基于RBF-AG算法的7050铝合金电磁半连铸参数优化  

Parameter optimization for semi-continuously casting of 7050 Al alloy under low frequency electromagnetic based on RBF-AG algorithm

作　　者：黄松林[1,2] 崔建忠[1] 

机构地区：[1]东北大学材料电磁过程研究教育部重点实验室,沈阳110004 [2]沈阳大学机械工程学院,沈阳110044

出　　处：《中国有色金属学报》2008年第12期2151-2157,共7页The Chinese Journal of Nonferrous Metals

基　　金：国家重点基础研究发展计划资助项目(2005CB623707)

摘　　要：为解决7050铝合金大尺寸扁锭成型裂纹倾向大、工艺参数不易找准的问题,建立基于RBF的电磁半连续铸造神经网络模型,并采用遗传算法对7050铝合金电磁半连续铸造过程的工艺参数进行了优化计算。结果表明,当7050铝合金的成分(质量分数)为Zn6.1%、Mg2.3%、Cu2.2%和Zr0.14%时,电磁半连铸工艺参数的优化值为:铸造速度52mm/min、铸造温度724℃、扁锭宽面冷却强度134L/min、扁锭窄面冷却强度22L/min、电磁强度11749A·turn、电磁频率27Hz。在优化后的工艺参数条件下,无裂纹铸锭成品率比优化前的成品率提高20%。To solve 7050 Al alloy ingot large crack tendency, a parameter optimization model was developed. The optimization model was based on RBF artificial neural network, the technics parameter was optimized by genetic algorithm. The simulation results show that when the 7050 alloy chemical composition（mass fraction） is Zn 6. 1%, Mg 2.3%, Cu 2.2% and Zr 0.14%, the optimizing value of cast velocity is 52 mm/min, the cast temperature is 724 ℃, the broarside cooling intensity is 134 L/min, the narrow-side cooling intensity is 22 L/min, the electromagnetic intensity is 11 749 AZ, the electromagnetic frequency is 27 Hz. Compared with the product without parameters optimized, the product rate is increased by 20%.

关 键 词：RBF神经网络 遗传算法 参数优化 电磁半连续铸造 

分 类 号：TG146.2[一般工业技术—材料科学与工程]