网站首页期刊简介编委会过刊目录投稿指南广告合作征订与发行联系我们English
基于有限元分析的车用空心六角头杆件芯模寿命研究
英文标题:Study on service life of core mold for vehicle hollow hexagonal rod based on finite element analysis
作者:赵慧真 崔华丽 
单位:郑州经贸学院 智慧制造学院 
关键词:空心六角头杆件 深孔挤压 磨损模型 芯模结构 TiAlN涂层 
分类号:TG376.3
出版年,卷(期):页码:2023,48(7):177-183
摘要:

 在某种车用空心六角头杆件的挤深孔工序中,负责成形深孔的芯模受力极大,表面磨损严重、失效快。为解决该问题,基于经典的粘着磨损理论,通过Deform11.0软件,研究了5种不同结构的芯模的挤压过程,对比了芯模表面的最大磨损深度和严重磨损面积,结果发现同等条件下锥面芯模的寿命更长。以降低磨损为目的,通过正交试验对锥面芯模的尺寸参数进行了优化,同时研究了不同涂层对芯模表面的降磨损作用,结果表明在TiAlN涂层的降磨损效果更好。采用改进后的芯模进行零件的深孔成形,孔的成形质量较好,芯模寿命为原来的2.48倍。

 In the deep hole extrusion process for a vehicle hollow hexagonal rod, the core mold responsible for forming deep holes has great force, the surface wear is serious, and the failure is rapid. Therefore, in order to solve this problem, based on the classic adhesive wear theory, the extrusion processes of five kinds of core molds with different structures were studied by saftware Deform 11.0, and the maximum wear depth and the severe wear area on the core mold surface were compared. The results show that the service life of tapered core mold is longer under the same condition. For the purpose of reducing wear, the size parameters of tapered core mold were optimized by orthogonal test. At the same time, the wear reduction effect of different coatings on the core mold surface was studied. The results show that the wear reduction effect of TiAlN coating is better. Thus, the deep hole forming of parts is conducted by the improved core mold, the forming quality of hole is better, and the service life of core mold is 2.48 times that of the original mold.

基金项目:
校级青年科研基金项目(QK2114)
作者简介:
作者简介:赵慧真(1988-),女,硕士,讲师 E-mail:zhz8812@163.com
参考文献:

 [1]仇丹丹,龚红英,李会肖,等.汽车过滤器壳体零件冷挤压成形方案研究[J].热加工工艺,2015,44(19):134-136.


Qiu D D,Gong H Y,Li H X,et al.Research on cold extrusion forming process for automotive filter shell part[J].Hot Working Technology,2015,44(19):134-136.

[2]夏建芳,张明强,高放军.挤压参数及模具结构参数对某大长径比铝合金零件冷挤压流变成形载荷的影响[J].现代制造工程,2021,(10):101-106.

Xia J F,Zhang M Q,Gao F J.Effect of extrusion parameters and die structure parameters on rheological forming load of a large aspect ratio aluminum alloy part during cold extrusion[J].Modern Manufacturing Engineering,2021,(10):101-106.

[3]侯红玲,陈鑫,常向龙,等.基于遗传算法优化BP神经网络的内螺纹冷挤压质量预测[J].塑性工程学报,2022,29(1):102-109.

Hou H L,Chen X,Chang X L,et al.Quality prediction of internal thread cold extrusion based on BP neural network optimized by genetic algorithm[J].Journal of Plasticity Engineering,2022,29(1):102-109.

[4]张东民,盛育东,张金玉,等.六角开槽螺母的冷镦工艺优化及数值模拟[J].机械设计与制造,2018,(3):191-194.

Zhang D M,Sheng Y D,Zhang J Y,et al. Numerical simulation and optimization for cold heading of hexagonal slot nut[J].Machinery Design & Manufacture,2018,(3):191-194.

[5]陈凌翔,李月超.汽车六角球头冷锻工艺优化与数值仿真[J].材料科学与工艺,2020,28(5):75-82.

Chen L X,Li Y C.Optimization and numerical simulation of cold forging process for automobile hexagonal ball head[J].Materials Science and Technology,2020,28(5):75-82.

[6]张京,吴淑芳,陈风龙.气门顶杆冷挤压过程模具磨损研究[J].机械工程与自动化,2019,(2):33-35.

Zhang J,Wu S F,Chen F L. Research on wear of cold extrusion die for valve stem[J].Mechanical Engineering & Automation,2019,(2):33-35.

[7]范建祥,程道来,倪伟豪.基于正交试验汽车六角螺母冷挤压模具磨损规律分析及优化[J].铸造技术,2018,39(4):929-932.

Fan J X,Chen D L,Ni W H.Process analysis and optimization on die wear in cold extrusion for vehicle hex nut[J].Foundry Technology,2018,39(4):929-932.

[8]张宇杭,张甲瑞.基于数值模拟的汽车深孔螺母件反挤压冲头磨损性能研究[J].锻压技术,2022,47(2):180-185.

Zhang Y H,Zhang J R.Research on wear performance of reverse extrusion punch for automotive deep hole nut based on numerical simulation[J].Forging & Stamping Technology,2022,47(2):180-185.

[9]Archard J F.Contact and rubbing of flat surfaces[J].Journal of Applied Physics,1953,24 ( 8):981-988.

[10]王凌浩,辛选荣.19MnB4冷镦钢常温压缩动态力学性能及本构方程[J].热加工工艺,2016,45(13):142-145.

Wang L H,Xin X R.Dynamic mechanical properties and constitutive equation of 19MnB4 cold heading steel compression at room temperature[J].Hot Working Technology,2016,45(13):142-145.

[11]胡建军,李小平.Deform3D塑性成形CAE应用教程[M].北京:北京大学出版社,2011.

Hu J J,Li X P.Deform3D Plastic Forming CAE Application Tutorial[M].Beijing:Peking University Press,2011.

[12]张智华. 冷挤压凸模磨损数值模拟及涂层强化研究[D]. 重庆:重庆大学,2017.

Zhang Z H.Wear Simulation of Cold Extrusion Punch and Research of Coating Strengthening[D]. Chongqing:Chongqing University,2017.

[13]张学奇,董万鹏,葛力华,等.基于正交试验的闭式挤压工艺参数优化[J].塑性工程学报,2017,24(3):84-89.

Zhang X Q,Dong W P,Ge L H,et al. Processing parameters optimization of closed extrusion based on orthogonal experiment[J].Journal of Plasticity Engineering,2017,24(3):84-89.

(上接第161页)

[9]万里瑞, 王康康, 王辉. 利用增强多尺度模糊熵的齿轮故障诊断方法[J]. 机械设计与研究, 2021, 37(5): 73-77.

Wan L R, Wang K K, Wang H. Gear fault diagnosis method based on enhanced multiscale fuzzy entropy [J]. Machine Design & Research, 2021, 37(5): 73-77.

[10]蔡波, 黄晋英, 杜金波, 等. 基于MEEMD多特征融合与LSSVM的行星齿轮箱故障诊断[J]. 中国测试, 2021, 47(9): 126-132.

Cai B, Huang J Y, Du J B, et al. Fault diagnosis of planetary gearbox based on MEEMD multifeature fusion and LSSVM [J].  China Measurement & Test, 2021, 47(9): 126-132.

[11]李韵仪, 沈艳霞. 基于连续隐马可夫模型的变工况风机冷轧机性能退化评估[J]. 机械设计与研究, 2021, 37(4): 106-109,114.

Li Y Y, Shen Y X. Gearbox performance degradation assessment under variable operating conditions based on continuous hidden markov model[J]. Machine Design & Research, 2021, 37(4): 106-109,114.

[12]李立成, 华成丽, 梁栋. 基于粒子群优化的机械传动齿轮箱非正常振动检测算法[J]. 现代制造工程, 2021,(8): 132-137.

Li L C, Hua C L, Liang D. Algorithm for detecting abnormal vibration of mechanical transmission gearbox based on particle swarm optimization[J]. Modern Manufacturing Engineering, 2021,(8): 132-137.

[13]黄丽丽, 范业锐, 张文兴. 参数优化形态谱和SVM的行星齿轮箱故障诊断[J]. 机械设计与制造, 2021,(8): 31-33.

Huang L L, Fan Y R, Zhang W X. Fault diagnosis of planetary gear box based on parameter optimization morphology spectrum and SVM [J]. Machinery Design & Manufacture, 2021,(8): 31-33.

[14]李宇恒, 蒋章雷, 梁好, 等. 基于HEI量化故障信息的行星齿轮箱故障诊断方法研究[J]. 机电工程, 2021, 38(7): 836-842.

Li Y H, Jiang Z L, Liang H, et al. Research on fault diagnosis method of planetary gearbox based on HEI quantization fault information [J]. Mechanical & Electrical Engineering, 2021, 38(7): 836-842.

[15]谢锋云, 董建坤, 王二化, 等. 基于双隐含层RWPSOBP神经网络的齿轮箱故障诊断研究[J]. 现代制造工程, 2021, (6): 155-160. 

Xie F Y, Dong J K, Wang E H, et al. Research on gearbox fault diagnosis based on RWPSOBP neural network with double hidden layers [J]. Modern Manufacturing Engineering, 2021, (6): 155-160.

 
服务与反馈:
本网站尚未开通全文下载服务】【加入收藏
《锻压技术》编辑部版权所有

中国机械工业联合会主管  中国机械总院集团北京机电研究所有限公司 中国机械工程学会主办
联系地址:北京市海淀区学清路18号 邮编:100083
电话:+86-010-82415085 传真:+86-010-62920652
E-mail: fst@263.net(稿件) dyjsjournal@163.com(广告)
京ICP备07007000号-9