Al-Si alloys manufactured via high-pressure die casting(HPDC)are suitable for a wide range of applications.However,the heterogeneous microstructure and unpredictable pore distribution of Al-Si high-pressure die castin...Al-Si alloys manufactured via high-pressure die casting(HPDC)are suitable for a wide range of applications.However,the heterogeneous microstructure and unpredictable pore distribution of Al-Si high-pressure die castings result in significant variations in the mechanical properties,thus leading to a complicated microstructure-property relationship that is difficult to capture.Hence,a computational framework incorporating machine learning and crystal plasticity method is proposed.This framework aims to provide a systematic and comprehensive understanding of this relationship and enable the rapid prediction of macroscopic mechanical properties based on the microstructure.Firstly,we select eight variables that can effectively characterize the microstructural features and then obtain their statistical information.Subsequently,based on 160 samples obtained via the Latin hypercube sampling method,representative volume elements are constructed,and the crystal plasticity fast Fourier transformation method is executed to obtain the macroscopic mechanical properties.Next,the yield strength,elastic modulus,strength coefficient,and strain-hardening exponent are used to characterize the stress-strain curve,and Gaussian process regression models and microstructural variables are developed.Finally,sensitivity and univariate analyses based on these machine-learning models are performed to obtain insights into the microstructure-property relationships of the HPDC Al-Si alloy.The results show that the Gaussian process regression models exhibit high accuracy(R^(2) greater than 0.84),thus confirming the viability of the proposed method.The results of sensitivity analysis indicate that the pore size exerts the most significant effect on the mechanical properties.Furthermore,the proposed framework can not only be transferred to other alloys but also be employed for material design.展开更多
The copper disc casting machine is core equipment for producing copper anode plates in the copper metallurgy industry.The copper disc casting machine casting package motion curve(CPMC) is significant for precise casti...The copper disc casting machine is core equipment for producing copper anode plates in the copper metallurgy industry.The copper disc casting machine casting package motion curve(CPMC) is significant for precise casting and efficient production.However,the lack of exact casting modeling and real-time simulation information severely restricts dynamic CPMC optimization.To this end,a liquid copper droplet model describes the casting package copper flow pattern in the casting process.Furthermore,a CPMC optimization model is proposed for the first time.On top of this,a digital twin dual closed-loop self-optimization application framework(DT-DCS) is constructed for optimizing the copper disc casting process to achieve self-optimization of the CPMC and closed-loop feedback of manufacturing information during the casting process.Finally,a case study is carried out based on the proposed methods in the industrial field.展开更多
基金support from the National Natural Science Foundation of China(Grant No.52375256)the Natural Science Foundation of Shanghai(Grant Nos.21ZR1431500,23ZR1431600).
文摘Al-Si alloys manufactured via high-pressure die casting(HPDC)are suitable for a wide range of applications.However,the heterogeneous microstructure and unpredictable pore distribution of Al-Si high-pressure die castings result in significant variations in the mechanical properties,thus leading to a complicated microstructure-property relationship that is difficult to capture.Hence,a computational framework incorporating machine learning and crystal plasticity method is proposed.This framework aims to provide a systematic and comprehensive understanding of this relationship and enable the rapid prediction of macroscopic mechanical properties based on the microstructure.Firstly,we select eight variables that can effectively characterize the microstructural features and then obtain their statistical information.Subsequently,based on 160 samples obtained via the Latin hypercube sampling method,representative volume elements are constructed,and the crystal plasticity fast Fourier transformation method is executed to obtain the macroscopic mechanical properties.Next,the yield strength,elastic modulus,strength coefficient,and strain-hardening exponent are used to characterize the stress-strain curve,and Gaussian process regression models and microstructural variables are developed.Finally,sensitivity and univariate analyses based on these machine-learning models are performed to obtain insights into the microstructure-property relationships of the HPDC Al-Si alloy.The results show that the Gaussian process regression models exhibit high accuracy(R^(2) greater than 0.84),thus confirming the viability of the proposed method.The results of sensitivity analysis indicate that the pore size exerts the most significant effect on the mechanical properties.Furthermore,the proposed framework can not only be transferred to other alloys but also be employed for material design.
基金supported in part by the National Major Scientific Research Equipment of China (61927803)the National Natural Science Foundation of China Basic Science Center Project (61988101)+1 种基金Science and Technology Innovation Program of Hunan Province (2021RC4054)the China Postdoctoral Science Foundation (2021M691681)。
文摘The copper disc casting machine is core equipment for producing copper anode plates in the copper metallurgy industry.The copper disc casting machine casting package motion curve(CPMC) is significant for precise casting and efficient production.However,the lack of exact casting modeling and real-time simulation information severely restricts dynamic CPMC optimization.To this end,a liquid copper droplet model describes the casting package copper flow pattern in the casting process.Furthermore,a CPMC optimization model is proposed for the first time.On top of this,a digital twin dual closed-loop self-optimization application framework(DT-DCS) is constructed for optimizing the copper disc casting process to achieve self-optimization of the CPMC and closed-loop feedback of manufacturing information during the casting process.Finally,a case study is carried out based on the proposed methods in the industrial field.
