摘要
大锻件成形通常借助有限元模拟来进行研究,由于大锻件的尺寸大、工序长而导致有限元计算耗费了大量时间。因此,首先,采用有限元软件DEFORM对大锻件拔长过程进行模拟,获得成形数据,构建了19维的输入特征量和以应力、应变为输出特征量的数据集。然后,应用机器学习中的随机森林和神经网络方法对数据集进行学习,训练对应模型。最后,利用机器学习模型对一个新的拔长过程进行应力和应变分布预测,与有限元模拟结果对比后发现,这些预测结果与有限元模拟结果相近。研究表明,通过机器学习可以快速预测拔长成形结果,进而进一步分析成形质量,节省计算时间。
The forming of heary forgings is usually studied by means of finite element simulation,and because of large size and long working procedure for heavy forgings,the finite element calculation takes a lot of time. Therefore,firstly the drawing process of heavy forgings was simulated by finite element software DEFORM,and the forming data were obtained to construct a data set which consisted of nineteen dimensional input characteristic variables and output characteristic variables of stress and strain. Then,the data set was learned by random forest and neural network methods in machine learning,and the corresponding model was trained. Finally,the stress and strain distributions of a new drawing process was predicted by the machine learning model. Compared with the results of finite element simulation,the predicted results were similar to those of finite element simulation. The results show that the machine learning quickly predicts the result of drawing and analyzes the forming quality to save a lot of calculation time.
作者
张梓煜
曾攀
雷丽萍
Zhang Ziyu;Zeng Pan;Lei Liping(Department of Mechanical Engineering,Tsinghua University,Beijing 100084,China)
出处
《锻压技术》
CAS
CSCD
北大核心
2020年第10期209-216,共8页
Forging & Stamping Technology
基金
国家重点研发计划(2017YFB0701803)。
关键词
大锻件
拔长
机器学习
随机森林
神经网络
heavy forging
drawing
machine learning
random forest
neural network
作者简介
张梓煜(1995-),男,硕士研究生, E-mail:zy-zhang17@mails.tsinghua.edu.cn;通讯作者:雷丽萍(1968-),女,博士,副教授 ,E-mail:leilp@tsinghua.edu.cn。
