GA-ELM混合算法预测齿轮坯终锻成形及预锻件优化  

GA-ELM Hybrid Algorithm to Predict Finish Forging and Optimization of Pre-forging for Gear Blank

作　　者：徐承亮[1,2] 曹志勇[3] 王大军[4] 胡吉全[1] 

机构地区：[1]武汉理工大学物流工程学院 [2]广州科技贸易职业学院 [3]湖北大学材料科学与工程学院 [4]重庆邮电大学自动化学院

出　　处：《机床与液压》2016年第11期88-93,共6页Machine Tool & Hydraulics

基　　金：国家自然科学基金面上项目(A020309)

摘　　要：多工位锻造齿轮坯的生产过程中,如何设计齿轮坯预锻件将直接影响到终锻件的金属流动、模具型腔的充满、锻件的质量以及模具的寿命。文中首先利用极限学习机ELM网络学习效率高、泛化能力强、预测精度高的特点,在预锻件尺寸、终锻成形力和终锻模具应力之间建立ELM网络模型,并使用遗传算法的全局寻优功能去优化ELM网络以便提高其预测精度和稳定性,这样得到的预锻件使终锻模具的受力大小合理,确定了特定条件下的最佳的预锻件形状和尺寸。In the course of multistage forging for producing gear blank,how to do pre-forging design of gear blank directly affects metal flowing patterns of the finish forging parts,the filling situation of the forging die cavity,the quality of the forging products and die life. Firstly,using characteristics of high learning efficiency and generalization ability and high prediction accuracy of the extreme learning machine( ELM) network,the ELM network model between the size parameters of pre-forging,finisher forming force and maximum die stress of finish forging was established. And then,by using genetic algorithm( GA) with the function of overall situation optimization in order to improve the prediction accuracy and stability,the reasonable size of finsher force of die was arrived for the obtained pre-forging parts. The optimal shape and size parameters of pre-forging parts are determined at specified conditions.

关 键 词：齿轮坯 预锻件优化设计 极限学习机ELM网络 GA遗传算法 

分 类 号：TG306[金属学及工艺—金属压力加工]