外星轮热挤压预成形多目标优化设计  

Intelligent optimization design of hot extrusion process for besides-star wheel

作　　者：李路[1] 于文斌[1] 王放[1] 

机构地区：[1]西南大学材料与能源学部,重庆400715

出　　处：《机械设计》2014年第5期42-46,共5页Journal of Machine Design

基　　金：国家自然科学基金青年基金资助项目(11102169);重庆市自然科学基金资助项目(cstc2012jjA70002);中央高校基本科研业务费专项资金资助项目(XDJK2011C070)

摘　　要：针对外星轮热挤压成形性能波动,模具杆部过渡圆角区磨损严重的问题,对关键模具结构和挤压工艺参数开展优化研究。基于有限元数值模拟仿真,结合析因试验分析首先得到影响磨损和填充的关键参数及其影响方向。随后对核心参数构造代理模型替代有限元机理模型,用于磨损和填充性能的数值预测。并使用线性加权和法将所得近似模型转化为单目标函数,耦合遗传算法进行全局寻优。计算发现,当入口斜度y=30°,根部圆角a=18.6 mm,杆部圆角b=5 mm,模具硬度HRC60,摩擦因数m=0.1,挤压速度v=10 mm/s,模具预热温度T=300℃,坯料初始温度t=1 180℃时,在保证锻件填充完全的前提下可获得模具最大磨损量的显著降低,模具寿命较初始提高约2 100件。最后通过仿真和生产试验验证了优化结果的正确性。A multi-objective optimization design of structural and process parameters is proposed to improve the filling capacity and tool wear for the besides-star wheel extrusion process. The fractional factorial experimental design and FEM are used to evaluate main effects of the structural and process parameters on filling capacity and the maximum tool wear. The Kriging meta-models on the filling capacity and wear are set up to numerical prediction in place of FEM. Models are then converted into single objective function by linear weighting method. The optimum parameters are obtained by using the genetic algorithm for global optimization. It has been found through calculation that when inlet inclination y= 30,root corner a=18.6 mm, rod corner b=5 mm, die rigidity H=60 HRC,friction factor m=0.1, extruding speed v=10 mm/s,die temperature T =300 ℃ and blank temperature t =1180 ℃,under the premise in forging filling, the wear amount of die can be significantly reduced, the die life increased 2100 parts. Finally, the research object got production test verifying.

关 键 词：参数优化 部分析因设计 Kriging函数 遗传算法 

分 类 号：TG315.2[金属学及工艺—金属压力加工]