayer,followed by encapsulation within a nitrogen-doped carbon shell,forming a MoSSe@NC heterostructure.This tailored structure synergistically accelerates both K^(+)diffusion kinetics and electron transfer,enabling unprecedented rate performance(107 mAh g^(-1)at 10 Ag^(-1))and ultralong cyclability(86.5%capacity retention after 1200 cycles at 3 A g^(-1)).Mechanistic insights reveal a distinctive“adsorption-conversion”pathway,where sulfur vacancies on exposed S-Mo-S basal planes act as preferential K^(+)adsorption sites,effectively suppressing parasitic phase transitions during intercalation.In situ X-ray diffraction and transmission electron microscopy corroborate the structural reversibility of the conversion reaction,with the carbon matrix dynamically accommodating strain while preserving electrode integrity.This work not only advances the understanding of defect-driven interfacial chemistry in conversion-type materials but also provides a versatile strategy for designing high-performance anodes in next-generation PIBs through heterostructure engineering.展开更多
Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To ...Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To address these issues,this paper proposes SIM-Net,an enhanced detection framework derived from YOLOv11.The model integrates SPDConv to preserve fine-grained features for small object detection,introduces a novel convolutional partial attention module(C2PAM)to suppress redundant background information and highlight salient regions,and employs a multi-scale fusion network(MFN)with a multi-grain contextual module(MGCT)to strengthen contextual representation and accelerate inference.Experimental evaluations demonstrate that SIM-Net achieves 92.4%mAP,92%accuracy,and 89.4%recall with an inference speed of 75.1 FPS,outperforming existing state-of-the-art methods.These results confirm the robustness and real-time applicability of SIM-Net for PCB defect inspection.展开更多
In printed circuit board(PCB)manufacturing,surface defects can significantly affect product quality.To address the performance degradation,high false detection rates,and missed detections caused by complex backgrounds...In printed circuit board(PCB)manufacturing,surface defects can significantly affect product quality.To address the performance degradation,high false detection rates,and missed detections caused by complex backgrounds in current intelligent inspection algorithms,this paper proposes CG-YOLOv8,a lightweight and improved model based on YOLOv8n for PCB surface defect detection.The proposed method optimizes the network architecture and compresses parameters to reduce model complexity while maintaining high detection accuracy,thereby enhancing the capability of identifying diverse defects under complex conditions.Specifically,a cascaded multi-receptive field(CMRF)module is adopted to replace the SPPF module in the backbone to improve feature perception,and an inverted residual mobile block(IRMB)is integrated into the C2f module to further enhance performance.Additionally,conventional convolution layers are replaced with GSConv to reduce computational cost,and a lightweight Convolutional Block Attention Module based Convolution(CBAMConv)module is introduced after Grouped Spatial Convolution(GSConv)to preserve accuracy through attention mechanisms.The detection head is also optimized by removing medium and large-scale detection layers,thereby enhancing the model’s ability to detect small-scale defects and further reducing complexity.Experimental results show that,compared to the original YOLOv8n,the proposed CG-YOLOv8 reduces parameter count by 53.9%,improves mAP@0.5 by 2.2%,and increases precision and recall by 2.0%and 1.8%,respectively.These improvements demonstrate that CG-YOLOv8 offers an efficient and lightweight solution for PCB surface defect detection.展开更多
In ultrasonic non-destructive testing of high-temperature industrial equipment,sound velocity drift induced by non-uniform temperature fields can severely compromise defect localization accuracy.Conventional approache...In ultrasonic non-destructive testing of high-temperature industrial equipment,sound velocity drift induced by non-uniform temperature fields can severely compromise defect localization accuracy.Conventional approaches that rely on room-temperature sound velocities introduce systematic errors,potentially leading to misjudgment of safety-critical components.Two primary challenges hinder current methods:first,it is difficult to monitor real-time changes in sound velocity distribution within a thermal gradient;second,traditional uniform-temperature correction models fail to capture the nonlinear dependence of material properties on temperature and their effect on ultrasonic velocity fields.Here,we propose a defect localization correction method based on multiphysics coupling.A two-dimensional coupled heat transfer–wave propagation model is established in COMSOL,and a one-dimensional steady-state heat transfer condition is used to design a numerical pulse–echo experiment in 1020 steel.Temperature-dependent material properties are incorporated,and the intrinsic relationship between sound velocity and temperature is derived,confirming consistency with classical theories.To account for gradient temperature fields,a micro-element integration algorithm discretizes the propagation path into segments,each associated with a locally computed temperature from the steady-state heat conduction solution.Defect positions are dynamically corrected through cumulative displacement along the propagation path.By integrating heat conduction and elastic wave propagation in a multiphysics framework,this method overcomes the limitations of uniform-temperature assumptions.The micro-element integration approach enables dynamic tracking of spatially varying sound velocities,offering a robust strategy to enhance ultrasonic testing accuracy in high-temperature industrial environments.展开更多
Treating bone defects complicated by bacterial infections remains a significant clinical challenge.Drawing inspiration from the human body's bone repair mechanisms,the use of biomimetic methods to design tissue en...Treating bone defects complicated by bacterial infections remains a significant clinical challenge.Drawing inspiration from the human body's bone repair mechanisms,the use of biomimetic methods to design tissue engineering scaffolds is of great significance for bone repair.This study synthesized copper(Cu)-doped mesoporous silica nanoparticles(Cu@MSN)modified with hydroxyethyl methacrylate to obtain methacrylated Cu@MSN(Cu@MSNMA).Furtheremore,bio-mimetic nanocomposite hydrogels were prepared by adding Cu@MSNMA to a GelMA/gelatin solution.This hydrogel achieves multi-modal bone tissue biomimicry:(ⅰ)GelMA/gelatin mimics the matrix components in bone ECM,ensuring biocompatibility while promoting cellular behavior(such as adhesion,proliferation,and differentiation);(ⅱ)GelMA/gela-tin and the crosslinking sites introduced by Cu@MSNMA form a stable porous network structure,achieving structural and mechanical biomimicry to provide necessary support for bone defects;(ⅲ)The elemental biomimicry of Si and Cu in Cu@MSNMA achieves efficient osteogenic induction.The effect of different proportions of Cu@MSNMA on the physi-cal properties of the composite hydrogels was investigated to determine the optimal proportion.The results indicated that the mechanical properties of hydrogel were enhanced with the increasing Cu@MSNMA mass ratio.Notably,5%NPs/GelMA/gelatin hydrogel exhibited excellent mechanical property compared to the GelMA/gelatin hydrogel.In vitro and vivo cellular experiments demonstrated a significant enhancement in antibacterial and osteogenic induction with Cu@MSNMA addition.In conclusion,the proposed nanocomposite hydrogel with biomimetic components and ion-regulating properties can serve as a multifunctional scaffold,offering antimicrobial properties for infected bone regeneration,and guide for future research in bone regeneration and three-dimensional printing.展开更多
In order to investigate the effect of die wall thickness on morphologies of defect band,a stepped mold with a wall thickness of 5 mm,4 mm,3 mm,2 mm,and 1 mm was designed to carry out high pressure die casting experime...In order to investigate the effect of die wall thickness on morphologies of defect band,a stepped mold with a wall thickness of 5 mm,4 mm,3 mm,2 mm,and 1 mm was designed to carry out high pressure die casting experiments with AlSi10 MgMn alloy.For castings with wall thickness of 2-4 mm,the ratio of the mean defect band width(w)and mean grain size(d)in the defect band(w/d)ranges 7-18,while it increases to 24.47 for the 5 mm-thick casting.This difference is related with the filling speed and the distribution of externally solidified crystals(ESCs).The mold flow analysis indicates that the filling speed decreases from 25.41 m·s^(-1)to 11.07 m·s^(-1)when wall thickness increases from 2 mm to 5 mm.Due to the decreasing filling speed along the wall thickness,ESCs gradually diffuse from the center to the defect band,which keep the shear strength in the defect band at a high-level during filling.Meanwhile,the shear strength generated during the filling also decreases as the shear rate drops.Finally,the defect bands in the 5 mm-thick region become widen and indistinct,and the porosity is as high as 5.25%.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61204008,11075176,and 11505211)the National Key Basic Research Special Foundation of China(Grant No.2013CB328705)
文摘Role of vacancy-type（N vacancy（VN） and Ga vacancy（VGa）） defects in magnetism of GaMnN is investigated by first-principle calculation.Theoretical results show that both the VNand VGainfluence the ferromagnetic state of a system.The VNcan induce antiferromagnetic state and the VGaindirectly modify the stability of the ferromagnetic state by depopulating the Mn levels in GaMnN.The transfer of electrons between the vacancy defects and Mn ions results in converting Mn3＋（d4） into Mn2＋（d5）.The introduced VNand the ferromagnetism become stronger and then gradually weaker with Mn concentration increasing,as well as the coexistence of Mn3＋（d4） and Mn2＋（d5） are found in GaMnN films grown by metal–organic chemical vapor deposition.The analysis suggests that a big proportion of Mn3＋changing into Mn2＋will reduce the exchange interaction and magnetic correlation of Mn atoms and lead to the reduction of ferromagnetism of material.
文摘The positron annihilation lifetimes and the Doppler broadening by slow positron beam are measured in thin Fe films with thickness 500 nm, a thin Hf film with thickness 100 nm, and the bilayer Fe (50 nm)/Hf (50 nm) on quartz glass substrate. We have analyzed the behavior in vacancy-type defects in each layer through some deposition temperatures and annealing. It is observed that the thin Fe film, the thin Hf film, and the bilayer Fe (50 nm)/Hf (50 nm) already contain many vacancy-type defects. We have investigated the change of densities of the vacancy-carbon complex and the small vacancy-cluster with carbons, through solid-state amorphization of Fe (50 nm)/Hf (50 nm) bilayer.
文摘The structural materials applied in future advanced nuclear energy systems will be exposed to high-energy and high-fux neutron irradiation,which puts forward high requirements for the neutron irradiation resistance of materials[1,2].
基金supported by the National Natural Science Foundation of China(Grant No.51276016)the National Basic Research Program of China("973" Project)(Grant No.2012CB720406)
文摘By thermal neutron irradiation particle tracking autoradiography(PTA)technique,the development of boron segregation at grain boundaries in ultra-low carbon micro-alloy steels was investigated during cooling from 1150°C to 850°C,and the effect of Cu on boron segregation at grain boundaries was discussed.By positron annihilation lifetime(PAL)technique,the changes of vacancy-type defects with temperatures and the effect of Cu on vacancy-type defects in the cooling process were discussed.Results show that,the concentration of boron at grain boundaries increases rapidly at the beginning of the cooling;after that,it begins to decrease;and then,it increases gradually again.The addition of Cu not only increases the concentration of boron at grain boundaries but also speeds up the development process of boron segregation at grain boundaries.During the continuous cooling process,the addition of Cu significantly affects the change of vacancy-type defects with temperatures in ultra-low carbon micro-alloy steels.
基金National Key Research and Development Program of China,Grant/Award Number:2023YFC2410403。
文摘Background:Rats are often used to prepare skin defect models.However,the skin defect sizes of the models prepared by researchers are different,and the lack of consensus on the critical-size defect makes it difficult to compare their research results.Methods:The time for wound closure was evaluated and recorded through gross observation.The regression equation between the healing time and the diameter of skin defect was established,which can be used to predict the healing time for a certain skin defect size in rats.Histochemical and immunohistochemical staining was used to observe the regeneration and reconstruction of skin appendages,and the functional skin repair was quantitatively scored.Results:The critical-size defect of rats was determined based on the maximum capacity of structural skin repair,and the functional skin repair was quantitatively scored based on the regeneration and reconstruction of skin appendages.The allowable range of critical-size skin defect of SD rats lies between 45 and 50 mm in diameter.The concept of structural repair and the category of functional repair of injured skin are put forward.The regression equation between the structural skin healing time and defect diameters is established.Conclusion:The allowable range of skin critical-size defect of SD rats lies between 45 and 50 mm in diameter.The regression equation between the structural skin healing time and defect diameters can be used to predict the healing time for a certain skin defect size in rats.
基金funded by Multimedia University,Cyberjaya,Selangor,Malaysia(Grant Number:PostDoc(MMUI/240029)).
文摘Manual inspection of onba earing casting defects is not realistic and unreliable,particularly in the case of some micro-level anomalies which lead to major defects on a large scale.To address these challenges,we propose BearFusionNet,an attention-based deep learning architecture with multi-stream,which merges both DenseNet201 and MobileNetV2 for feature extraction with a classification head inspired by VGG19.This hybrid design,figuratively beaming from one layer to another,extracts the enormity of representations on different scales,backed by a prepreprocessing pipeline that brings defect saliency to the fore through contrast adjustment,denoising,and edge detection.The use of multi-head self-attention enhances feature fusion,enabling the model to capture both large and small spatial features.BearFusionNet achieves an accuracy of 99.66%and Cohen’s kappa score of 0.9929 in Kaggle’s Real-life Industrial Casting Defects dataset.Both McNemar’s and Wilcoxon signed-rank statistical tests,as well as fivefold cross-validation,are employed to assess the robustness of our proposed model.To interpret the model,we adopt Grad-Cam visualizations,which are the state of the art standard.Furthermore,we deploy BearFusionNet as a webbased system for near real-time inference(5-6 s per prediction),which enables the quickest yet accurate detection with visual explanations.Overall,BearFusionNet is an interpretable,accurate,and deployable solution that can automatically detect casting defects,leading to significant advances in the innovative industrial environment.
文摘The original online version of this article was revised:The layout update for Article 758 has impacted the page range in the published issue,but did not affect the scholarly content.To ensure consistency with the originally assigned pages(2595-2614),we will need to publish an erratum to correct the article and restore the original page range.The original article has been corrected.
基金supported by the National Natural Science Foundation of China(No.52403035)the Shanghai Sailing Program(23YF1400300)+1 种基金the Fundamental Research Funds for the Central Universities(2232023D-05)the Weiqiao Teaching and Research Innovation Program.
文摘The lack of macro-continuity and mechanical strength of covalent organic frameworks(COFs)has significantly limited their practical applications.Here,we propose an“alcohol-triggered defect cleavage”strategy to precisely regulate the growth and stacking of COF grains through a moderate reversed Schiff base reaction,realizing the direct synthesis of COF nanofibers(CNFs)with high aspect ratio(L/D=103.05)and long length(>20μm).An individual CNF exhibits a biomimetic scale-like architecture,achieving superior flexibility and fatigue resistance under dynamic bending via a multiscale stress dissipation mechanism.Taking advantages of these structural features,we engineer CNF aerogels(CNF-As)with programmable porous structures(e.g.,honeycomb,lamellar,isotropic)via directional ice-template methodology.CNF-As demonstrate 100%COF content,high specific surface area(396.15 m^(2)g^(-1))and superelasticity(~0%elastic deformation after 500 compression cycles at 50%strain),outperforming most COF-based counterparts.Compared with the conventional COF aerogels,the unique structural features of CNF-A enable it to perform outstandingly in uranium extraction,with an 11.72-fold increment in adsorption capacity(920.12 mg g^(-1))and adsorption rate(89.9%),and a 2.48-fold improvement in selectivity(U/V=2.31).This study provides a direct strategy for the development of next-generation COF materials with outstanding functionality and structural robustness.
基金supported by the State Grid Southwest Branch Project“Research on Defect Diagnosis and Early Warning Technology of Relay Protection and Safety Automation Devices Based on Multi-Source Heterogeneous Defect Data”.
文摘The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.
基金supported in part by the National Natural Science Foundation of China(Grant No.62071123)in part by the Natural Science Foundation of Fujian Province(Grant Nos.2024J01971,2022J05202)in part by the Young and Middle-Aged Teacher Education Research Project of Fujian Province(Grant No.JAT210370).
文摘In industrial manufacturing,efficient surface defect detection is crucial for ensuring product quality and production safety.Traditional inspectionmethods are often slow,subjective,and prone to errors,while classicalmachine vision techniques strugglewith complex backgrounds and small defects.To address these challenges,this study proposes an improved YOLOv11 model for detecting defects on hot-rolled steel strips using the NEU-DET dataset.Three key improvements are introduced in the proposed model.First,a lightweight Guided Attention Feature Module(GAFM)is incorporated to enhance multi-scale feature fusion,allowing the model to better capture and integrate semantic and spatial information across different layers,which improves its ability to detect defects of varying sizes.Second,an Aggregated Attention(AA)mechanism is employed to strengthen the representation of critical defect features while effectively suppressing irrelevant background information,particularly enhancing the detection of small,low-contrast,or complex defects.Third,Ghost Dynamic Convolution(GDC)is applied to reduce computational cost by generating low-cost ghost features and dynamically reweighting convolutional kernels,enabling faster inference without sacrificing feature quality or detection accuracy.Extensive experiments demonstrate that the proposed model achieves a mean Average Precision(mAP)of 87.2%,compared to 81.5%for the baseline,while lowering computational cost from6.3Giga Floating-point Operations Per Second(GFLOPs)to 5.1 GFLOPs.These results indicate that the improved YOLOv11 is both accurate and computationally efficient,making it suitable for real-time industrial surface defect detection and contributing to the development of practical,high-performance inspection systems.
基金funded by Ministry of Education Humanities and Social Science Research Project,grant number 23YJAZH034The Postgraduate Research and Practice Innovation Program of Jiangsu Province,grant number SJCX25_17National Computer Basic Education Research Project in Higher Education Institutions,grant number 2024-AFCEC-056,2024-AFCEC-057.
文摘To solve the false detection and missed detection problems caused by various types and sizes of defects in the detection of steel surface defects,similar defects and background features,and similarities between different defects,this paper proposes a lightweight detection model named multiscale edge and squeeze-and-excitation attention detection network(MSESE),which is built upon the You Only Look Once version 11 nano(YOLOv11n).To address the difficulty of locating defect edges,we first propose an edge enhancement module(EEM),apply it to the process of multiscale feature extraction,and then propose a multiscale edge enhancement module(MSEEM).By obtaining defect features from different scales and enhancing their edge contours,the module uses the dual-domain selection mechanism to effectively focus on the important areas in the image to ensure that the feature images have richer information and clearer contour features.By fusing the squeeze-and-excitation attention mechanism with the EEM,we obtain a lighter module that can enhance the representation of edge features,which is named the edge enhancement module with squeeze-and-excitation attention(EEMSE).This module was subsequently integrated into the detection head.The enhanced detection head achieves improved edge feature enhancement with reduced computational overhead,while effectively adjusting channel-wise importance and further refining feature representation.Experiments on the NEU-DET dataset show that,compared with the original YOLOv11n,the improved model achieves improvements of 4.1%and 2.2%in terms of mAP@0.5 and mAP@0.5:0.95,respectively,and the GFLOPs value decreases from the original value of 6.4 to 6.2.Furthermore,when compared to current mainstream models,Mamba-YOLOT and RTDETR-R34,our method achieves superior performance with 6.5%and 8.9%higher mAP@0.5,respectively,while maintaining a more compact parameter footprint.These results collectively validate the effectiveness and efficiency of our proposed approach.
基金supported in part by the Technical Service for the Development and Application of an Intelligent Visual Management Platformfor Expressway Construction Progress Based on BIM Technology(grant NO.JKYZLX-2023-09)in partby the Technical Service for the Development of an Early Warning Model in the Research and Application of Key Technologies for Tunnel Operation Safety Monitoring and Early Warning Based on Digital Twin(grant NO.JK-S02-ZNGS-202412-JISHU-FA-0035)sponsored by Yunnan Transportation Science Research Institute Co.,Ltd.
文摘With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-consuming and labor-intensive,but they also struggle to provide consistent,high-precision detection and realtime monitoring of pavement surface defects.To overcome these limitations,we propose an Automatic Recognition of PavementDefect(ARPD)algorithm,which leverages unmanned aerial vehicle(UAV)-based aerial imagery to automate the inspection process.The ARPD framework incorporates a backbone network based on the Selective State Space Model(S3M),which is designed to capture long-range temporal dependencies.This enables effective modeling of dynamic correlations among redundant and often repetitive structures commonly found in road imagery.Furthermore,a neck structure based on Semantics and Detail Infusion(SDI)is introduced to guide cross-scale feature fusion.The SDI module enhances the integration of low-level spatial details with high-level semantic cues,thereby improving feature expressiveness and defect localization accuracy.Experimental evaluations demonstrate that theARPDalgorithm achieves a mean average precision(mAP)of 86.1%on a custom-labeled pavement defect dataset,outperforming the state-of-the-art YOLOv11 segmentation model.The algorithm also maintains strong generalization ability on public datasets.These results confirm that ARPD is well-suited for diverse real-world applications in intelligent,large-scale highway defect monitoring and maintenance planning.
基金supported by the Research Project on Strengthening the Construction of an Important Ecological Security Barrier in Northern China by Higher Education Institutions in the Inner Mongolia Autonomous Region(STAQZX202313)the Inner Mongolia Autonomous Region Education Science‘14th Five-Year Plan’2024 Annual Research Project(NGJGH2024635).
文摘Vacancy defects,as fundamental disruptions in metallic lattices,play an important role in shaping the mechanical and electronic properties of aluminum crystals.However,the influence of vacancy position under coupled thermomechanical fields remains insufficiently understood.In this study,transmission and scanning electron microscopy were employed to observe dislocation structures and grain boundary heterogeneities in processed aluminum alloys,suggesting stress concentrations and microstructural inhomogeneities associated with vacancy accumulation.To complement these observations,first-principles calculations and molecular dynamics simulations were conducted for seven single-vacancy configurations in face-centered cubic aluminum.The stress response,total energy,density of states(DOS),and differential charge density were examined under varying compressive strain(ε=0–0.1)and temperature(0–600 K).The results indicate that face-centered vacancies tend to reduce mechanical strength and perturb electronic states near the Fermi level,whereas corner and edge vacancies appear to have weaker effects.Elevated temperatures may partially restore electronic uniformity through thermal excitation.Overall,these findings suggest that vacancy position exerts a critical but position-dependent influence on coupled structure-property relationships,offering theoretical insights and preliminary experimental support for defect-engineered aluminum alloy design.
基金supported by the Jilin Science and Technology Development Plan (20240101029JJ) for the following study:synchronized high-speed detection of surface shape and defects in the grinding stage of complex surfaces (KLMSZZ202305)for the high-precision wide dynamic large aperture optical inspection system for fine astronomical observation by the National Major Research Instrument Development Project (62127901)+2 种基金for ultrasmooth manufacturing technology of large diameter complex curved surface by the National Key R&D Program(2022YFB3403405)for research on the key technology of rapid synchronous detection of surface shape and subsurface defects in the grinding stage of large diameter complex surfaces by the International Cooperation Project(2025010157)The Key Laboratory of Optical System Advanced Manufacturing Technology,Chinese Academy of Sciences (2022KLOMT02-04) also supported this study
文摘To address the challenges of high-precision optical surface defect detection,we propose a novel design for a wide-field and broadband light field camera in this work.The proposed system can achieve a 50°field of view and operates at both visible and near-infrared wavelengths.Using the principles of light field imaging,the proposed design enables 3D reconstruction of optical surfaces,thus enabling vertical surface height measurements with enhanced accuracy.Using Zemax-based simulations,we evaluate the system’s modulation transfer function,its optical aberrations,and its tolerance to shape variations through Zernike coefficient adjustments.The results demonstrate that this camera can achieve the required spatial resolution while also maintaining high imaging quality and thus offers a promising solution for advanced optical surface defect inspection.
基金financially supported by the supported by Shandong Provincial Natural Science Foundation(ZR2024MB108)Taishan Young Scholar Program(tsqn202312312)Excellent Young Scholars of the Shandong Provincial Natural Science Foundation(Overseas)(2023HWYQ-112)。
文摘Conversion-type electrode materials hold significant promise for potassium-ion batteries(PIBs)due to their high theoretical capacities,yet their practical deployment is hindered by sluggish kinetics and irreversible structural degradation.To overcome these limitations,we propose a rationally engineered nanoreactor architecture that stabilizes defect-rich MoS_(2)via interlayer incorporation of a carbon monolayer,followed by encapsulation within a nitrogen-doped carbon shell,forming a MoSSe@NC heterostructure.This tailored structure synergistically accelerates both K^(+)diffusion kinetics and electron transfer,enabling unprecedented rate performance(107 mAh g^(-1)at 10 Ag^(-1))and ultralong cyclability(86.5%capacity retention after 1200 cycles at 3 A g^(-1)).Mechanistic insights reveal a distinctive“adsorption-conversion”pathway,where sulfur vacancies on exposed S-Mo-S basal planes act as preferential K^(+)adsorption sites,effectively suppressing parasitic phase transitions during intercalation.In situ X-ray diffraction and transmission electron microscopy corroborate the structural reversibility of the conversion reaction,with the carbon matrix dynamically accommodating strain while preserving electrode integrity.This work not only advances the understanding of defect-driven interfacial chemistry in conversion-type materials but also provides a versatile strategy for designing high-performance anodes in next-generation PIBs through heterostructure engineering.
文摘Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To address these issues,this paper proposes SIM-Net,an enhanced detection framework derived from YOLOv11.The model integrates SPDConv to preserve fine-grained features for small object detection,introduces a novel convolutional partial attention module(C2PAM)to suppress redundant background information and highlight salient regions,and employs a multi-scale fusion network(MFN)with a multi-grain contextual module(MGCT)to strengthen contextual representation and accelerate inference.Experimental evaluations demonstrate that SIM-Net achieves 92.4%mAP,92%accuracy,and 89.4%recall with an inference speed of 75.1 FPS,outperforming existing state-of-the-art methods.These results confirm the robustness and real-time applicability of SIM-Net for PCB defect inspection.
基金funded by the Joint Funds of the National Natural Science Foundation of China(U2341223)the Beijing Municipal Natural Science Foundation(No.4232067).
文摘In printed circuit board(PCB)manufacturing,surface defects can significantly affect product quality.To address the performance degradation,high false detection rates,and missed detections caused by complex backgrounds in current intelligent inspection algorithms,this paper proposes CG-YOLOv8,a lightweight and improved model based on YOLOv8n for PCB surface defect detection.The proposed method optimizes the network architecture and compresses parameters to reduce model complexity while maintaining high detection accuracy,thereby enhancing the capability of identifying diverse defects under complex conditions.Specifically,a cascaded multi-receptive field(CMRF)module is adopted to replace the SPPF module in the backbone to improve feature perception,and an inverted residual mobile block(IRMB)is integrated into the C2f module to further enhance performance.Additionally,conventional convolution layers are replaced with GSConv to reduce computational cost,and a lightweight Convolutional Block Attention Module based Convolution(CBAMConv)module is introduced after Grouped Spatial Convolution(GSConv)to preserve accuracy through attention mechanisms.The detection head is also optimized by removing medium and large-scale detection layers,thereby enhancing the model’s ability to detect small-scale defects and further reducing complexity.Experimental results show that,compared to the original YOLOv8n,the proposed CG-YOLOv8 reduces parameter count by 53.9%,improves mAP@0.5 by 2.2%,and increases precision and recall by 2.0%and 1.8%,respectively.These improvements demonstrate that CG-YOLOv8 offers an efficient and lightweight solution for PCB surface defect detection.
基金supported by the following projects:National Natural Science Foundation of China[U24A20135]Science and Technology Program of the State Administration for Market Regulation[2024MK016]+9 种基金Basic Scientific Research Fund Project for Higher Education Institutions of Inner Mongolia(2024YXXS057)Key Project of Natural Science Foundation of Inner Mongolia[2023ZD12]2023 Inner Mongolia Autonomous Region Key R&D and Achievement Transformation Program[2023YFHH0090]Natural Science Foundation of Inner Mongolia[2022MS05006]Talent Development Fund of Inner Mongolia Autonomous RegionFundamental Research Funds for Universities[2023RCTD012]Fundamental Research Funds for Universities[2023QNJS075]Inner Mongolia Autonomous Region Postgraduate Research Innovation Project[KC2024053B]Fundamental Research Funds for Universities[2024YXXS012]Open Project of the National Key Laboratory of Special Vehicle Design and Manufacturing Integration Technology[GZ2023KF012].
文摘In ultrasonic non-destructive testing of high-temperature industrial equipment,sound velocity drift induced by non-uniform temperature fields can severely compromise defect localization accuracy.Conventional approaches that rely on room-temperature sound velocities introduce systematic errors,potentially leading to misjudgment of safety-critical components.Two primary challenges hinder current methods:first,it is difficult to monitor real-time changes in sound velocity distribution within a thermal gradient;second,traditional uniform-temperature correction models fail to capture the nonlinear dependence of material properties on temperature and their effect on ultrasonic velocity fields.Here,we propose a defect localization correction method based on multiphysics coupling.A two-dimensional coupled heat transfer–wave propagation model is established in COMSOL,and a one-dimensional steady-state heat transfer condition is used to design a numerical pulse–echo experiment in 1020 steel.Temperature-dependent material properties are incorporated,and the intrinsic relationship between sound velocity and temperature is derived,confirming consistency with classical theories.To account for gradient temperature fields,a micro-element integration algorithm discretizes the propagation path into segments,each associated with a locally computed temperature from the steady-state heat conduction solution.Defect positions are dynamically corrected through cumulative displacement along the propagation path.By integrating heat conduction and elastic wave propagation in a multiphysics framework,this method overcomes the limitations of uniform-temperature assumptions.The micro-element integration approach enables dynamic tracking of spatially varying sound velocities,offering a robust strategy to enhance ultrasonic testing accuracy in high-temperature industrial environments.
基金National Key R&D Program of China(grant number 2022YFA1207500)National Natural Science Foundation of China(grant number 82072412).
文摘Treating bone defects complicated by bacterial infections remains a significant clinical challenge.Drawing inspiration from the human body's bone repair mechanisms,the use of biomimetic methods to design tissue engineering scaffolds is of great significance for bone repair.This study synthesized copper(Cu)-doped mesoporous silica nanoparticles(Cu@MSN)modified with hydroxyethyl methacrylate to obtain methacrylated Cu@MSN(Cu@MSNMA).Furtheremore,bio-mimetic nanocomposite hydrogels were prepared by adding Cu@MSNMA to a GelMA/gelatin solution.This hydrogel achieves multi-modal bone tissue biomimicry:(ⅰ)GelMA/gelatin mimics the matrix components in bone ECM,ensuring biocompatibility while promoting cellular behavior(such as adhesion,proliferation,and differentiation);(ⅱ)GelMA/gela-tin and the crosslinking sites introduced by Cu@MSNMA form a stable porous network structure,achieving structural and mechanical biomimicry to provide necessary support for bone defects;(ⅲ)The elemental biomimicry of Si and Cu in Cu@MSNMA achieves efficient osteogenic induction.The effect of different proportions of Cu@MSNMA on the physi-cal properties of the composite hydrogels was investigated to determine the optimal proportion.The results indicated that the mechanical properties of hydrogel were enhanced with the increasing Cu@MSNMA mass ratio.Notably,5%NPs/GelMA/gelatin hydrogel exhibited excellent mechanical property compared to the GelMA/gelatin hydrogel.In vitro and vivo cellular experiments demonstrated a significant enhancement in antibacterial and osteogenic induction with Cu@MSNMA addition.In conclusion,the proposed nanocomposite hydrogel with biomimetic components and ion-regulating properties can serve as a multifunctional scaffold,offering antimicrobial properties for infected bone regeneration,and guide for future research in bone regeneration and three-dimensional printing.
基金supported by the National Natural Science Foundation of China(No.52474396 and 52175284)the National Key Research and Development Program of China(Grant No.2022YFB3404201)。
文摘In order to investigate the effect of die wall thickness on morphologies of defect band,a stepped mold with a wall thickness of 5 mm,4 mm,3 mm,2 mm,and 1 mm was designed to carry out high pressure die casting experiments with AlSi10 MgMn alloy.For castings with wall thickness of 2-4 mm,the ratio of the mean defect band width(w)and mean grain size(d)in the defect band(w/d)ranges 7-18,while it increases to 24.47 for the 5 mm-thick casting.This difference is related with the filling speed and the distribution of externally solidified crystals(ESCs).The mold flow analysis indicates that the filling speed decreases from 25.41 m·s^(-1)to 11.07 m·s^(-1)when wall thickness increases from 2 mm to 5 mm.Due to the decreasing filling speed along the wall thickness,ESCs gradually diffuse from the center to the defect band,which keep the shear strength in the defect band at a high-level during filling.Meanwhile,the shear strength generated during the filling also decreases as the shear rate drops.Finally,the defect bands in the 5 mm-thick region become widen and indistinct,and the porosity is as high as 5.25%.
