基于BP 神经网络的汽车镁合金轮毂低压铸造工艺优化  

Optimization of Low Pressure Casting Process for Automobile Mg AlloyWheel Hub Based on BP Neural Network

作　　者：程诚 宁萍[2] 李玲玲 CHENG Cheng;NING Ping;LI Lingling(Chongqing Creation Vocational College,Chongqing 402160,China;School of Shipping&Ship Engineering,Chongqing Jiaotong University,Chongqing 400060,China;School of Materials Science and Engineering,Chongqing University,Chongqing 400045,China)

机构地区：[1]重庆科创职业学院,重庆402160 [2]重庆交通大学船运与船舶工程学院,重庆400060 [3]重庆大学材料科学与工程学院,重庆400045

出　　处：《热加工工艺》2023年第7期77-80,共4页Hot Working Technology

基　　金：重庆市教委科学技术研究项目(KJQN201905402)。

摘　　要：基于BP神经网络建立的4×24×12×2四层结构汽车镁合金轮毂低压铸造优化模型,输入层为铸造模具预热温度、浇注温度、浇注速度及充型压力,输出层为晶粒尺寸、屈服强度。通过BP神经网络优化模型对汽车镁合金轮毂低压铸造成型工艺进行优化。结果表明:优化后的汽车镁合金轮毂铸件晶粒尺寸由146.8μm减小到123.2μm,晶粒尺寸减小率为16.0%;屈服强度由169.7 MPa提升到185.6 MPa,屈服强度提升率为9.4%。汽车镁合金轮毂低压铸造最优工艺参数为模具预热温度380℃、浇注温度690℃、浇注速度0.50 m/s、充型压力6.5 kPa。Based on the BP neural network,the 4×24×12×2 four-layer structure automobile magnesium alloy wheel hubs low-pressure casting optimization model was established,the input layer was the casting mold preheating temperature,pouring temperature,pouring speed and filling pressure,and the output layer was the grain size and yield strength.The low-pressure casting molding process of automobile magnesium alloy wheel hubs was optimized through the BP neural network optimization model.The results show that the grain size of the optimized automobile magnesium alloy wheel hub castings is reduced from 146.8μm to 123.2μm,the grain size reduction rate is 16.0%,the yield strength is increased from 169.7 MPa to 185.6 MPa,and the yield strength improvement rate is 9.4%.The optimal process parameters for low-pressure casting of automobile magnesium alloy wheel hubs are casting mold preheating temperature of 380℃,pouring temperature of 690℃,pouring speed of 0.50 m/s and filling pressure of 6.5 kPa.

关 键 词：BP神经网络 镁合金轮毂 低压铸造 工艺优化 

分 类 号：TG292[金属学及工艺—铸造]