Since the introduction of Tesla's Giga-Casting process, the automotive industry has widely accepted the concept of super-sized structural components due to their significant potential for enhancing the light-weigh...Since the introduction of Tesla's Giga-Casting process, the automotive industry has widely accepted the concept of super-sized structural components due to their significant potential for enhancing the light-weighting of both electric and internal combustion engine vehicles.These super-sized components can be further lightened by using Mg alloys because of their exceptional lightweight characteristics, with a density only two-thirds that of aluminium alloys and one-fourth that of steel. This outstanding attribute offers the attractive prospect of achieving significant weight reduction without compromising structural integrity. This review examines studies on the Mg-alloy HighPressure Die Casting(HPDC) process, providing insights into the future prospects of incorporating Mg alloys into super-sized automotive HPDC components.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
基金the funding from the National Key R&D Program of China (No.2022YFB3709300 and No.2021YFB3701000)National Natural Science Foundation of China (No.U21A2048,No.52271090,and No.52101125)。
文摘Since the introduction of Tesla's Giga-Casting process, the automotive industry has widely accepted the concept of super-sized structural components due to their significant potential for enhancing the light-weighting of both electric and internal combustion engine vehicles.These super-sized components can be further lightened by using Mg alloys because of their exceptional lightweight characteristics, with a density only two-thirds that of aluminium alloys and one-fourth that of steel. This outstanding attribute offers the attractive prospect of achieving significant weight reduction without compromising structural integrity. This review examines studies on the Mg-alloy HighPressure Die Casting(HPDC) process, providing insights into the future prospects of incorporating Mg alloys into super-sized automotive HPDC components.
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.
