The solidification characteristics and microstructure evolution in grey cast iron were investigated through Jmat-Pro simulations and quenching performed during directional solidification. The phase transition sequence...The solidification characteristics and microstructure evolution in grey cast iron were investigated through Jmat-Pro simulations and quenching performed during directional solidification. The phase transition sequence of grey cast iron was determined as L → L + γ→ L + γ + G →γ + G → P(α + Fe_3C) + α + G. The graphite can be formed in three ways: directly nucleated from liquid through the eutectic reaction(L →γ + G), independently precipitated from the oversaturated γ phase(γ→γ + G), and produced via the eutectoid transformation(γ→ G + α). The area fraction and length of graphite as well as the primary dendrite spacing decrease with increasing cooling rate. Type-A graphite is formed at a low cooling rate, whereas a high cooling rate results in the precipitation of type-D graphite. After analyzing the graphite precipitation in the as-cast and transition regions separately solidified with and without inoculation, we concluded that, induced by the inoculant addition, the location of graphite precipitation changes from mainly the γ interdendritic region to the entire γ matrix. It suggests that inoculation mainly acts on graphite precipitation in the γ matrix, not in the liquid or at the solid–liquid front.展开更多
High-pressure die casting(HPDC)is one of the most popular mass production processes in the automotive industry owing to its capability for part consolidation.However,the nonuniform distribution of mechanical propertie...High-pressure die casting(HPDC)is one of the most popular mass production processes in the automotive industry owing to its capability for part consolidation.However,the nonuniform distribution of mechanical properties in large-sized HPDC products adds complexity to part property evaluation.Therefore,a methodology for property prediction must be developed.Material characterization,simulation technologies,and artificial intelligence(AI)algorithms were employed.Firstly,an image recognition technique was employed to construct a temperature-microstructure characteristic model for a typical HPDC Al7Si0.2Mg alloy.Moreover,a porosity/microstructure-mechanical property model was established using a machine learning method based on the finite element method and representative volume element model results.Additionally,the computational results of the casting simulation software were mapped with the porosity/microstructure-mechanical property model,allowing accurate prediction of the property distribution of the HPDC Al-Si alloy.The AI-enabled property distribution model developed in this study is expected to serve as a foundation for intelligent HPDC part design platforms in the automotive industry.展开更多
This research presents an innovative approach to accurately predict the nugget diameter in resistance spot welding(RSW)by leveraging machine learning and transfer learning methods.Initially,low-fidelity(LF)data were o...This research presents an innovative approach to accurately predict the nugget diameter in resistance spot welding(RSW)by leveraging machine learning and transfer learning methods.Initially,low-fidelity(LF)data were obtained through finite element numerical simulation and design of experiments(DOEs)to train the LF machine learning model.Subsequently,high-fidelity(HF)data were collected from RSW process experiments and used to fine-tune the LF model by transfer learning techniques.The accuracy and generalization performance of the models were thoroughly validated.The results demonstrated that combining different fidelity datasets and employing transfer learning could significantly improve the prediction accuracy while minimize the costs associated with experimental trials,and provide an effective and valuable method for predicting critical process parameters in RSW.展开更多
Background:Since the outbreak of coronavirus disease 2019(COVID-19),human mobility restriction measures have raised controversies,partly because of the inconsistent findings.An empirical study is promptly needed to re...Background:Since the outbreak of coronavirus disease 2019(COVID-19),human mobility restriction measures have raised controversies,partly because of the inconsistent findings.An empirical study is promptly needed to reliably assess the causal effects of the mobility restriction.The purpose of this study was to quantify the causal effects of human mobility restriction on the spread of COVID-19.Methods:Our study applied the difference-in-difference(DID)model to assess the declines of population mobility at the city level,and used the log-log regression model to examine the effects of population mobility declines on the disease spread measured by cumulative or new cases of COVID-19 over time after adjusting for confounders.Results:The DID model showed that a continual expansion of the relative declines over time in 2020.After 4 weeks,population mobility declined by-54.81%(interquartile range,-65.50%to-43.56%).The accrued population mobility declines were associated with the significant reduction of cumulative COVID-19 cases throughout 6 weeks(ie,1%decline of population mobility was associated with 0.72%[95%CI:0.50%-0.93%]reduction of cumulative cases for 1 week,1.42%2 weeks,1.69%3 weeks,1.72%4 weeks,1.64%5 weeks,and 1.52%6 weeks).The impact on the weekly new cases seemed greater in the first 4 weeks but faded thereafter.The effects on cumulative cases differed by cities of different population sizes,with greater effects seen in larger cities.Conclusions:Persistent population mobility restrictions are well deserved.Implementation of mobility restrictions in major cities with large population sizes may be even more important.展开更多
基金financially supported by the Fundamental Research Funds for the Central Universities,China(No.2020CDJDPT001)the Chongqing Natural Science Foundation,China(No.cstc2021jcyj-msxm X0699)。
基金The financial support provided by Ford Motor Company (University Research Program)
文摘The solidification characteristics and microstructure evolution in grey cast iron were investigated through Jmat-Pro simulations and quenching performed during directional solidification. The phase transition sequence of grey cast iron was determined as L → L + γ→ L + γ + G →γ + G → P(α + Fe_3C) + α + G. The graphite can be formed in three ways: directly nucleated from liquid through the eutectic reaction(L →γ + G), independently precipitated from the oversaturated γ phase(γ→γ + G), and produced via the eutectoid transformation(γ→ G + α). The area fraction and length of graphite as well as the primary dendrite spacing decrease with increasing cooling rate. Type-A graphite is formed at a low cooling rate, whereas a high cooling rate results in the precipitation of type-D graphite. After analyzing the graphite precipitation in the as-cast and transition regions separately solidified with and without inoculation, we concluded that, induced by the inoculant addition, the location of graphite precipitation changes from mainly the γ interdendritic region to the entire γ matrix. It suggests that inoculation mainly acts on graphite precipitation in the γ matrix, not in the liquid or at the solid–liquid front.
基金support from the National Natural Science Foundation of China(Grant Nos.51575068,51501023,and 52271019).
文摘High-pressure die casting(HPDC)is one of the most popular mass production processes in the automotive industry owing to its capability for part consolidation.However,the nonuniform distribution of mechanical properties in large-sized HPDC products adds complexity to part property evaluation.Therefore,a methodology for property prediction must be developed.Material characterization,simulation technologies,and artificial intelligence(AI)algorithms were employed.Firstly,an image recognition technique was employed to construct a temperature-microstructure characteristic model for a typical HPDC Al7Si0.2Mg alloy.Moreover,a porosity/microstructure-mechanical property model was established using a machine learning method based on the finite element method and representative volume element model results.Additionally,the computational results of the casting simulation software were mapped with the porosity/microstructure-mechanical property model,allowing accurate prediction of the property distribution of the HPDC Al-Si alloy.The AI-enabled property distribution model developed in this study is expected to serve as a foundation for intelligent HPDC part design platforms in the automotive industry.
基金founded by the Construction Project of the National Natural Science Foundation(Grant No.52205377)the National Key Research and Development Program(Grant No.2022YFB4601804)the Key Basic Research Project of Suzhou(Grant Nos.SJC2022029,SJC2022031).
文摘This research presents an innovative approach to accurately predict the nugget diameter in resistance spot welding(RSW)by leveraging machine learning and transfer learning methods.Initially,low-fidelity(LF)data were obtained through finite element numerical simulation and design of experiments(DOEs)to train the LF machine learning model.Subsequently,high-fidelity(HF)data were collected from RSW process experiments and used to fine-tune the LF model by transfer learning techniques.The accuracy and generalization performance of the models were thoroughly validated.The results demonstrated that combining different fidelity datasets and employing transfer learning could significantly improve the prediction accuracy while minimize the costs associated with experimental trials,and provide an effective and valuable method for predicting critical process parameters in RSW.
基金supported by the grants from the National Natural Science Foundation of China(Nos.71704122 and 71974138)National Science and Technology Major Project(No.2018ZX10302206)1·3·5 project for disciplines of excellence,West China Hospital,Sichuan University(No.ZYYC08003)。
文摘Background:Since the outbreak of coronavirus disease 2019(COVID-19),human mobility restriction measures have raised controversies,partly because of the inconsistent findings.An empirical study is promptly needed to reliably assess the causal effects of the mobility restriction.The purpose of this study was to quantify the causal effects of human mobility restriction on the spread of COVID-19.Methods:Our study applied the difference-in-difference(DID)model to assess the declines of population mobility at the city level,and used the log-log regression model to examine the effects of population mobility declines on the disease spread measured by cumulative or new cases of COVID-19 over time after adjusting for confounders.Results:The DID model showed that a continual expansion of the relative declines over time in 2020.After 4 weeks,population mobility declined by-54.81%(interquartile range,-65.50%to-43.56%).The accrued population mobility declines were associated with the significant reduction of cumulative COVID-19 cases throughout 6 weeks(ie,1%decline of population mobility was associated with 0.72%[95%CI:0.50%-0.93%]reduction of cumulative cases for 1 week,1.42%2 weeks,1.69%3 weeks,1.72%4 weeks,1.64%5 weeks,and 1.52%6 weeks).The impact on the weekly new cases seemed greater in the first 4 weeks but faded thereafter.The effects on cumulative cases differed by cities of different population sizes,with greater effects seen in larger cities.Conclusions:Persistent population mobility restrictions are well deserved.Implementation of mobility restrictions in major cities with large population sizes may be even more important.
