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
A thermodynamic assessment of the Al-Fe-Mn-Si quaternary system and its subsystems was performed by the Calphad method. First, the Al-Fe-Si ternary description was deeply revised by considering the most recent experim...A thermodynamic assessment of the Al-Fe-Mn-Si quaternary system and its subsystems was performed by the Calphad method. First, the Al-Fe-Si ternary description was deeply revised by considering the most recent experimental investigations and employing new models to ternary compounds. Significant improvements were made on the calculated liquidus projection over the entire compositional range, especially in the Al-rich corner. The Al-Mn-Si system was refined in the Al-rich region by adopting new models for the two ternary compounds, a-AlMnSi and β-AlMnSi. The extended solubility of the a-AlMnSi phase into the Al-Fe-Mn-Si quaternary system was modeled to reproduce the phase equilibria in the Al-rich region. Special cares were taken in order to prevent a-AlMnSi from becoming stable in the Al-Fe-Si ternary system. The obtained thermodynamic descriptions were then implemented into the TCAL database, and extensively validated with phase equilibrium calculations and solidification simulations against experimental data/information from commercial aluminum alloys. The updated TCAL database can reliably predict the phase formation in Al-Fe-Si- and Al-Fe-Mn-Si-based aluminum alloys.展开更多
As potential cast and wrought Mg alloys,Mg-X(X=Al,Zn,Sn)based alloys have attracted great interest.This work is to develop a dataset of atomic mobilities that is valid over a wide composition range.With the obtained m...As potential cast and wrought Mg alloys,Mg-X(X=Al,Zn,Sn)based alloys have attracted great interest.This work is to develop a dataset of atomic mobilities that is valid over a wide composition range.With the obtained mobilities,and a compatible thermodynamic description,as well as thermophysical parameters,simulations are performed to predict the characteristics of precipitation evolution.Examples are presented for the isothermal aging processes in Mg-x wt.%Al(x=5.9,6,8.8,9),Mg-x wt.%Zn(x=6,6.2,8,8.65),Mg-x wt.%Sn(x=6.04,6.92,8.64)alloys.The simulated size distribution,number density and volume fraction of precipitates reasonably account for the experimental results and provide additional information for further alloy composition design and heat treatment optimization.展开更多
Melting properties are critical for designing novel materials,especially for discovering highperformance,high-melting refractory materials.Experimental measurements of these properties are extremely challenging due to...Melting properties are critical for designing novel materials,especially for discovering highperformance,high-melting refractory materials.Experimental measurements of these properties are extremely challenging due to their high melting temperatures.Complementary theoretical predictions are,therefore,indispensable.One of the most accurate approaches for this purpose is the ab initio free-energy approach based on density functional theory(DFT).However,it generally involves expensive thermodynamic integration using ab initio molecular dynamic simulations.The high computational cost makes high-throughput calculations infeasible.Here,we propose a highly efficient DFT-based method aided by a specially designed machine learning potential.As the machine learning potential can closely reproduce the ab initio phase-space distribution,even for multi-component alloys,the costly thermodynamic integration can be fully substituted with more efficient free energy perturbation calculations.The method achieves overall savings of computational resources by 80%compared to current alternatives.We apply the method to the high-entropy alloy TaVCrW and calculate its melting properties,including the melting temperature,entropy and enthalpy of fusion,and volume change at the melting point.Additionally,the heat capacities of solid and liquid TaVCrW are calculated.The results agree reasonably with the CALPHAD extrapolated values.展开更多
In this work,eight Mn-RE(RE=Ce,Pr,Sm,Tb,Er,Tm,Lu,and Y)binary systems were reassessed thermodynamically by the CALPHAD method based on the reported optimizations and experimental information.Self-consistent thermodyna...In this work,eight Mn-RE(RE=Ce,Pr,Sm,Tb,Er,Tm,Lu,and Y)binary systems were reassessed thermodynamically by the CALPHAD method based on the reported optimizations and experimental information.Self-consistent thermodynamic parameters to describe Gibbs energies of various phases in eight Mn-RE binary systems were obtained.The calculated phase equilibria and thermodynamic properties of eight Mn-RE binary systems are in good accor-dance with the experimental results.Furthermore,phase equilibria and ther-modynamic properties of 13 Mn-RE(RE=La,Ce,Pr,Nd,Sm,Gd,Tb,Dy,Ho,Er,Tm,Lu,and Y)binary systems were discussed systematically in combination with the present calculations and the reported optimizations.A trend was found for the variation of phase equilibria and thermodynamic properties of the Mn-RE binary systems.In general,as the RE atomic number increases,the enthalpies of mixing of liquid alloys as well as the enthalpies of formation of the intermetallic compounds become increasingly negative,and the formation temperatures of the intermetallic compounds become higher.The results provide a complete set of self-consistent thermodynamic parameters for the Mn-RE binary systems,and a thermodynamic database of 13 Mn-RE binary systems was finally achieved.展开更多
Magnesium alloys,known for their lightweight advantages,are increasingly in demand across a range of applications,from aerospace to the automotive industry.With rising requirements for strength and corrosion resistanc...Magnesium alloys,known for their lightweight advantages,are increasingly in demand across a range of applications,from aerospace to the automotive industry.With rising requirements for strength and corrosion resistance,the development of new magnesium alloy systems has become critical.Phase diagrams play a crucial role in guiding the magnesium alloy design by providing key insights into phase stability,composition,and temperature ranges,enabling the optimization of alloy properties and processing conditions.However,accessing and interpreting phase diagram data with thermodynamic calculation software can be complex and time-consuming,often requiring intricate calculations and iterative refinement based on thermodynamic models.To address this challenge,we introduce PDGPT,a ChatGPT-based large language model designed to streamline the acquisition of magnesium alloys Phase Diagram information with high efficiency and accuracy.Enhanced by promptengineering,supervised fine-tuning and retrieval-augmented generation,PDGPT leverages the predictive and reasoning capabilities of large language models along with computational phase diagram data.By combining large language models with traditional phase diagram research tools,PDGPT not only improves the accessibility of critical phase diagram information but also sets the stage for future advancements in applying large language models to materials science.展开更多
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A thermodynamic assessment of the Al-Fe-Mn-Si quaternary system and its subsystems was performed by the Calphad method. First, the Al-Fe-Si ternary description was deeply revised by considering the most recent experimental investigations and employing new models to ternary compounds. Significant improvements were made on the calculated liquidus projection over the entire compositional range, especially in the Al-rich corner. The Al-Mn-Si system was refined in the Al-rich region by adopting new models for the two ternary compounds, a-AlMnSi and β-AlMnSi. The extended solubility of the a-AlMnSi phase into the Al-Fe-Mn-Si quaternary system was modeled to reproduce the phase equilibria in the Al-rich region. Special cares were taken in order to prevent a-AlMnSi from becoming stable in the Al-Fe-Si ternary system. The obtained thermodynamic descriptions were then implemented into the TCAL database, and extensively validated with phase equilibrium calculations and solidification simulations against experimental data/information from commercial aluminum alloys. The updated TCAL database can reliably predict the phase formation in Al-Fe-Si- and Al-Fe-Mn-Si-based aluminum alloys.
基金financially supported by the National Key Research and Development Program of China(No.2016YFB0701202)the Innovation Foundation for Postgraduate and Fundamental Research Funds of Central South University(No.1053320182102)China Scholarship Council(No.201906370116)for the award of a fellowship and funding。
文摘As potential cast and wrought Mg alloys,Mg-X(X=Al,Zn,Sn)based alloys have attracted great interest.This work is to develop a dataset of atomic mobilities that is valid over a wide composition range.With the obtained mobilities,and a compatible thermodynamic description,as well as thermophysical parameters,simulations are performed to predict the characteristics of precipitation evolution.Examples are presented for the isothermal aging processes in Mg-x wt.%Al(x=5.9,6,8.8,9),Mg-x wt.%Zn(x=6,6.2,8,8.65),Mg-x wt.%Sn(x=6.04,6.92,8.64)alloys.The simulated size distribution,number density and volume fraction of precipitates reasonably account for the experimental results and provide additional information for further alloy composition design and heat treatment optimization.
基金funding by the Deutsche Forschungsgemeinschaft(DFG,493417040)funding from the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation program(grant agreement No.865855)F.K.acknowledges the LRP and MC simulation packages by Alexander Shapeev.
文摘Melting properties are critical for designing novel materials,especially for discovering highperformance,high-melting refractory materials.Experimental measurements of these properties are extremely challenging due to their high melting temperatures.Complementary theoretical predictions are,therefore,indispensable.One of the most accurate approaches for this purpose is the ab initio free-energy approach based on density functional theory(DFT).However,it generally involves expensive thermodynamic integration using ab initio molecular dynamic simulations.The high computational cost makes high-throughput calculations infeasible.Here,we propose a highly efficient DFT-based method aided by a specially designed machine learning potential.As the machine learning potential can closely reproduce the ab initio phase-space distribution,even for multi-component alloys,the costly thermodynamic integration can be fully substituted with more efficient free energy perturbation calculations.The method achieves overall savings of computational resources by 80%compared to current alternatives.We apply the method to the high-entropy alloy TaVCrW and calculate its melting properties,including the melting temperature,entropy and enthalpy of fusion,and volume change at the melting point.Additionally,the heat capacities of solid and liquid TaVCrW are calculated.The results agree reasonably with the CALPHAD extrapolated values.
基金supported financially by Guangxi Natural Science Foundation(2020GXNSFFA297004)National Natural Science Foundation of China(51971069,51461013,51761008)+1 种基金Guangxi Key Laboratory of Information Materials(211007-Z)Engineering Research Center of Electronic Information Materials and Devices(EIMDAA202004)。
文摘In this work,eight Mn-RE(RE=Ce,Pr,Sm,Tb,Er,Tm,Lu,and Y)binary systems were reassessed thermodynamically by the CALPHAD method based on the reported optimizations and experimental information.Self-consistent thermodynamic parameters to describe Gibbs energies of various phases in eight Mn-RE binary systems were obtained.The calculated phase equilibria and thermodynamic properties of eight Mn-RE binary systems are in good accor-dance with the experimental results.Furthermore,phase equilibria and ther-modynamic properties of 13 Mn-RE(RE=La,Ce,Pr,Nd,Sm,Gd,Tb,Dy,Ho,Er,Tm,Lu,and Y)binary systems were discussed systematically in combination with the present calculations and the reported optimizations.A trend was found for the variation of phase equilibria and thermodynamic properties of the Mn-RE binary systems.In general,as the RE atomic number increases,the enthalpies of mixing of liquid alloys as well as the enthalpies of formation of the intermetallic compounds become increasingly negative,and the formation temperatures of the intermetallic compounds become higher.The results provide a complete set of self-consistent thermodynamic parameters for the Mn-RE binary systems,and a thermodynamic database of 13 Mn-RE binary systems was finally achieved.
基金the financial support provided by the National Natural Science Foundation of China(Grant Nos.52425101,52401216,52471012)Hongbin Zhang acknowledges also the funding by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)-Project-ID 405553726-TRR 270.
文摘Magnesium alloys,known for their lightweight advantages,are increasingly in demand across a range of applications,from aerospace to the automotive industry.With rising requirements for strength and corrosion resistance,the development of new magnesium alloy systems has become critical.Phase diagrams play a crucial role in guiding the magnesium alloy design by providing key insights into phase stability,composition,and temperature ranges,enabling the optimization of alloy properties and processing conditions.However,accessing and interpreting phase diagram data with thermodynamic calculation software can be complex and time-consuming,often requiring intricate calculations and iterative refinement based on thermodynamic models.To address this challenge,we introduce PDGPT,a ChatGPT-based large language model designed to streamline the acquisition of magnesium alloys Phase Diagram information with high efficiency and accuracy.Enhanced by promptengineering,supervised fine-tuning and retrieval-augmented generation,PDGPT leverages the predictive and reasoning capabilities of large language models along with computational phase diagram data.By combining large language models with traditional phase diagram research tools,PDGPT not only improves the accessibility of critical phase diagram information but also sets the stage for future advancements in applying large language models to materials science.
