Metal additive manufacturing(AM)technology has promising applications across many fields due to its near-net-shape advantages.The quality of the as-built component is closely linked to the temperature evolution during...Metal additive manufacturing(AM)technology has promising applications across many fields due to its near-net-shape advantages.The quality of the as-built component is closely linked to the temperature evolution during the metal AM process,which exhibits strong nonlinearities,localized high gradients,and rapid cooling rates.Therefore,real-time prediction of the temperature field is essential for effective online process control to achieve high fabrication quality,which poses surprising challenges for numerical methods,as traditional methods suffer from the inherent time-consuming nature of fine time-space discretizations.In this study,we proposed an isothermal surface imaging and transfer learning framework for fast prediction of isothermal surfaces,which are further used to reconstruct the high-dimensional,nonlinear temperature field.It consists of three key parts:physics-guided isothermal surface imaging to reduce the problem dimensionality by transforming the unstructured temperature field into a series of structured grayscale images,a pre-trained hybrid parameter-to-image generative neural network for the isothermal surface prediction in favor of small training samples,and a transfer learning strategy leveraging physical similarity of these isothermal surfaces in the metal AM process to obtain the 3D temperature field.The training samples are generated using a high-fidelity numerical model,which is validated against experimental data.The predicted results from the proposed framework agree well with those from the high-fidelity numerical simulation for a given combination of process parameters,achieving a computational cost measured in seconds.It is expected that the proposed framework could serve as a powerful tool for predicting the temperature field and further facilitating online control of process parameters.展开更多
Additive manufacturing(AM)promotes the production of metallic parts with significant design flexibility,yet its use in critical applications is hindered by challenges in ensuring consistent quality and performance.Pro...Additive manufacturing(AM)promotes the production of metallic parts with significant design flexibility,yet its use in critical applications is hindered by challenges in ensuring consistent quality and performance.Process variability often leads to defects,insufficient geometric accuracy and inadequate material properties,which are difficult to effectively manage due to limitations of traditional quality control methods in modeling highdimensional nonlinear relationships and enabling adaptive control.Machine learning(ML)offers a transformative approach to model intricate process-structure-property relationships by leveraging the rich data environment of AM.The study presents a comprehensive examination of ML-driven quality assurance implementations in metallic AM.First,it uniquely examines the innovative exploration of ML in predicting and understanding the fundamental multi-physics fields that influence the quality of a fabricated component,including temperature fields,fluid dynamics and stress/strain evolution.Subsequently,the application of ML in optimizing key quality attributes,including defect detection and mitigation(porosity,cracks,etc.),geometric fidelity enhancement(dimensional accuracy,surface roughness,etc.)and material property tailoring(mechanical strength,fatigue life,corrosion resistance,etc.),are discussed in detail.Finally,the development of ML-driven real-time closed-loop control systems for intelligent quality assurance,the strategies for addressing the data scarcity and cross-scenario transferability in metal AM are discussed.This article provides a novel perspective on the profound potential of ML technology for metal AM quality control applications,highlights the challenges faced during research,and outlines future development directions.展开更多
Metal additive manufacturing(MAM)technology has experienced rapid development in recent years.As both equipment and materials progress towards increased maturity and commercialization,material metallurgy technology ba...Metal additive manufacturing(MAM)technology has experienced rapid development in recent years.As both equipment and materials progress towards increased maturity and commercialization,material metallurgy technology based on high energy sources has become a key factor influencing the future development of MAM.The calculation of phase diagrams(CALPHAD)is an essential method and tool for constructing multi-component phase diagrams by employing experimental phase diagrams and Gibbs free energy models of simple systems.By combining with the element mobility data and non-equilibrium phase transition model,it has been widely used in the analysis of traditional metal materials.The development of CALPHAD application technology for MAM is focused on the compositional design of printable materials,the reduction of metallurgical imperfections,and the control of microstructural attributes.This endeavor carries considerable theoretical and practical significance.This paper summarizes the important achievements of CALPHAD in additive manufacturing(AM)technology in recent years,including material design,process parameter optimization,microstructure evolution simulation,and properties prediction.Finally,the limitations of applying CALPHAD technology to MAM technology are discussed,along with prospective research directions.展开更多
The most widely used metal additive manufacturing processes utilize powder that is spread or fed onto a building platform. Although there are reviews of the literature on some aspects of the powder, many aspects have ...The most widely used metal additive manufacturing processes utilize powder that is spread or fed onto a building platform. Although there are reviews of the literature on some aspects of the powder, many aspects have been under-reviewed or unreviewed. The present work is a review of the literature on these aspects. Articles published in the open literature through the end of February 2022 were collected by consulting highly regarded relevant bibliographic databases, such as Google Scholar and Science Direct. The aspects reviewed were emerging methods of powder production, methods used to improve the quality of a powder after production by a well-established method, influence of variables of well-established powder production methods on powder properties, influence of powder production method on powder properties, and influence of powder reuse on properties of powders of a wide collection of alloys. One key finding was that with regard to powder reuse, the only consistent finding is that it leads to increase in the oxygen content of the powder. Another key finding was that the literature on the aspects of the literature reviewed herein contains many shortcomings and gaps, which suggest potential areas for future research, such as techniques for optimization of process variables for a given combination of metal powder and powder production method and development of methods for production of powders of new/emerging metallic materials.展开更多
Machine learning prediction models for thin wire-based metal additive manufacturing(MAM)process were proposed,aiming at the complex relationship between the process parameters and the geometric characteristics of sing...Machine learning prediction models for thin wire-based metal additive manufacturing(MAM)process were proposed,aiming at the complex relationship between the process parameters and the geometric characteristics of single track of the deposition layer and surface roughness.The effects of laser power,wire feeding speed and scanning speed on the width and height of the single track and surface roughness were experimentally studied.The results show that laser power has a significant impact on the width of the single track but little effect on the height.As the wire feeding speed increases,the width and height of the single track increase,especially the height.The faster the scanning speed,the smaller the width of the single track,while the height does not change much.Then,support vector regression(SVR)and artificial neural network(ANN)regression methods were employed to set up prediction models.The SVR and ANN regression models perform well in predicting the width,with a smaller root mean square error and a higher correlation coefficient R2.Compared with the ANN model,the SVR model performs better both in predicting geometric characteristics of single track and surface roughness.Multi-layer thin-walled parts were manufactured to verify the accuracy of the models.展开更多
Many engineering alloys originally designed for conventional manufacturing lack considerations for additive manufacturing (AM), presenting opportunities for novel alloy designs. Evaluating alloy printability requires ...Many engineering alloys originally designed for conventional manufacturing lack considerations for additive manufacturing (AM), presenting opportunities for novel alloy designs. Evaluating alloy printability requires extensive analysis of chemical composition and processing conditions. The complexity of experimental exploration drives the need for high-throughput computational frameworks. This study introduces a framework that integrates material properties, processing parameters, and melt pool profiles from three thermal models to assess process-induced defects, such as lack-of-fusion, balling, and keyholing. A deep learning surrogate model accelerates the printability assessment by 1000 times without losing accuracy. We validate the framework with printability maps for the equiatomic CoCrFeMnNi system and apply it to explore printable alloys in the Co-Cr-Fe-Mn-Ni high-entropy alloy space. Ensemble probabilistic printability maps further provide insights into defect likelihood and uncertainty, enhancing alloy design for AM by efficiently navigating vast design spaces.展开更多
Metal additive manufacturing(MAM)is an emerging and disruptive technology that builds three-dimensional(3D)components by adding layer-upon-layer of metallic materials.The complex cyclic thermal history and highly loca...Metal additive manufacturing(MAM)is an emerging and disruptive technology that builds three-dimensional(3D)components by adding layer-upon-layer of metallic materials.The complex cyclic thermal history and highly localized energy can produce large temperature gradients,which will,in turn,lead to compressive and tensile stress during the MAM process and eventually result in residual stress.Being an issue of great concern,residual stress,which can cause distortion,delamination,cracking,etc.,is considered a key mechanical quantity that affects the manufacturing quality and service performance of MAM parts.In this review paper,the ongoing work in the field of residual stress determination and control for MAM is described with a particular emphasis on the experimental measurement/control methods and numerical models.We also provide insight on what still requires to be achieved and the research opportunities and challenges.展开更多
CONSPECTUS:Additive Manufacturing(AM)technology produces three-dimensional components in a layer-by-layer fashion and offers numerous advantages over conventional manufacturing processes.Driven by the growing needs of...CONSPECTUS:Additive Manufacturing(AM)technology produces three-dimensional components in a layer-by-layer fashion and offers numerous advantages over conventional manufacturing processes.Driven by the growing needs of diverse industrial sectors,this technology has seen significant advances on both scientific and engineering fronts.Fusion-based processes are the mainstream techniques for AM of metallic materials.As the metals go through melting and solidification during the printing processes,the final microstructure and hence the properties of the printed components are highly sensitive to the printing conditions and can be very different from those of the feedstock.It is critical to understand the process-microstructure-property relationship for the accelerated optimization of the processing conditions and certification of the printed components.While experimentation has been used widely to acquire a mechanistic understanding of this subject matter,numerical modeling has become increasingly helpful in achieving the same purpose.In this Account,the authors review their ongoing collaborative effort to establish a multiphysics modeling framework to predict the process-microstructure-property relationship in fusion-based metal AM processes.The framework includes three individual modules to simulate the dominating physics that dictate the process dynamics and microstructure evolution during printing as well as the responses of the printed microstructure to specific mechanical loadings.The process model uses the material properties and processing conditions as the inputs and simulates the laser-material interaction,multiphase thermo-fluid flow,and fluid-driven powder motion.It has successfully revealed the physical causes of depression zone shape variation as well as powder motion during the laser powder bed fusion process.The microstructure model uses the thermal history of the printing process and the material chemistry as the inputs and predicts the nucleation and growth of multiple grains in the multipass and multilayer printing processes.It has been used to understand the effects of inoculation and thermal conditions on grain texture evolution.The property models use microstructure data from simulations,experimental measurements,or statistical analyses as the inputs and leverage various computational tools to predict the mechanical response of the AM materials.These models have been used to quantitatively evaluate the effects of grain structure,residual strain,and pore and void defects on their properties and performance.While this and many other modeling works have significantly grown our collective knowledge of the process-microstructure-property relationship in fusion-based metal AM processes,efforts should be further invested in developing advanced theories and algorithms for the governing physics,leveraging data-driven approaches,accelerating simulation speed,and calibrating/validating models with controlled experimental measurements,among other aspects.展开更多
Defect formation is a critical challenge for powder-based metal additive manufacturing(AM).Current understanding on the three important issues including formation mechanism,influence and control method of metal AM def...Defect formation is a critical challenge for powder-based metal additive manufacturing(AM).Current understanding on the three important issues including formation mechanism,influence and control method of metal AM defects should be updated.In this review paper,multi-scale defects in AMed metals and alloys are identified and for the first time classified into three categories,including geometry related,surface integrity related and microstructural defects.In particular,the microstructural defects are further divided into internal cracks and pores,textured columnar grains,compositional defects and dislocation cells.The root causes of the multi-scale defects are discussed.The key factors that affect the defect formation are identified and analyzed.The detection methods and modeling of the multi-scale defects are briefly introduced.The effects of the multi-scale defects on the mechanical properties especially for tensile properties and fatigue performance of AMed metallic components are reviewed.Various control and mitigation methods for the corresponding defects,include process parameter control,post processing,alloy design and hybrid AM techniques,are summarized and discussed.From research aspect,current research gaps and future prospects from three important aspects of the multi-scale AM defects are identified and delineated.展开更多
Solid-state sintering is a crucial thermal post-processing step in metal extrusion additive manufacturing(MExAM),influencing the microstructural evolution,densification,and final properties of fabricated components.Ho...Solid-state sintering is a crucial thermal post-processing step in metal extrusion additive manufacturing(MExAM),influencing the microstructural evolution,densification,and final properties of fabricated components.However,accurately simulating this process remains challenging due to its inherently multiscale and multiphysics nature.This comprehensive and critical review examines the main computational approaches developed to model solidstate sintering in the MExAM context,ranging from nano-to macrostructure scales,including molecular dynamics,kinetic Monte Carlo,discrete element methods,phase-field models,and continuum-based methods.For each,we detail the underlying mathematical formulations,numerical strategies,and implementation environments.Their capabilities and limitations are evaluated in terms of scale resolution,physical accuracy,and computational demands.Particular attention is given to challenges such as the coupling of thermal,mechanical,and diffusive phenomena,as well as the difficulty of bridging disparate spatial and temporal scales.In response to these limitations,emerging trends such as Physics-informed machine learning(PIML)offer promising avenues to enhance predictive accuracy,improve computational efficiency,and streamline simulation workflows.By integrating conventional modeling techniques with data-driven approaches,the field is moving toward faster and more reliable predictions of sintering behavior,advancing the goals of the MExAM initiative.The insights presented aim to guide future research focused on optimizing sintering processes for broader industrial applications and improved material performance.展开更多
A numerical model is presented in this article to investigate the interactions between laser generated ultrasonic and the microdefects(0.01 to 0.1 mm),which are on the surface of the laser powder bed fusion additive m...A numerical model is presented in this article to investigate the interactions between laser generated ultrasonic and the microdefects(0.01 to 0.1 mm),which are on the surface of the laser powder bed fusion additive manufactured 316L stainless steel.Firstly,the influence of the transient sound field and detection positions on Rayleigh wave signals are investigated.The interactions between the varied microdefects and the laser ultrasonic are studied.It is shown that arrival time of reflected Rayleigh(RR)waves wave is only related to the location of defects.The depth can be checked from the feature point Q,the displacement amplitude and time delay of converted transverse(RS)wave,while the width information can be evaluated from the RS wave time delay.With the aid of fitting curves,it is found to be linearly related.This simulation study provides a theoretical basis for quantitative detection of surface microdefects of additive manufactured 316L stainless steel components.展开更多
A comparative study on the performance of gas atomized(GA)and rotating-disk atomized(RDA)aluminum alloy powders produced on industrial scale for laser directed energy deposition(L-DED)process was carried out.The powde...A comparative study on the performance of gas atomized(GA)and rotating-disk atomized(RDA)aluminum alloy powders produced on industrial scale for laser directed energy deposition(L-DED)process was carried out.The powder characteristics,the printing process window,and the quality,microstructure,and mechanical properties of printed parts were taken into account for comparison and discussion.The results demonstrate that the RDA powder is superior to the GA powder in terms of sphericity,surface quality,internal defects,flowability,and apparent density,together with a larger printing process window during the L-DED parts fabrication.Besides,the resultant parts from the RDA powder have higher dimensional accuracy,lower internal defects,more uniform and finer microstructure,and more favorable mechanical properties than those from the GA powder.展开更多
In solid-state metal additive manufacturing,oxide removal is crucial for high-quality bonding,yet its impact remains unclear.In the article,the influence of oxides on the bonding interface by using cold spray additive...In solid-state metal additive manufacturing,oxide removal is crucial for high-quality bonding,yet its impact remains unclear.In the article,the influence of oxides on the bonding interface by using cold spray additive manufacturing and leveraging diverse deformation mechanisms of high-entropy alloys is revealed.Advanced microstructure characterization shows that oxide fragmentation drives grain refinement via lattice rotation and substructure formation.As grain refinement progresses,oxide fragmentation increases metal-to-metal contact,promoting metallurgical bonding.These findings offer deep insights into the role of native oxides at the bonding interface and provide guidance for optimizing bond quality in solid-state additive manufacturing.展开更多
基金funded by the National Natural Science Foundation of China under Grant No.11972086the Fundamental Research Funds for the Central Universities。
文摘Metal additive manufacturing(AM)technology has promising applications across many fields due to its near-net-shape advantages.The quality of the as-built component is closely linked to the temperature evolution during the metal AM process,which exhibits strong nonlinearities,localized high gradients,and rapid cooling rates.Therefore,real-time prediction of the temperature field is essential for effective online process control to achieve high fabrication quality,which poses surprising challenges for numerical methods,as traditional methods suffer from the inherent time-consuming nature of fine time-space discretizations.In this study,we proposed an isothermal surface imaging and transfer learning framework for fast prediction of isothermal surfaces,which are further used to reconstruct the high-dimensional,nonlinear temperature field.It consists of three key parts:physics-guided isothermal surface imaging to reduce the problem dimensionality by transforming the unstructured temperature field into a series of structured grayscale images,a pre-trained hybrid parameter-to-image generative neural network for the isothermal surface prediction in favor of small training samples,and a transfer learning strategy leveraging physical similarity of these isothermal surfaces in the metal AM process to obtain the 3D temperature field.The training samples are generated using a high-fidelity numerical model,which is validated against experimental data.The predicted results from the proposed framework agree well with those from the high-fidelity numerical simulation for a given combination of process parameters,achieving a computational cost measured in seconds.It is expected that the proposed framework could serve as a powerful tool for predicting the temperature field and further facilitating online control of process parameters.
基金supported by the National Key R&D Program of China(No.2024YFB4609700)Major Research Plan of the National Natural Science Foundation of China(No.92266102)+4 种基金National Natural Science Foundation of China(No.52271135,No.52433016)Open project of Key Laboratory of Green Fabrication and Surface Technology of Advanced Metal Materials,China(No.GFST2024KF05)Innovative Research Group Project of Hubei Provincial Natural Science Foundation,China(No.2025AFA014)ECU DVC Strategic Research Support Fund,Australia(No.23965)Natural Science Foundation of Hubei Province,China(No.2025AFD399).
文摘Additive manufacturing(AM)promotes the production of metallic parts with significant design flexibility,yet its use in critical applications is hindered by challenges in ensuring consistent quality and performance.Process variability often leads to defects,insufficient geometric accuracy and inadequate material properties,which are difficult to effectively manage due to limitations of traditional quality control methods in modeling highdimensional nonlinear relationships and enabling adaptive control.Machine learning(ML)offers a transformative approach to model intricate process-structure-property relationships by leveraging the rich data environment of AM.The study presents a comprehensive examination of ML-driven quality assurance implementations in metallic AM.First,it uniquely examines the innovative exploration of ML in predicting and understanding the fundamental multi-physics fields that influence the quality of a fabricated component,including temperature fields,fluid dynamics and stress/strain evolution.Subsequently,the application of ML in optimizing key quality attributes,including defect detection and mitigation(porosity,cracks,etc.),geometric fidelity enhancement(dimensional accuracy,surface roughness,etc.)and material property tailoring(mechanical strength,fatigue life,corrosion resistance,etc.),are discussed in detail.Finally,the development of ML-driven real-time closed-loop control systems for intelligent quality assurance,the strategies for addressing the data scarcity and cross-scenario transferability in metal AM are discussed.This article provides a novel perspective on the profound potential of ML technology for metal AM quality control applications,highlights the challenges faced during research,and outlines future development directions.
基金supported by the National Key Research and Development Program of China(No.2021YFB3702500)。
文摘Metal additive manufacturing(MAM)technology has experienced rapid development in recent years.As both equipment and materials progress towards increased maturity and commercialization,material metallurgy technology based on high energy sources has become a key factor influencing the future development of MAM.The calculation of phase diagrams(CALPHAD)is an essential method and tool for constructing multi-component phase diagrams by employing experimental phase diagrams and Gibbs free energy models of simple systems.By combining with the element mobility data and non-equilibrium phase transition model,it has been widely used in the analysis of traditional metal materials.The development of CALPHAD application technology for MAM is focused on the compositional design of printable materials,the reduction of metallurgical imperfections,and the control of microstructural attributes.This endeavor carries considerable theoretical and practical significance.This paper summarizes the important achievements of CALPHAD in additive manufacturing(AM)technology in recent years,including material design,process parameter optimization,microstructure evolution simulation,and properties prediction.Finally,the limitations of applying CALPHAD technology to MAM technology are discussed,along with prospective research directions.
文摘The most widely used metal additive manufacturing processes utilize powder that is spread or fed onto a building platform. Although there are reviews of the literature on some aspects of the powder, many aspects have been under-reviewed or unreviewed. The present work is a review of the literature on these aspects. Articles published in the open literature through the end of February 2022 were collected by consulting highly regarded relevant bibliographic databases, such as Google Scholar and Science Direct. The aspects reviewed were emerging methods of powder production, methods used to improve the quality of a powder after production by a well-established method, influence of variables of well-established powder production methods on powder properties, influence of powder production method on powder properties, and influence of powder reuse on properties of powders of a wide collection of alloys. One key finding was that with regard to powder reuse, the only consistent finding is that it leads to increase in the oxygen content of the powder. Another key finding was that the literature on the aspects of the literature reviewed herein contains many shortcomings and gaps, which suggest potential areas for future research, such as techniques for optimization of process variables for a given combination of metal powder and powder production method and development of methods for production of powders of new/emerging metallic materials.
基金173 Basic Strengthening ProgramXi'an Science and Technology Plan(21ZCZZHXJS-QCY6-0002)。
文摘Machine learning prediction models for thin wire-based metal additive manufacturing(MAM)process were proposed,aiming at the complex relationship between the process parameters and the geometric characteristics of single track of the deposition layer and surface roughness.The effects of laser power,wire feeding speed and scanning speed on the width and height of the single track and surface roughness were experimentally studied.The results show that laser power has a significant impact on the width of the single track but little effect on the height.As the wire feeding speed increases,the width and height of the single track increase,especially the height.The faster the scanning speed,the smaller the width of the single track,while the height does not change much.Then,support vector regression(SVR)and artificial neural network(ANN)regression methods were employed to set up prediction models.The SVR and ANN regression models perform well in predicting the width,with a smaller root mean square error and a higher correlation coefficient R2.Compared with the ANN model,the SVR model performs better both in predicting geometric characteristics of single track and surface roughness.Multi-layer thin-walled parts were manufactured to verify the accuracy of the models.
基金support of NSF, United States, through Grant No. 1849085. BV, SS, and DS acknowledge Grant No. NSF-DGE-1545403 (NSF-NRT: Data-Enabled Discovery and Design of Energy Materials, D3EM)The authors would also like to acknowledge the NASA-ESI Program under Grant Number 80NSSC21K0223 and the ARPA-E ULTIMATE Program through Project DE-AR0001427RA acknowledges NSF-DMREF through Grant No. 2323611. High-throughput calculations were conducted partly at the Texas A&M High-Performance Research Computing (HPRC) Facility.
文摘Many engineering alloys originally designed for conventional manufacturing lack considerations for additive manufacturing (AM), presenting opportunities for novel alloy designs. Evaluating alloy printability requires extensive analysis of chemical composition and processing conditions. The complexity of experimental exploration drives the need for high-throughput computational frameworks. This study introduces a framework that integrates material properties, processing parameters, and melt pool profiles from three thermal models to assess process-induced defects, such as lack-of-fusion, balling, and keyholing. A deep learning surrogate model accelerates the printability assessment by 1000 times without losing accuracy. We validate the framework with printability maps for the equiatomic CoCrFeMnNi system and apply it to explore printable alloys in the Co-Cr-Fe-Mn-Ni high-entropy alloy space. Ensemble probabilistic printability maps further provide insights into defect likelihood and uncertainty, enhancing alloy design for AM by efficiently navigating vast design spaces.
基金financially supported by the National Natural Science Foundation of China(12032013,12272131)the Provincial Natural Science Foundation of Hunan(2022JJ40029)the Scientific Research Foundation of Hunan Provincial Education Department(21C0087)。
文摘Metal additive manufacturing(MAM)is an emerging and disruptive technology that builds three-dimensional(3D)components by adding layer-upon-layer of metallic materials.The complex cyclic thermal history and highly localized energy can produce large temperature gradients,which will,in turn,lead to compressive and tensile stress during the MAM process and eventually result in residual stress.Being an issue of great concern,residual stress,which can cause distortion,delamination,cracking,etc.,is considered a key mechanical quantity that affects the manufacturing quality and service performance of MAM parts.In this review paper,the ongoing work in the field of residual stress determination and control for MAM is described with a particular emphasis on the experimental measurement/control methods and numerical models.We also provide insight on what still requires to be achieved and the research opportunities and challenges.
基金support provided by the National Science Foundation under Grant No.CMMI-2119671.
文摘CONSPECTUS:Additive Manufacturing(AM)technology produces three-dimensional components in a layer-by-layer fashion and offers numerous advantages over conventional manufacturing processes.Driven by the growing needs of diverse industrial sectors,this technology has seen significant advances on both scientific and engineering fronts.Fusion-based processes are the mainstream techniques for AM of metallic materials.As the metals go through melting and solidification during the printing processes,the final microstructure and hence the properties of the printed components are highly sensitive to the printing conditions and can be very different from those of the feedstock.It is critical to understand the process-microstructure-property relationship for the accelerated optimization of the processing conditions and certification of the printed components.While experimentation has been used widely to acquire a mechanistic understanding of this subject matter,numerical modeling has become increasingly helpful in achieving the same purpose.In this Account,the authors review their ongoing collaborative effort to establish a multiphysics modeling framework to predict the process-microstructure-property relationship in fusion-based metal AM processes.The framework includes three individual modules to simulate the dominating physics that dictate the process dynamics and microstructure evolution during printing as well as the responses of the printed microstructure to specific mechanical loadings.The process model uses the material properties and processing conditions as the inputs and simulates the laser-material interaction,multiphase thermo-fluid flow,and fluid-driven powder motion.It has successfully revealed the physical causes of depression zone shape variation as well as powder motion during the laser powder bed fusion process.The microstructure model uses the thermal history of the printing process and the material chemistry as the inputs and predicts the nucleation and growth of multiple grains in the multipass and multilayer printing processes.It has been used to understand the effects of inoculation and thermal conditions on grain texture evolution.The property models use microstructure data from simulations,experimental measurements,or statistical analyses as the inputs and leverage various computational tools to predict the mechanical response of the AM materials.These models have been used to quantitatively evaluate the effects of grain structure,residual strain,and pore and void defects on their properties and performance.While this and many other modeling works have significantly grown our collective knowledge of the process-microstructure-property relationship in fusion-based metal AM processes,efforts should be further invested in developing advanced theories and algorithms for the governing physics,leveraging data-driven approaches,accelerating simulation speed,and calibrating/validating models with controlled experimental measurements,among other aspects.
基金the funding support to this research via the projects of ZVMR,BBAT and ZE1W from The Hong Kong Polytechnic Universityproject#RNE-p2–21 of the Shun Hing Institute of Advanced EngineeringThe Chinese University of Hong Kong and the GRF projects(Nos.15223520 and 15228621)。
文摘Defect formation is a critical challenge for powder-based metal additive manufacturing(AM).Current understanding on the three important issues including formation mechanism,influence and control method of metal AM defects should be updated.In this review paper,multi-scale defects in AMed metals and alloys are identified and for the first time classified into three categories,including geometry related,surface integrity related and microstructural defects.In particular,the microstructural defects are further divided into internal cracks and pores,textured columnar grains,compositional defects and dislocation cells.The root causes of the multi-scale defects are discussed.The key factors that affect the defect formation are identified and analyzed.The detection methods and modeling of the multi-scale defects are briefly introduced.The effects of the multi-scale defects on the mechanical properties especially for tensile properties and fatigue performance of AMed metallic components are reviewed.Various control and mitigation methods for the corresponding defects,include process parameter control,post processing,alloy design and hybrid AM techniques,are summarized and discussed.From research aspect,current research gaps and future prospects from three important aspects of the multi-scale AM defects are identified and delineated.
基金the financial support provided by Total Energies S.E.,contract No.FR00055666 and the French Em-bassy in Angola.
文摘Solid-state sintering is a crucial thermal post-processing step in metal extrusion additive manufacturing(MExAM),influencing the microstructural evolution,densification,and final properties of fabricated components.However,accurately simulating this process remains challenging due to its inherently multiscale and multiphysics nature.This comprehensive and critical review examines the main computational approaches developed to model solidstate sintering in the MExAM context,ranging from nano-to macrostructure scales,including molecular dynamics,kinetic Monte Carlo,discrete element methods,phase-field models,and continuum-based methods.For each,we detail the underlying mathematical formulations,numerical strategies,and implementation environments.Their capabilities and limitations are evaluated in terms of scale resolution,physical accuracy,and computational demands.Particular attention is given to challenges such as the coupling of thermal,mechanical,and diffusive phenomena,as well as the difficulty of bridging disparate spatial and temporal scales.In response to these limitations,emerging trends such as Physics-informed machine learning(PIML)offer promising avenues to enhance predictive accuracy,improve computational efficiency,and streamline simulation workflows.By integrating conventional modeling techniques with data-driven approaches,the field is moving toward faster and more reliable predictions of sintering behavior,advancing the goals of the MExAM initiative.The insights presented aim to guide future research focused on optimizing sintering processes for broader industrial applications and improved material performance.
基金supported by the National Key Research and Development Program of China(No.2017YFB1103900)the National Natural Science Foundation of China(No.51605340)。
文摘A numerical model is presented in this article to investigate the interactions between laser generated ultrasonic and the microdefects(0.01 to 0.1 mm),which are on the surface of the laser powder bed fusion additive manufactured 316L stainless steel.Firstly,the influence of the transient sound field and detection positions on Rayleigh wave signals are investigated.The interactions between the varied microdefects and the laser ultrasonic are studied.It is shown that arrival time of reflected Rayleigh(RR)waves wave is only related to the location of defects.The depth can be checked from the feature point Q,the displacement amplitude and time delay of converted transverse(RS)wave,while the width information can be evaluated from the RS wave time delay.With the aid of fitting curves,it is found to be linearly related.This simulation study provides a theoretical basis for quantitative detection of surface microdefects of additive manufactured 316L stainless steel components.
基金supported by the National Natural Science Foundation of China(No.52074157)Department of Education of Guangdong Province,China(No.2023KTSCX121)Shenzhen Science and Technology Programs,China(Nos.JSGG20210802154210032,JCYJ20210324104608023,JSGG20180508152608855)。
文摘A comparative study on the performance of gas atomized(GA)and rotating-disk atomized(RDA)aluminum alloy powders produced on industrial scale for laser directed energy deposition(L-DED)process was carried out.The powder characteristics,the printing process window,and the quality,microstructure,and mechanical properties of printed parts were taken into account for comparison and discussion.The results demonstrate that the RDA powder is superior to the GA powder in terms of sphericity,surface quality,internal defects,flowability,and apparent density,together with a larger printing process window during the L-DED parts fabrication.Besides,the resultant parts from the RDA powder have higher dimensional accuracy,lower internal defects,more uniform and finer microstructure,and more favorable mechanical properties than those from the GA powder.
基金support from the European Unioin’s Horizon Europe Research and Innovation Programme(Grant 101130639)Horizon Europe Marie Sklodowska‐Curie Actions(Grant 101109931)+1 种基金Introduced Intelligence Project from Northwestern Polytechnical University(Grant 20100‐W010103)the International Cooperation Project of Guangdong Province(Grant 2023A0505050145).
文摘In solid-state metal additive manufacturing,oxide removal is crucial for high-quality bonding,yet its impact remains unclear.In the article,the influence of oxides on the bonding interface by using cold spray additive manufacturing and leveraging diverse deformation mechanisms of high-entropy alloys is revealed.Advanced microstructure characterization shows that oxide fragmentation drives grain refinement via lattice rotation and substructure formation.As grain refinement progresses,oxide fragmentation increases metal-to-metal contact,promoting metallurgical bonding.These findings offer deep insights into the role of native oxides at the bonding interface and provide guidance for optimizing bond quality in solid-state additive manufacturing.
