薄壁构件铣削加工可靠性灵敏度分析

doi:10.12068/j.issn.1005-3026.2019.04.017

东北大学学报:自然科学版 ›› 2019, Vol. 40 ›› Issue (4): 543-547.DOI: 10.12068/j.issn.1005-3026.2019.04.017

薄壁构件铣削加工可靠性灵敏度分析

黄贤振¹，臧云飞¹，宋增旺²，矫宝龙¹

(1. 东北大学机械工程与自动化学院，辽宁沈阳110819; 2. 北方重工集团有限公司，辽宁沈阳110141)

收稿日期:2018-04-18 修回日期:2018-04-18 出版日期:2019-04-15 发布日期:2019-04-26
通讯作者: 黄贤振
作者简介:黄贤振(1982-)，男，山东定陶人，东北大学教授，博士.
基金资助:
国家自然科学基金资助项目(51575094)；国家博士后基金资助项目(2017M611244)；中央高校基本科研业务费专项资金资助项目(N160304004).

Reliability Sensitivity Analysis of Thin-Walled Components Milling Machining

HUANG Xian-zhen ¹， ZANG Yun-fei¹， SONG Zeng-wang²， JIAO Bao-long¹

1. School of Mechanical Engineering & Automation， Northeastern University， Shenyang 110819， China; 2. Northern Heavy Industries Group Co. Ltd.， Shenyang 110141， China.

Received:2018-04-18 Revised:2018-04-18 Online:2019-04-15 Published:2019-04-26
Contact: HUANG Xian-zhen
About author:-
Supported by:
-

摘要/Abstract

摘要： 利用单频率法建立薄壁构件铣削稳定性及表面位置误差模型，采用Kriging方法对薄壁构件铣削稳定性及表面位置误差进行了可靠性灵敏度分析，评价铣削参数对薄壁构件铣削稳定性及表面位置误差的影响程度.研究结果表明:随薄壁构件铣削系统的阻尼比及y向刚度的增加，系统的可靠度增加;随切向力系数、固有频率、径向侵入比及轴向切深的增加，系统可靠度降低;径向力系数和每齿进给量对系统的影响较小.研究结果能为薄壁构件高速铣削加工提供合理的理论依据.

关键词: 薄壁构件, Kriging, 铣削稳定性, 表面位置误差, 可靠性灵敏度

Abstract: A stability and surface position error model for the thin-walled components milling was established by using a single frequency method. The Kriging method was used to analyze the reliability sensitivity of the stability and surface position error of thin-walled components milling， and to evaluate the influence of milling parameters on the stability and surface position error of thin-walled components milling. The results show that the system reliability increases with the increase of damping ratio and y direction stiffness of the thin-walled components milling system. The system reliability decreases with the increase of tangential force coefficient， natural frequency， radial invasion ratio and axial cutting depth. The radial force factor and feed per tooth have less impact on the system. The research results may provide reasonable theoretical basis for high speed milling of thin-walled components.

Key words: thin-walled components, Kriging, milling stability, surface position error, reliability sensitivity

中图分类号:

TG54

黄贤振，臧云飞，宋增旺，矫宝龙. 薄壁构件铣削加工可靠性灵敏度分析[J]. 东北大学学报:自然科学版, 2019, 40(4): 543-547.

HUANG Xian-zhen ， ZANG Yun-fei， SONG Zeng-wang， JIAO Bao-long. Reliability Sensitivity Analysis of Thin-Walled Components Milling Machining[J]. Journal of Northeastern University Natural Science, 2019, 40(4): 543-547.

参考文献

[1]卢晓红，王凤晨，王华，等.铣削过程颤振稳定性分析的研究进展［J］.振动与冲击，2016，35(1):74-82.(Lu Xiao-hong，Wang Feng-chen，Wang Hua，et al.Research progress on flutter stability analysis in milling process［J］.Vibration and Impact，2016，35(1):74-82.)
[2]Wu G，Luan W.Self-excited vibration of driveline for vehicle launch［J］.Journal of Beijing Institute of Technology，2013(3):330-336.
[3]Ding H，Ding Y.On time-domain methods for milling stability analysis［J］.Chinese Science Bulletin，2012(33):4336-4345.
[4]Jemielniak K，Widota A.Suppression of self-excited vibration by the spindle speed variation method［J］.International Journal of Machine Tool Design & Research，1984，24(3):207-214.
[5]Li H，Shin Y C.A comprehensive dynamic end milling simulation model［J］.Journal of Manufacturing Science & Engineering，2006，128(1):86-95.
[6]Thevenot V，Arnaud L，Dessein G，et al.Influence of material removal on the dynamic behavior of thin-walled structures in peripheral milling［J］.Machining Science and Technology，2006，10(3):276-283.
[7]Tang A，Liu Z.Three-dimensional stability lobe and maximum material removal rate in end milling of thin-walled plate［J］.The International Journal of Advanced Manufacturing Technology，2009(1/2):33-39.
[8]Bravo U，Altuzarra O.Stability limits of milling considering the flexibility of the workpiece and the machine［J］.International Journal of Machine Tools & Manufacture，2005，45(15):1669-1680.
[9]Seguy S.Toolpath dependent stability lobes for the milling of thin-walled parts［J］.International Journal of Machining & Machinability of Materials，2008，4(3/4):261-274.
[10]Kaymaz I.Application of Kriging method to structural reliability problems［J］.Structural Safety，2005，27(2):133-151.
[11]Sun Z，Wang J，Li R，et al.LIF:a new Kriging based learning function and its application to structural reliability analysis［J］.Reliability Engineering & System Safety，2017，157:152-165.
[12]Schmitz T L，Mann B P.Closed-form solutions for surface location error in milling［J］.International Journal of Machine Tools and Manufacture，2006，46(12/13):1369-1377.
[13]Echard B，Gayton N，Lemaire M.AK-MCS: an active learning reliability method combining Kriging and Monte Carlo simulation［J］.Structural Safety，2011，33(2):145-154.
[14]Zhang W，Dai B，Liu Z，et al.Unconfined seepage analysis using moving Kriging mesh-free method with Monte Carlo integration［J］.Transport in Porous Media，2016，116(1):1-18.
[15]Ma J，Ren Z，Zhao G，et al.A new reliability analysis method combining adaptive Kriging with weight index Monte Carlo simulation ［J］.IEEE Transactions on Magnetics，2018，54(3):1-4.
[16]Gong Q，Zhang J G，Su D.Reliability simulation combining Kriging and Monte Carlo radius-outside importance sampling in space structure latch［J］.Applied Mechanics and Materials，2012，166/167/168/169:1872-1878.
[17]Emery X.Two ordinary Kriging approaches to predicting block grade distributions［J］.Mathematical Geology，2006，38(7):801-819.
[18]Ginsbourger D，Rosspopoff B，Pirot G，et al.Distance-based Kriging relying on proxy simulations for inverse conditioning［J］.Advances in Water Resources，2013，52:275-291.
[19]Emery X.Simple and ordinary multigaussian Kriging for estimating recoverable reserves［J］.Mathematical Geology，2005，37(3):295-319.
[20]Janusevskis J，Riche R L.Simultaneous Kriging-based estimation and optimization of mean response［J］.Journal of Global Optimization，2013，55(2):313-336.
[21]Kleijnen W，Jack P C.Simulation-optimization via Kriging and bootstrapping: a survey［J］.Journal of Simulation，2014，8(4):241-250.
[22]Zhang L，Lu Z，Wang P.Efficient structural reliability analysis method based on advanced Kriging model［J］.Applied Mathematical Modeling，2015，39(2):781-793.
[23]Ng S H，Yin J.Bayesian Kriging analysis and design for stochastic simulations［J］.ACM Transactions on Modeling and Computer Simulation，2012，22(3):1-26.
[24]刘彪.薄壁件铣削稳定性与动态加工误差研究［D］.北京:北京理工大学，2015.(Liu Biao.Study on stability and dynamic machining error of thin-walled parts［D］.Beijing:Beijing Institute of Technology，2015.)

薄壁构件铣削加工可靠性灵敏度分析

Reliability Sensitivity Analysis of Thin-Walled Components Milling Machining

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	杨周，朴银成，权哲优. 盘式制动器热-机耦合渐变可靠性灵敏度分析[J]. 东北大学学报（自然科学版）, 2022, 43(1): 48-56.
[2]	曹汝男，孙志礼，郭凡逸，王健. 基于Kriging和Monte Carlo的动态可靠性算法[J]. 东北大学学报（自然科学版）, 2021, 42(5): 658-664.
[3]	丁鹏飞，王昌利，黄贤振，李禹雄. 细长轴车削加工误差可靠性灵敏度分析[J]. 东北大学学报（自然科学版）, 2021, 42(5): 693-699.
[4]	黄贤振，朱会彬，姜智元，姜睿. 角接触球轴承打滑可靠性灵敏度分析[J]. 东北大学学报（自然科学版）, 2021, 42(12): 1731-1738.
[5]	张灏岩，毕秋实，李勃，郭广勇. 基于Kriging和MCMC的结构可靠性主动学习算法[J]. 东北大学学报（自然科学版）, 2021, 42(10): 1444-1450.
[6]	刘博林，谢里阳. 一种改进的全局可靠性分析方法[J]. 东北大学学报:自然科学版, 2020, 41(7): 943-948.
[7]	于震梁，孙志礼，张毅博，王健. 一种自适应PC-Kriging模型的结构可靠性分析方法[J]. 东北大学学报:自然科学版, 2020, 41(5): 667-672.
[8]	杨周，姜超，张义民，姜红猛. 采煤机截割部扭矩轴的动态可靠性分析[J]. 东北大学学报:自然科学版, 2020, 41(2): 217-222.
[9]	张毅博，孙志礼，闫玉涛，王健. 一种基于失效概率相对误差估计的可靠性分析方法[J]. 东北大学学报:自然科学版, 2020, 41(2): 229-234.
[10]	马小英，孙志礼，张毅博，臧旭. 基于Kriging-PSO智能算法优化焊接工艺参数[J]. 东北大学学报:自然科学版, 2019, 40(3): 370-375.
[11]	于震梁，孙志礼，曹汝男，张毅博. 基于PC-Kriging模型与主动学习的齿轮热传递误差可靠性分析[J]. 东北大学学报:自然科学版, 2019, 40(12): 1750-1754.
[12]	杨周，姜红猛，张义民，姜超. 实况下机械零件的动态可靠性分析[J]. 东北大学学报:自然科学版, 2019, 40(11): 1584-1589.
[13]	恩溪弄，张义民，黄贤振. 车削颤振时变可靠性分析[J]. 东北大学学报:自然科学版, 2019, 40(10): 1442-1447.
[14]	刘阔，李晓雷，王健. 一种基于Kriging模型的机械结构可靠性分析方法[J]. 东北大学学报:自然科学版, 2017, 38(7): 1002-1006.
[15]	周笛，张旭方，张义民. 采煤机牵引部可靠性灵敏度分析及优化设计[J]. 东北大学学报:自然科学版, 2017, 38(1): 81-85.