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

变参数铣削刀具磨损状态监测研究

谢庆陆 王国锋

谢庆陆, 王国锋. 变参数铣削刀具磨损状态监测研究[J]. 机械科学与技术, 2016, 35(12): 1842-1847. doi: 10.13433/j.cnki.1003-8728.2016.1207
引用本文: 谢庆陆, 王国锋. 变参数铣削刀具磨损状态监测研究[J]. 机械科学与技术, 2016, 35(12): 1842-1847. doi: 10.13433/j.cnki.1003-8728.2016.1207
Xie Qinglu, Wang Guofeng. Study on Tool Wear State Monitoring of Variable Parameters Milling[J]. Mechanical Science and Technology for Aerospace Engineering, 2016, 35(12): 1842-1847. doi: 10.13433/j.cnki.1003-8728.2016.1207
Citation: Xie Qinglu, Wang Guofeng. Study on Tool Wear State Monitoring of Variable Parameters Milling[J]. Mechanical Science and Technology for Aerospace Engineering, 2016, 35(12): 1842-1847. doi: 10.13433/j.cnki.1003-8728.2016.1207

变参数铣削刀具磨损状态监测研究

doi: 10.13433/j.cnki.1003-8728.2016.1207
基金项目: 

国家自然科学基金项目(51175371)、国家重大专项(2014ZX04012014)及天津市科技支撑计划项目(14ZCZDGX00021)资助

详细信息
    作者简介:

    谢庆陆(1991-),硕士研究生,研究方向为刀具磨损状态监测和数控机床伺服进给系统数学建模仿真,xql246888@126.com

    通讯作者:

    王国锋(联系人),副教授,博士,gfwangmail@tju.edu.cn

Study on Tool Wear State Monitoring of Variable Parameters Milling

  • 摘要: 针对切削力旧有时域特征易受切削参数变动影响而不适用于变参数铣削刀具磨损状态监测的缺陷,采用了一组新的无量纲切削力时域特征(归一化切削力指标NCF、变异系数Cv和峰值力比MFR)。并以难加工材料TC4钛合金变参数铣削实验来验证新特征在变参数铣削刀具磨损状态监测上的有效性,分别以新旧特征作为SVM分类器的输入,分析和比较结果表明本文提出的无量纲切削力时域特征对切削参数变化不敏感,而仅对刀具磨损状态变化敏感,因此能够实现变参数铣削刀具磨损状态监测。
  • [1] Kurada S, Bradley C. A review of machine vision sensors for tool condition monitoring[J]. Computer in Industry, 1997,34(1):55-72
    [2] Li H Z, Zeng H, Chen X Q. An experimental study of tool wear and cutting force variation in the end milling of Inconel 718 with coated carbide inserts[J]. Journal of Materials Processing Technology, 2006,180(1-3):296-304
    [3] Alonso F J, Salgado D R. Analysis of the structure of vibration signals for tool wear detection[J]. Mechanical Systems and Signal Processing, 2008,22(3):735-748
    [4] Lee J H, Kim D E, Lee S J. Statistical analysis of cutting force ratios for flank-wear monitoring[J]. Journal of Materials Processing Technology, 1998,74(1-3):104-114
    [5] Ravindra H V, Srinivasa Y G, Krishnamurthy R. Acoustic emission for tool condition monitoring in metal cutting[J]. Wear, 1997,212(1):78-84
    [6] Salgado D R, Alonso F J. An approach based on current and sound signals for in-process tool wear monitoring[J]. International Journal of Machine Tools and Manufacture, 2007,47(14):2140-2152
    [7] 叶林,刘鹏.基于经验模态分解和支持向量机的短期风电功率组合预测模型[J].中国电机工程学报,2011,31(31):102-108 Ye L, Liu P. Combined model based on EMD-SVM for short-term wind power prediction[J]. Proceedings of the CSEE, 2011,31(31):102-108 (in Chinese)
    [8] Shi D F, Gindy N N. Tool wear predictive model based on least squares support vector machines[J]. Mechanical Systems and Signal Processing, 2007,21(4):1799-1814
    [9] Lin K P, Chen M S. On the design and analysis of the privacy-preserving SVM classifier[J]. IEEE Transactions on Knowledge and Data Engineering, 2011,23(11):1704-1717
    [10] Zhou J G, Bai T, Tian J M, et al. The study of SVM optimized by culture particle swarm optimization on predicting financial distress[C]//Proceedings of the 2008 IEEE International Conference on Automation and Logistics. New York: IEEE, 2008:1054-1059
    [11] Elangovan M, Sugumaran V, Ramachandran K I, et al. Effect of SVM kernel functions on classification of vibration signals of a single point cutting tool[J]. Expert Systems with Applications, 2011,38(12):15202-15207
    [12] Chen X, Limchimchol T. Monitoring grinding wheel redress-life using support vector machines[J]. International Journal of Automation and Computing, 2006,3(1):56-62
    [13] Huang J, Hu X G, Yang F. Support vector machine with genetic algorithm for machinery fault diagnosis of high voltage circuit breaker[J]. Measurement, 2011,44(6):1018-1027
    [14] 关山.在线金属切削刀具磨损状态监测研究的回顾与展望IIΠ信号特征的提取[J].机床与液压,2010,38(17):121-125 Guan S. The review and perspective of the research of on-line and indirect metal cutting tool condition monitoring ΠI: feature extraction of monitoring signals[J]. Machine Tool & Hydraulics, 2010,38(17):121-125 (in Chinese)
    [15] Abellan-Nebot J V, Subirón F R. A review of machining monitoring systems based on artificial intelligence process models[J]. The International Journal of Advanced Manufacturing Technology, 2010,47(1-4):237-257
    [16] 姚继涛,解耀魁.既有结构可靠性评定中变异系数统计推断[J].建筑结构学报,2010,31(8):101-105 Yao J T, Xie Y K. Statistical inference for coefficient of variation in reliability assessment of existing structure[J]. Journal of Building Structures, 2010,31(8):101-105 (in Chinese)
    [17] 钱磊.基于支持向量机的变参数铣削刀具磨损状态监测研究[D].天津:天津大学,2014 Qian L. Research on tool wear state monitoring based on support vector machine in milling under variable parameters[D]. Tianjin: Tianjin University, 2014 (in Chinese)
    [18] 赵湛,李耀明,陈义,等.水稻籽粒碰撞力学特性研究[J].农业机械学报,2013,44(6):88-92 Zhao Z, Li Y M, Chen Y, et al. Impact mechanical characteristics analysis of rice grain[J]. Transactions of the Chinese Society for Agricultural Machinery, 2013,44(6):88-92 (in Chinese)
    [19] Kuljanic E, Sortino M. TWEM, a method based on cutting forces-monitoring tool wear in face milling[J]. International Journal of Machine Tools and Manufacture, 2005,45(1):29-34
  • 加载中
计量
  • 文章访问数:  125
  • HTML全文浏览量:  17
  • PDF下载量:  10
  • 被引次数: 0
出版历程
  • 收稿日期:  2015-03-30
  • 刊出日期:  2017-01-05

目录

    /

    返回文章
    返回