留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

表面三维高度参数与最大Mises应力关联性研究

夏富佳 唐进元 杨铎 温昱钦

夏富佳,唐进元,杨铎, 等. 表面三维高度参数与最大Mises应力关联性研究[J]. 机械科学与技术,2023,42(6):890-897 doi: 10.13433/j.cnki.1003-8728.20220011
引用本文: 夏富佳,唐进元,杨铎, 等. 表面三维高度参数与最大Mises应力关联性研究[J]. 机械科学与技术,2023,42(6):890-897 doi: 10.13433/j.cnki.1003-8728.20220011
XIA Fujia, TANG Jinyuan, YANG Duo, WEN Yuqin. Study on Relationship Between Three-dimensional Height Parameters and Maximum Mises Stress of Surface[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(6): 890-897. doi: 10.13433/j.cnki.1003-8728.20220011
Citation: XIA Fujia, TANG Jinyuan, YANG Duo, WEN Yuqin. Study on Relationship Between Three-dimensional Height Parameters and Maximum Mises Stress of Surface[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(6): 890-897. doi: 10.13433/j.cnki.1003-8728.20220011

表面三维高度参数与最大Mises应力关联性研究

doi: 10.13433/j.cnki.1003-8728.20220011
基金项目: 国家重点研发计划项目(2018YFB2001300)
详细信息
    作者简介:

    夏富佳(2002−),硕士,研究方向为界面摩擦、形貌表面重构,lxqf_xfj@163.com

    通讯作者:

    唐进元,教授,博士生导师,jytangcsu@163.com

  • 中图分类号: TH117.1

Study on Relationship Between Three-dimensional Height Parameters and Maximum Mises Stress of Surface

  • 摘要: 为研究粗糙表面微观形貌特征参数与接触应力的关联性,选取粗糙度参数标准ISO25178中与接触变形密切相关的三维高度参数为研究对象。通过BP神经网络构建高度参数与最大Mises应力的关系模型,并分别采用Sobol和MIV敏感性分析法,基于220组实测超声磨削加工表面数据分析了高度参数对最大Mises应力的影响程度并筛选出了主要影响参数。利用统计学相关性原理和多项式回归分析方法,建立了主要影响参数和最大Mises应力的非线性回归模型。结果表明:1)7个高度参数对最大Mises应力的重要性由大到小为:算数平均高度Sa、均方根高度Sq、偏斜度Ssk、最大高度Sz、最大峰高Sp最大谷深Sv和峭度Sku;2)以90% 的总影响度为基准,筛选出主要影响参数为:SaSqSsk;3)回归模型相对于椭球微凸体接触算法,相对误差在10%以内,且计算效率更高,具有一定工程实用价值。
  • 图  1  BP神经网络传播原理示意图

    图  2  参数筛选流程图

    图  3  BP神经网络误差变化

    图  4  训练集预测值和期望值相对误差

    图  5  测试集预测值和期望值相对误差

    图  6  高度参数敏感性分析结果

    图  7  筛选参数的BP神经网络误差变化

    图  8  筛选参数的训练集预测值和期望值相对误差

    图  9  筛选参数的测试集预测值和期望值相对误差

    图  10  筛选参数敏感性分析结果

    图  11  回归分析流程图

    图  12  最大Mises应力预测值和期望值相对误差

    图  13  改进后模型最大的相对误差

    表  1  超声磨削实验条件

    加工参数参数水平
    砂轮类型CBN砂轮
    半径100 mm
    目数120
    转速1500 r/min
    切削速度200 mm/min
    切削深度5~25 μm
    超声振动频率20 kHz
    振幅0~10 μm
    下载: 导出CSV

    表  2  SaSqSsk和最大Mises应力之间相关系数

    SaSqSsk最大Mises应力
    Sa10.9790.1910.814
    Sq0.97910.0970.792
    Ssk0.1910.09710.223
    最大Mises应力0.8140.7920.2231
    下载: 导出CSV

    表  3  最大Mises应力计算结果对比

    样本序号文献[20]模型/MPa本文模型/MPa相对误差/%
    11366.3 1447.7 MPa6.0
    21632.5 1646.7 0.9
    31713.8 1752.0 2.2
    41454.8 1474.9 1.4
    51241.2 1289.2 3.9
    下载: 导出CSV
  • [1] 胡瑢华, 甘泽新. 公差配合与测量[M]. 北京: 清华大学出版社, 2005

    HU R H, GAN Z X. Tolerance fit and measurement[M]. Beijing: Tsinghua University Press, 2005. (in Chinese)
    [2] 李成贵, 董申. 表面粗糙度的现状及发展[J]. 航空精密制造技术, 1999, 35(5): 1-4. doi: 10.3969/j.issn.1003-5451.1999.05.001

    LI C G, DONG S. Current situation and development of surface roughness[J]. Aviation Precision Manufacturing Technology, 1999, 35(5): 1-4. (in Chinese) doi: 10.3969/j.issn.1003-5451.1999.05.001
    [3] 王玉田, 韩向春. 表面粗糙度的表征参数及其与表面功能特性的关系[J]. 东北重型机械学院学报, 1983(4): 49-57.

    WANG Y T, HAN X C. Characterization parameters of surface roughness and its relationship with surface fun- ctional properties[J]. Journal of Yanshan University, 1983(4): 49-57. (in Chinese)
    [4] 何宝凤, 魏翠娥, 刘柄显, 等. 三维表面粗糙度的表征和应用[J]. 光学 精密工程, 2018, 26(8): 1994-2911. doi: 10.3788/OPE.20182608.1994

    HE B F, WEI C E, LIU B X, et al. Three-dimensional surface roughness characterization and application[J]. Optics and Precision Engineering, 2018, 26(8): 1994-2911. (in Chinese) doi: 10.3788/OPE.20182608.1994
    [5] STOUT K J. Development of methods for the characte- rization of roughness in three dimensions[M]. Amstrdam: Elsevier, 1998
    [6] 陈国强, 张维强, 彭文静. 研磨表面微观形貌的三维检测及Areal表征[J]. 机械设计与研究, 2009, 25(2): 19-22.

    CHEN G Q, ZHANG W Q, PENG W J. Research on the 3-D detection and Areal characterization of micro-topography of lapping surface[J]. Machine Design and Research, 2009, 25(2): 19-22. (in Chinese)
    [7] RENOUF M, MASSI F, FILLOT N, et al. Numerical tribology of a dry contact[J]. Tribology International, 2011, 44(7-8): 834-844. doi: 10.1016/j.triboint.2011.02.008
    [8] MENG X H, GU C X, XIE Y B. Elasto-plastic contact of rough surfaces: a mixed-lubrication model for the textured surface analysis[J]. Meccanica, 2017, 52(7): 1541-1559. doi: 10.1007/s11012-016-0492-1
    [9] QI Q, LI T, SCOTT P J, et al. A correlational study of areal surface texture parameters on some typical machined surfaces[J]. Procedia CIRP, 2015, 27: 149-154. doi: 10.1016/j.procir.2015.04.058
    [10] YANG D, TANG J Y, ZHOU W, et al. Correlation between surface roughness parameters and contact stress of gear[J]. Proceedings of the Institution of Mechanical Engineers, Part J:Journal of Engineering Tribology, 2021, 235(3): 551-563. doi: 10.1177/1350650120928661
    [11] RUMELHART D E, HINTON G E, WILLIAMS R J. Learning representations by back-propagating errors[J]. Nature, 1986, 323(6088): 533-536. doi: 10.1038/323533a0
    [12] 池浩瀚, 李世海, 刘雪松. 基于遗传算法及神经网络的6005A-T6铝合金FSW工艺参数优化[J]. 热加工工艺, 2021, 50(7): 118-120.

    CHI H H, LI S H, LIU X S. FSW process parameters optimization of 6005A-T6 Al alloy based on genetic algorithm and neural network[J]. Hot Working Technology, 2021, 50(7): 118-120. (in Chinese)
    [13] SIETSMA J, DOW R J F. Neural net pruning-why and how[C]//Proceedings of the IEEE 1988 International Conference on Neural Networks. San Diego: IEEE, 1988: 325-333
    [14] CASTELLANO G, FANELLI A M, PELILLO M. An iterative pruning algorithm for feedforward neural networks[J]. IEEE Transactions on Neural Networks, 1997, 8(3): 519-531. doi: 10.1109/72.572092
    [15] 张虹, 王丹. 一种改进的BP神经网络剪枝算法研究[J]. 西南大学学报(自然科学版), 2016, 38(3): 165-170. doi: 10.13718/j.cnki.xdzk.2016.03.026

    ZHANG H, WANG D. An improved BP neural network based on CPA[J]. Journal of Southwest University (Natural Science Edition), 2016, 38(3): 165-170. (in Chinese) doi: 10.13718/j.cnki.xdzk.2016.03.026
    [16] GAN Y J, DUAN Q Y, GONG W, et al. A comprehensive evaluation of various sensitivity analysis methods: a case study with a hydrological model[J]. Environmental Modelling & Software, 2014, 51: 269-285.
    [17] SOBOL′ I M, ASOTSKY D, KREININ A, et al. Construction and comparison of high-dimensional Sobol′ generators[J]. Wilmott, 2011, 2011(56): 64-79. doi: 10.1002/wilm.10056
    [18] DOMBI G W, NANDI P, SAXE J M, et al. Prediction of rib fracture injury outcome by an artificial neural network[J]. The Journal of Trauma:Injury, Infection, and Critical Care, 1995, 39(5): 915-921. doi: 10.1097/00005373-199511000-00016
    [19] 王紫微, 叶奇旺. 基于神经网络MIV值分析的肿瘤基因信息提取[J]. 数学的实践与认识, 2011, 41(14): 47-58.

    WANG Z W, YE Q W. Cancer informative gene identification based on MIV method of neural network[J]. Mathematics in Practice and Theory, 2011, 41(14): 47-58. (in Chinese)
    [20] WEN Y Q, TANG J Y, ZHOU W, et al. A reconstruction and contact analysis method of three-dimensional rough surface based on ellipsoidal asperity[J]. Journal of Tribology, 2020, 142(4): 041502. doi: 10.1115/1.4045633
    [21] 田峰, 李昂, 陈德春, 等. 灰色关联分析和多元二次非线性回归分析模型研究及应用[J]. 内蒙古石油化工, 2012, 38(9): 118-120.

    TIAN F, LI A, CHEN D C, et al. The model research and software development of gray correlation degree and multiple quadratic regression analysis[J]. Inner Mongolia Petrochemical Industry, 2012, 38(9): 118-120. (in Chinese)
    [22] 廖森, 王建设, 陈超球. 共线性在多元二次多项式回归模型中的危害及其处理方法[J]. 广西科学, 2003, 10(2): 101-103. doi: 10.3969/j.issn.1005-9164.2003.02.006

    LIAO S, WANG J S, CHEN C Q. Problems caused by collinearity and solution in the multivariate second-degree polynomial models[J]. Guangxi Sciences, 2003, 10(2): 101-103. (in Chinese) doi: 10.3969/j.issn.1005-9164.2003.02.006
    [23] 宋春山, 朱新宇, 韩红卫, 等. 河道特征对黑龙江上游冰坝生消影响[J]. 水利学报, 2020, 51(10): 1256-1266. doi: 10.13243/j.cnki.slxb.20200045

    SONG C S, ZHU X Y, HAN H W, et al. The influence of riverway characteristics on the generation and dissipation of ice dam in the upper reaches of Heilongjiang River[J]. Journal of Hydraulic Engineering, 2020, 51(10): 1256-1266. (in Chinese) doi: 10.13243/j.cnki.slxb.20200045
  • 加载中
图(13) / 表(3)
计量
  • 文章访问数:  109
  • HTML全文浏览量:  46
  • PDF下载量:  15
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-03-24
  • 刊出日期:  2023-06-25

目录

    /

    返回文章
    返回