Wire arc additive manufacturing(WAAM)has emerged as a promising approach for fabricating large-scale components.However,conventional WAAM still faces challenges in optimizing microstructural evolution,minimizing addit...Wire arc additive manufacturing(WAAM)has emerged as a promising approach for fabricating large-scale components.However,conventional WAAM still faces challenges in optimizing microstructural evolution,minimizing additive-induced defects,and alleviating residual stress and deformation,all of which are critical for enhancing the mechanical performance of the manufactured parts.Integrating interlayer friction stir processing(FSP)into WAAM significantly enhances the quality of deposited materials.However,numerical simulation research focusing on elucidating the associated thermomechanical coupling mechanisms remains insufficient.A comprehensive numerical model was developed to simulate the thermomechanical coupling behavior in friction stir-assisted WAAM.The influence of post-deposition FSP on the coupled thermomechanical response of the WAAM process was analyzed quantitatively.Moreover,the residual stress distribution and deformation behavior under both single-layer and multilayer deposition conditions were investigated.Thermal analysis of different deposition layers in WAAM and friction stir-assisted WAAM was conducted.Results show that subsequent layer deposition induces partial remelting of the previously solidified layer,whereas FSP does not cause such remelting.Furthermore,thermal stress and deformation analysis confirm that interlayer FSP effectively mitigates residual stresses and distortion in WAAM components,thereby improving their structural integrity and mechanical properties.展开更多
Laser powder bed fusion(LPBF)is highly suitable for forming 18Ni300 mold steel,thanks to its excellent capability in manufacturing complex shapes and outstanding capacity for regulating microstructures.It is widely us...Laser powder bed fusion(LPBF)is highly suitable for forming 18Ni300 mold steel,thanks to its excellent capability in manufacturing complex shapes and outstanding capacity for regulating microstructures.It is widely used in fields such as injection molding,die casting,and stamping dies.Adding reinforcing particles into steel is an effective means to improve its performance.Nb/18Ni300 composites were fabricated by LPBF using two kinds of Nb powders with different particle sizes,and their microstructures and properties were studied.The results show that the unmelted Nb particles are uniformly distributed in the 18Ni300 matrix and the grains are refined,which is particularly pronounced with fine Nb particles.In addition,element diffusion occurs between the particles and the matrix.The main phases of the base alloy are α-Fe and a small amount of γ-Fe.With the addition of Nb,part of the α-Fe is transformed into γ-Fe,and unmelted Nb phases appear.The addition of Nb also enhances the hardness and wear resistance of the composites but slightly reduces their tensile properties.After aging treatment,the molten pools and grain boundaries become blurred,grains are further refined,and the interfaces around the particles are thinned.The aging treatment also promotes the formation of reverted austenite.The hardness,ultimate tensile strength,and volumetric wear rate of the base alloy reach 51.9 HRC,1704 MPa,and 17.8×10^(-6) mm^(3)/(N·m),respectively.In contrast,the sample added with fine Nb particles has the highest hardness(56.1 HRC),ultimate tensile strength(1892 MPa)and yield strength(1842 MPa),and the volume wear rate of the sample added with coarse Nb particles is reduced by 90%to 1.7×10^(-6) mm^(3)/(N·m).展开更多
Additive manufacturing(AM)technology has emerged as a viable solution for manufacturing complexshaped WC−Co cemented carbide products,thereby expanding their applications in industries such as resource mining,equipmen...Additive manufacturing(AM)technology has emerged as a viable solution for manufacturing complexshaped WC−Co cemented carbide products,thereby expanding their applications in industries such as resource mining,equipment manufacturing,and electronic information.This review provides a comprehensive summary of the progress of AM technology in WC−Co cemented carbides.The fundamental principles and classification of AM techniques are introduced,followed by a categorization and evaluation of the AM techniques for WC−Co cemented carbides.These techniques are classified as either direct AM technology(DAM)or indirect AM technology(IDAM),depending on their inclusion of post-processes like de-binding and sintering.Through an analysis of microstructure features,the most suitable AM route for WC−Co cemented carbide products with controllable microstructure is identified as the indirect AM technology,such as binder jet printing(BJP),which integrates AM with conventional powder metallurgy.展开更多
Additive manufacturing(AM),with its high flexibility,cost-effectiveness,and customization,significantly accelerates the advancement of nanogenerators,contributing to sustainable energy solutions and the Internet of Th...Additive manufacturing(AM),with its high flexibility,cost-effectiveness,and customization,significantly accelerates the advancement of nanogenerators,contributing to sustainable energy solutions and the Internet of Things.In this review,an in-depth analysis of AM for piezoelectric and triboelectric nanogenerators is presented from the perspectives of fundamental mechanisms,recent advancements,and future prospects.It highlights AM-enabled advantages of versatility across materials,structural topology optimization,microstructure design,and integrated printing,which enhance critical performance indicators of nanogenerators,such as surface charge density and piezoelectric constant,thereby improving device performance compared to conventional fabrication.Common AM techniques for nanogenerators,including fused deposition modeling,direct ink writing,stereolithography,and digital light processing,are systematically examined in terms of their working principles,improved metrics(output voltage/current,power density),theoretical explanation,and application scopes.Hierarchical relationships connecting AM technologies with performance optimization and applications of nanogenerators are elucidated,providing a solid foundation for advancements in energy harvesting,self-powered sensors,wearable devices,and human-machine interaction.Furthermore,the challenges related to fabrication quality,cross-scale manufacturing,processing efficiency,and industrial deployment are critically discussed.Finally,the future prospects of AM for nanogenerators are explored,aiming to foster continuous progress and innovation in this field.展开更多
Aqueous zinc metal batteries(AZMBs)are promising candidates for renewable energy storage,yet their practical deployment in subzero environments remains challenging due to electrolyte freezing and dendritic growth.Alth...Aqueous zinc metal batteries(AZMBs)are promising candidates for renewable energy storage,yet their practical deployment in subzero environments remains challenging due to electrolyte freezing and dendritic growth.Although organic additives can enhance the antifreeze properties of electrolytes,their weak polarity diminishes ionic conductivity,and their flammability poses safety concerns,undermining the inherent advantages of aqueous systems.Herein,we present a cost-effective and highly stable Na_(2)SO_(4)additive introduced into a Zn(ClO_(4))2-based electrolyte to create an organic-free antifreeze electrolyte.Through Raman spectroscopy,in situ optical microscopy,densityfunctional theory computations,and molecular dynamics simulations,we demonstrate that Na+ions improve low-temperature electrolyte performance and mitigate dendrite formation by regulating uniform Zn^(2+)deposition through preferential adsorption and electrostatic interactions.As a result,the Zn||Zn cells using this electrolyte achieve a remarkable cycling life of 360 h at-40℃ with 61% depth of discharge,and the Zn||PANI cells retained an ultrahigh capacity retention of 91%even after 8000 charge/discharge cycles at-40℃.This work proposes a cost-effective and practical approach for enhancing the long-term operational stability of AZMBs in low-temperature environments.展开更多
Heart disease remains a leading cause of mortality worldwide,emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention.However,existing Deep Learni...Heart disease remains a leading cause of mortality worldwide,emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention.However,existing Deep Learning(DL)approaches often face several limitations,including inefficient feature extraction,class imbalance,suboptimal classification performance,and limited interpretability,which collectively hinder their deployment in clinical settings.To address these challenges,we propose a novel DL framework for heart disease prediction that integrates a comprehensive preprocessing pipeline with an advanced classification architecture.The preprocessing stage involves label encoding and feature scaling.To address the issue of class imbalance inherent in the personal key indicators of the heart disease dataset,the localized random affine shadowsampling technique is employed,which enhances minority class representation while minimizing overfitting.At the core of the framework lies the Deep Residual Network(DeepResNet),which employs hierarchical residual transformations to facilitate efficient feature extraction and capture complex,non-linear relationships in the data.Experimental results demonstrate that the proposed model significantly outperforms existing techniques,achieving improvements of 3.26%in accuracy,3.16%in area under the receiver operating characteristics,1.09%in recall,and 1.07%in F1-score.Furthermore,robustness is validated using 10-fold crossvalidation,confirming the model’s generalizability across diverse data distributions.Moreover,model interpretability is ensured through the integration of Shapley additive explanations and local interpretable model-agnostic explanations,offering valuable insights into the contribution of individual features to model predictions.Overall,the proposed DL framework presents a robust,interpretable,and clinically applicable solution for heart disease prediction.展开更多
目的基于机器学习构建和验证肝移植术后患者重症监护室(intensive care unit,ICU)住院时间延长(prolonged ICU stay,pLOS-ICU)的预测模型。方法回顾性纳入2013年4月至2024年4月的新疆医科大学第一附属医院接受肝移植并收治ICU的成年患...目的基于机器学习构建和验证肝移植术后患者重症监护室(intensive care unit,ICU)住院时间延长(prolonged ICU stay,pLOS-ICU)的预测模型。方法回顾性纳入2013年4月至2024年4月的新疆医科大学第一附属医院接受肝移植并收治ICU的成年患者。基于10种机器学习方法建立预测模型,通过比较受试者工作特征曲线下面积(area under the curve,AUC)、临床决策曲线等评估模型预测能力,使用可解释机器学习算法(shapley additive explanations,SHAP)对变量进行解释。结果本研究纳入230例患者,分为训练集162例,内部验证集68例。在训练集中对术前人口学特征、基础疾病、术中情况、术后早期实验室指标及支持治疗等共20个变量进行筛选,最终选取术后8 h中性粒细胞占比、术后8 h血红蛋白、术后8 h白蛋白、术后8 h非结合胆红素、术后24 h丙氨酸氨基转移酶(alanine aminotransferase,ALT)、术后是否使用多巴胺及术后是否使用肠外营养支持7个变量进行模型构建,其中Logistic模型为最佳模型(训练集AUC=0.797,内部验证集AUC=0.819)。采用SHAP值评估特征重要性,其中早期肝功能指标、营养指标和全身炎症反应在pLOS-ICU预测中占据核心地位。结论术后早期肝功能指标、炎症状态、循环与营养支持及基础疾病是肝移植患者PLOS-ICU的关键预测因素,SHAP值分析有助于个体化风险评估与早期干预决策。展开更多
基金National Key Research and Development Program of China(2022YFB4600902)Shandong Provincial Science Foundation for Outstanding Young Scholars(ZR2024YQ020)。
文摘Wire arc additive manufacturing(WAAM)has emerged as a promising approach for fabricating large-scale components.However,conventional WAAM still faces challenges in optimizing microstructural evolution,minimizing additive-induced defects,and alleviating residual stress and deformation,all of which are critical for enhancing the mechanical performance of the manufactured parts.Integrating interlayer friction stir processing(FSP)into WAAM significantly enhances the quality of deposited materials.However,numerical simulation research focusing on elucidating the associated thermomechanical coupling mechanisms remains insufficient.A comprehensive numerical model was developed to simulate the thermomechanical coupling behavior in friction stir-assisted WAAM.The influence of post-deposition FSP on the coupled thermomechanical response of the WAAM process was analyzed quantitatively.Moreover,the residual stress distribution and deformation behavior under both single-layer and multilayer deposition conditions were investigated.Thermal analysis of different deposition layers in WAAM and friction stir-assisted WAAM was conducted.Results show that subsequent layer deposition induces partial remelting of the previously solidified layer,whereas FSP does not cause such remelting.Furthermore,thermal stress and deformation analysis confirm that interlayer FSP effectively mitigates residual stresses and distortion in WAAM components,thereby improving their structural integrity and mechanical properties.
基金Key-Area Research and Development Program of Guangdong Province(2023B0909020004)Project of Innovation Research Team in Zhongshan(CXTD2023006)+1 种基金Natural Science Foundation of Guangdong Province(2023A1515011573)Zhongshan Social Welfare Science and Technology Research Project(2024B2022)。
文摘Laser powder bed fusion(LPBF)is highly suitable for forming 18Ni300 mold steel,thanks to its excellent capability in manufacturing complex shapes and outstanding capacity for regulating microstructures.It is widely used in fields such as injection molding,die casting,and stamping dies.Adding reinforcing particles into steel is an effective means to improve its performance.Nb/18Ni300 composites were fabricated by LPBF using two kinds of Nb powders with different particle sizes,and their microstructures and properties were studied.The results show that the unmelted Nb particles are uniformly distributed in the 18Ni300 matrix and the grains are refined,which is particularly pronounced with fine Nb particles.In addition,element diffusion occurs between the particles and the matrix.The main phases of the base alloy are α-Fe and a small amount of γ-Fe.With the addition of Nb,part of the α-Fe is transformed into γ-Fe,and unmelted Nb phases appear.The addition of Nb also enhances the hardness and wear resistance of the composites but slightly reduces their tensile properties.After aging treatment,the molten pools and grain boundaries become blurred,grains are further refined,and the interfaces around the particles are thinned.The aging treatment also promotes the formation of reverted austenite.The hardness,ultimate tensile strength,and volumetric wear rate of the base alloy reach 51.9 HRC,1704 MPa,and 17.8×10^(-6) mm^(3)/(N·m),respectively.In contrast,the sample added with fine Nb particles has the highest hardness(56.1 HRC),ultimate tensile strength(1892 MPa)and yield strength(1842 MPa),and the volume wear rate of the sample added with coarse Nb particles is reduced by 90%to 1.7×10^(-6) mm^(3)/(N·m).
基金supported by Major Science and Technology Projects in Fujian Province,China(No.2023HZ021005)State Key Laboratory of Powder Metallurgy,Central South University,ChinaFujian Key Laboratory of Rare-earth Functional Materials,China。
文摘Additive manufacturing(AM)technology has emerged as a viable solution for manufacturing complexshaped WC−Co cemented carbide products,thereby expanding their applications in industries such as resource mining,equipment manufacturing,and electronic information.This review provides a comprehensive summary of the progress of AM technology in WC−Co cemented carbides.The fundamental principles and classification of AM techniques are introduced,followed by a categorization and evaluation of the AM techniques for WC−Co cemented carbides.These techniques are classified as either direct AM technology(DAM)or indirect AM technology(IDAM),depending on their inclusion of post-processes like de-binding and sintering.Through an analysis of microstructure features,the most suitable AM route for WC−Co cemented carbide products with controllable microstructure is identified as the indirect AM technology,such as binder jet printing(BJP),which integrates AM with conventional powder metallurgy.
基金support from the Research Committee of The Hong Kong Polytechnic University(Project codes:RMJK and 4-ZZSJ)supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region,China(Project No.PolyU15212523).
文摘Additive manufacturing(AM),with its high flexibility,cost-effectiveness,and customization,significantly accelerates the advancement of nanogenerators,contributing to sustainable energy solutions and the Internet of Things.In this review,an in-depth analysis of AM for piezoelectric and triboelectric nanogenerators is presented from the perspectives of fundamental mechanisms,recent advancements,and future prospects.It highlights AM-enabled advantages of versatility across materials,structural topology optimization,microstructure design,and integrated printing,which enhance critical performance indicators of nanogenerators,such as surface charge density and piezoelectric constant,thereby improving device performance compared to conventional fabrication.Common AM techniques for nanogenerators,including fused deposition modeling,direct ink writing,stereolithography,and digital light processing,are systematically examined in terms of their working principles,improved metrics(output voltage/current,power density),theoretical explanation,and application scopes.Hierarchical relationships connecting AM technologies with performance optimization and applications of nanogenerators are elucidated,providing a solid foundation for advancements in energy harvesting,self-powered sensors,wearable devices,and human-machine interaction.Furthermore,the challenges related to fabrication quality,cross-scale manufacturing,processing efficiency,and industrial deployment are critically discussed.Finally,the future prospects of AM for nanogenerators are explored,aiming to foster continuous progress and innovation in this field.
基金financially supported by the National Natural Science Foundation of China(Grant No.52377206,52307237)Natural Science Foundation of Heilongjiang Province of China(YQ2024E046)Postdoctoral Science Foundation of Heilongjiang Province of China(LBH-TZ2413,LBH-Z23198)。
文摘Aqueous zinc metal batteries(AZMBs)are promising candidates for renewable energy storage,yet their practical deployment in subzero environments remains challenging due to electrolyte freezing and dendritic growth.Although organic additives can enhance the antifreeze properties of electrolytes,their weak polarity diminishes ionic conductivity,and their flammability poses safety concerns,undermining the inherent advantages of aqueous systems.Herein,we present a cost-effective and highly stable Na_(2)SO_(4)additive introduced into a Zn(ClO_(4))2-based electrolyte to create an organic-free antifreeze electrolyte.Through Raman spectroscopy,in situ optical microscopy,densityfunctional theory computations,and molecular dynamics simulations,we demonstrate that Na+ions improve low-temperature electrolyte performance and mitigate dendrite formation by regulating uniform Zn^(2+)deposition through preferential adsorption and electrostatic interactions.As a result,the Zn||Zn cells using this electrolyte achieve a remarkable cycling life of 360 h at-40℃ with 61% depth of discharge,and the Zn||PANI cells retained an ultrahigh capacity retention of 91%even after 8000 charge/discharge cycles at-40℃.This work proposes a cost-effective and practical approach for enhancing the long-term operational stability of AZMBs in low-temperature environments.
基金funded by Ongoing Research Funding Program for Project number(ORF-2025-648),King Saud University,Riyadh,Saudi Arabia.
文摘Heart disease remains a leading cause of mortality worldwide,emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention.However,existing Deep Learning(DL)approaches often face several limitations,including inefficient feature extraction,class imbalance,suboptimal classification performance,and limited interpretability,which collectively hinder their deployment in clinical settings.To address these challenges,we propose a novel DL framework for heart disease prediction that integrates a comprehensive preprocessing pipeline with an advanced classification architecture.The preprocessing stage involves label encoding and feature scaling.To address the issue of class imbalance inherent in the personal key indicators of the heart disease dataset,the localized random affine shadowsampling technique is employed,which enhances minority class representation while minimizing overfitting.At the core of the framework lies the Deep Residual Network(DeepResNet),which employs hierarchical residual transformations to facilitate efficient feature extraction and capture complex,non-linear relationships in the data.Experimental results demonstrate that the proposed model significantly outperforms existing techniques,achieving improvements of 3.26%in accuracy,3.16%in area under the receiver operating characteristics,1.09%in recall,and 1.07%in F1-score.Furthermore,robustness is validated using 10-fold crossvalidation,confirming the model’s generalizability across diverse data distributions.Moreover,model interpretability is ensured through the integration of Shapley additive explanations and local interpretable model-agnostic explanations,offering valuable insights into the contribution of individual features to model predictions.Overall,the proposed DL framework presents a robust,interpretable,and clinically applicable solution for heart disease prediction.
