汽车轮毂轴承外圈锻造工艺优化设计与仿真分析
Optimized design and simulation analysis on automotive wheel hub bearing outer ring forging process
刘鹏宇 1康乃正 2黄江华 2李俊玲 3纪宏超 1刘悦4
作者信息
- 1. 华北理工大学机械工程学院,河北 唐山 063210;浙江兆丰机电股份有限公司,浙江 杭州 311231
- 2. 浙江兆丰机电股份有限公司,浙江 杭州 311231
- 3. 北京科技大学机械工程学院,北京 100083
- 4. 华北理工大学机械工程学院,河北 唐山 063210
- 折叠
摘要
针对某汽车轮毂轴承外圈制备中存在的生产效率低、材料利用率低等问题,设计了精密闭式热模锻成形工艺,并根据锻造成形理论制定了镦粗、预锻、终锻3工位锻造工序.利用Deform-3D软件对轴承外圈零件的成形过程进行模拟,发现该工艺存在锻造载荷大、模具磨损深度大、模具寿命低等问题.为此对预锻结构进行改进,提出两种优化方案,并进行成形检验,依次对比2种优化方案的锻后等效应力、锻造载荷以及模具磨损情况.研究结果表明,采用方案2时锻件的金属流线及外部成形情况良好,锻造载荷降低约20%,上模和下模磨损深度分别降低了1.200×10-6和0.780×10-6 mm,有利于锻件质量提高和锻造模具寿命延长.生产试验验证表明,方案2获得的成形锻件符合生产要求.
Abstract
For the problems of low production efficiency and low material utilization in preparation of a certain automotive wheel hub bearing outer ring,a precision hot closed die forging process was designed,and based on forging forming theory,three forging processes including upsetting,pre-forging and final forging were formulated.Then,the forming process of the bearing outer ring parts was simulated by software Deform-3D,and it was found that the forging load under this process was high,the degree of die wear was deep,and the die life was low.Furthermore,the pre-forged structure was improved,and two optimization schemes were proposed,which was subjected to forming inspection and compared in sequence the post-forging equivalent stress,forging load and die wear conditions between two optimization schemes.The results indicate that the metal streamline and external forming conditions of the forgings using scheme 2 are good,the forging load decreases by approximately 20%,and the wear depths of upper die and lower die reduce by 1.200×10-6 and 0.780×10-6 mm,respectively which is helpful for improving the quality of forgings and ex-tends the life of forging dies.Finally,the production test indicates that the formed forgings meet the production requirements by using scheme 2.
关键词
轴承外圈/锻造/载荷/模具磨损/金属流线/模具寿命Key words
bearing outer ring/forging/load/die wear/metal streamline/die life引用本文复制引用
出版年
2025
NETL
NSTL
万方数据