基于MOGA的拉拔机主机框架轻量化设计  

Lightweight design on drawing machine host frame based on MOGA

作　　者：李昆 王宗彦[1,2] 李梦龙 李飞 鲍东辉 高沛 Li Kun;Wang Zongyan;Li Menglong;Li Fei;Bao Donghui;Gao Pei(School of Mechanical Engineering,North University of China,Taiyuan 030051,China;Shanxi Provincial Key Laboratory Digital Design and Manufacturing,North University of China,Taiyuan 030051,China;Shanxi Limin Industry Co.,Ltd.,Jinzhong 030812,China;Department of Mechanical and Electrical,Shanxi Polytechnic College,Taiyuan 030006,China)

机构地区：[1]中北大学机械工程学院,山西太原030051 [2]中北大学数字化设计与制造山西省重点实验室,山西太原030051 [3]山西利民工业有限责任公司,山西晋中030812 [4]山西职业技术学院机电工程系,山西太原030006

出　　处：《锻压技术》2023年第11期151-158,共8页Forging & Stamping Technology

基　　金：山西省重点国际科技合作项目(201903D421015);国家科技部创新方法专项(2020IM020700)。

摘　　要：为了解决某型1200 t液压拉拔机主机框架材料成本高的问题,以拉拔机主机框架为研究对象,根据其结构特点,运用多目标遗传算法(MOGA)对其进行轻量化设计。利用Workbench对主机框架整体进行静力学结构分析,分析主机框架的最大等效应力。基于以上分析,在满足主机框架强度的情况下,对关键设计尺寸进行优化取值,选择一组最优设计点,并对优化后的主机框架重新进行静力学分析。结果表明:优化后的主机框架最大等效应力为191.86 MPa,可以满足实际使用工况要求,质量减少了39.6%,有效地减少了主机框架质量、降低了材料成本。In order to solve the problem of high material cost of host frame for a certain type of 1200 t hydraulic drawing machine,for the host frame of drawing machine,according to its structural characteristics,the lightweight design was conducted by the multi-objective genetic algorithm(MOGA).Then,the static structural analysis on the entire host frame was carried out by Workbench,and the maximum equivalent stress of the host frame was analyzed.Furthermore,based on the above analysis,the values of key design dimensions were optimized under the condition that the strength of the host frame was satisfied,a set of optimal design points were selected,and the static analysis of the optimized host frame was re-carried out.The results show that the maximum equivalent stress of the optimized host frame is 191.86 MPa,which can meet the requirements of the actual working conditions,and the mass is reduced by 39.6%,which effectively reduces the mass of the host frame and the material costs.

关 键 词：液压拉拔机 主机框架 多目标遗传算法 最大等效应力 轻量化 

分 类 号：TP202.7[自动化与计算机技术—检测技术与自动化装置] TP203[自动化与计算机技术—控制科学与工程] TH164[机械工程—机械制造及自动化]