摘要
为解决低压铸造铝合金车轮质量控制难度大的问题,采用遗传算法对工艺参数进行优化.基于铸造数值模拟结果,利用BP人工神经网络建立了铸造工艺参数与质量控制目标缩松缺陷和凝固时间的非线性关系,采用遗传算法实现了铸造工艺参数的优化.以某型低压铸造A356铝合金车轮为例,对浇注温度、上模温度、下模温度、侧模温度、模芯温度5个参数进行优化,得到的最佳工艺组合,可有效控制缩松缺陷和凝固时间.利用数值模拟结果、建立神经网络模型,采用遗传算法优化的方法,获得近似最优解,有助于优化低压铸造工艺.
In order to find a solution to the casting quality control of low-pressure die casting aluminum alloy wheel,genetic algorithm is applied to the optimization of process parameters. Based on casting simulation results,the BP network is employed to build up the nonlinear mapping relationship between process parameters and control objectives,and then the optimization of parameters is realized by using genetic algorithm. A lowpressure die casting A356 aluminum alloy wheel is studied as an instance,and the parameters such as casting temperature,upper die temperature,bottom die temperature,mold core temperature and so on are optimized. Results show that this approach is effective to optimize the process parameters and control the porosity defect and solidification time,which can improve the casting process.
出处
《材料科学与工艺》
EI
CAS
CSCD
北大核心
2010年第2期233-237,共5页
Materials Science and Technology
关键词
铸造模拟
人工神经网络
遗传算法
铝合金车轮
工艺优化
casting simulation
artificial neural network
genetic algorithm
A356 aluminum alloy wheel
process optimization
