Larger and larger proportions of aluminium castings,especially those produced by the die casting process,can be observed during recent years in the automotive industry,house-hold articles and others.In case of the aut...Larger and larger proportions of aluminium castings,especially those produced by the die casting process,can be observed during recent years in the automotive industry,house-hold articles and others.In case of the automotive industry,apart from the traditional elements produced by the die pressure method such as engine blocks or crank shaft bedplates,aluminium is displacing steel from structural parts of cars('body in white').The current state and development directions of the structural solutions of cold-chamber die castings are analysed in this paper.These solutions drive the prospective development of these machines and die casting technology.The focus is mainly on essential functional systems such as:hydraulic drives of closing and locking units,as well as pressing in die machines of known companies present on the European market.展开更多
Silicon-based aluminum casting alloys are known to be one of the most widely used alloy systems mainly due to their superior casting characteristics and unique combination of mechanical and physical properties. Howeve...Silicon-based aluminum casting alloys are known to be one of the most widely used alloy systems mainly due to their superior casting characteristics and unique combination of mechanical and physical properties. However,manufacturing of thin-walled aluminum die-casting components,less than 1.0 mm in thickness,is generally known to be very difficult task to achieve aluminum casting alloys with high fluidity.Therefore,in this study,the optimal die-casting conditions for producing 297 mm×210 mm×0.7 mm thin-walled aluminum component was examined experimentally by using 2 different gating systems,tangential and split type,and vent design.Furthermore,computational solidification simulation was also conducted.The results showed that split type gating system was preferable gating design than tangential type gating system at the point of view of soundness of casting and distortion generated after solidification.It was also found that proper vent design was one of the most important factors for producing thin-wall casting components because it was important for the fulfillment of the thin-wall cavity and the minimization of the casting distortion.展开更多
Die casting machines, dies, die castings, peripheral equipments, die lubricants, raw materials for die casting, melting & holding furnaces, cleaning equipments, etc. were exhibited during the 4th China Internation...Die casting machines, dies, die castings, peripheral equipments, die lubricants, raw materials for die casting, melting & holding furnaces, cleaning equipments, etc. were exhibited during the 4th China International Die Casting Exhibition, which was surveyed in the paper.展开更多
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(R2 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
Die casting machines,which are the core equipment of the machinery manufacturing industry,consume great amounts of energy.The energy consumption prediction of die casting machines can support energy consumption quota,...Die casting machines,which are the core equipment of the machinery manufacturing industry,consume great amounts of energy.The energy consumption prediction of die casting machines can support energy consumption quota,process parameter energy-saving optimization,energy-saving design,and energy efficiency evaluation;thus,it is of great significance for Industry 4.0 and green manufacturing.Nevertheless,due to the uncertainty and complexity of the energy consumption in die casting machines,there is still a lack of an approach for energy consumption prediction that can provide support for process parameter optimization and product design taking energy efficiency into consideration.To fill this gap,this paper proposes an energy consumption prediction approach for die casting machines driven by product parameters.Firstly,the system boundary of energy consumption prediction is defined,and subsequently,based on the energy consumption characteristics analysis,a theoretical energy consumption model is established.Consequently,a systematic energy consumption prediction approach for die casting machines,involving product,die,equipment,and process parameters,is proposed.Finally,the feasibility and reliability of the proposed energy consumption prediction approach are verified with the help of three die casting machines and six types of products.The results show that the prediction accuracy of production time and energy consumption reached 91.64%and 85.55%,respectively.Overall,the proposed approach can be used for the energy consumption prediction of different die casting machines with different products.展开更多
文摘Larger and larger proportions of aluminium castings,especially those produced by the die casting process,can be observed during recent years in the automotive industry,house-hold articles and others.In case of the automotive industry,apart from the traditional elements produced by the die pressure method such as engine blocks or crank shaft bedplates,aluminium is displacing steel from structural parts of cars('body in white').The current state and development directions of the structural solutions of cold-chamber die castings are analysed in this paper.These solutions drive the prospective development of these machines and die casting technology.The focus is mainly on essential functional systems such as:hydraulic drives of closing and locking units,as well as pressing in die machines of known companies present on the European market.
基金Acknowledgement This work was supported by Korea Institute of Industrial Technology and Gwangju Metropolitan City through ＂The Advanced Materials and Components Industry Development Program＂.
文摘Silicon-based aluminum casting alloys are known to be one of the most widely used alloy systems mainly due to their superior casting characteristics and unique combination of mechanical and physical properties. However,manufacturing of thin-walled aluminum die-casting components,less than 1.0 mm in thickness,is generally known to be very difficult task to achieve aluminum casting alloys with high fluidity.Therefore,in this study,the optimal die-casting conditions for producing 297 mm×210 mm×0.7 mm thin-walled aluminum component was examined experimentally by using 2 different gating systems,tangential and split type,and vent design.Furthermore,computational solidification simulation was also conducted.The results showed that split type gating system was preferable gating design than tangential type gating system at the point of view of soundness of casting and distortion generated after solidification.It was also found that proper vent design was one of the most important factors for producing thin-wall casting components because it was important for the fulfillment of the thin-wall cavity and the minimization of the casting distortion.
文摘Die casting machines, dies, die castings, peripheral equipments, die lubricants, raw materials for die casting, melting & holding furnaces, cleaning equipments, etc. were exhibited during the 4th China International Die Casting Exhibition, which was surveyed in the paper.
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(R2 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This work was supported by the National Natural Science Foundation of China(Grant No.51805066)the Natural Science Foundation of Chongqing,China(Grant No.cstc2018jcyjAX0579)。
文摘Die casting machines,which are the core equipment of the machinery manufacturing industry,consume great amounts of energy.The energy consumption prediction of die casting machines can support energy consumption quota,process parameter energy-saving optimization,energy-saving design,and energy efficiency evaluation;thus,it is of great significance for Industry 4.0 and green manufacturing.Nevertheless,due to the uncertainty and complexity of the energy consumption in die casting machines,there is still a lack of an approach for energy consumption prediction that can provide support for process parameter optimization and product design taking energy efficiency into consideration.To fill this gap,this paper proposes an energy consumption prediction approach for die casting machines driven by product parameters.Firstly,the system boundary of energy consumption prediction is defined,and subsequently,based on the energy consumption characteristics analysis,a theoretical energy consumption model is established.Consequently,a systematic energy consumption prediction approach for die casting machines,involving product,die,equipment,and process parameters,is proposed.Finally,the feasibility and reliability of the proposed energy consumption prediction approach are verified with the help of three die casting machines and six types of products.The results show that the prediction accuracy of production time and energy consumption reached 91.64%and 85.55%,respectively.Overall,the proposed approach can be used for the energy consumption prediction of different die casting machines with different products.
