摘要
为提升汽车轻量化铝合金门板的压铸工艺性能,提出针对铝合金门板模具压铸工艺参数的优化方法。选取ADC12铝合金材料制备门板,工艺流程包括模具预热、合模、开模等步骤,并设计了浇注系统,确定内浇口填充速度等关键参数,同时选用DM1500卧式冷室压铸机。以最小孔隙率和最高致密度为参数优化目标,通过分析压射速度、模具温度、浇注温度、保压压力对门板铸件缺陷及孔隙率的影响,确定最优参数组合。实验结果表明,最优参数为压射速度为2.2 m/s、模具温度为260℃、浇注温度为750℃、保压压力为60 MPa。在此参数下,汽车轻量化铝合金门板铸件的孔洞、气孔以及缩孔缩松面积达到最小,孔隙率最低,致密度最高。这表明,采用优化后的压铸工艺参数制备的汽车轻量化铝合金门板铸件质量较高,具有较高的可靠性,适用于实际生产。
To improve the die-casting process performance of lightweight aluminum alloy door panels for automobiles,an optimization method for die-casting process parameters of aluminum alloy door panel molds is proposed.Selecting ADC12 aluminum alloy material to prepare door panels,the process includes steps such as mold preheating,mold closing,and mold opening.A pouring system is designed to determine key parameters such as the filling speed of the sprue,and a DM1500 horizontal cold chamber die-casting machine is selected.By optimizing the parameters of minimum porosity and maximum density,the optimal parameter combination is determined by analyzing the effects of injection speed,mold temperature,pouring temperature,and holding pressure on the defects and porosity of door panel castings.The experimental results show that the optimal parameters are injection velocity of 2.2 m/s,mold temperature of 260℃,pouring temperature of 750℃,and holding pressure of 60 MPa.Under these parameters,the porosity,porosity,and shrinkage area of lightweight aluminum alloy door panel castings for automobiles are minimized,with the lowest porosity and highest density.This indicates that the automotive lightweight aluminum alloy door panel castings prepared using optimized die-casting process parameters have high quality and reliability,and are suitable for practical production.
作者
刘家麟
LIU Jialin(Sichuan Institute of Industrial Technology,Deyang 618000,Sichuan,China)
出处
《模具技术》
2025年第6期79-86,共8页
Die and Mould Technology
基金
德阳市新能源专用车辆工程技术研究中心2024年度科研项目:铝合金新材料及新工艺在新能源汽车轻量化上的研究与应用(编号:XNYCL2403)。
关键词
汽车轻量化
铝合金
门板模具
压铸工艺
参数优化
压射速度
automotive lightweighting
aluminium alloy
door panel mold
die casting process
parameter optimization
injection velocity
