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不完备先验知识下的刀具磨损状态评估方法研究

张尔卿 傅攀 李威霖

张尔卿, 傅攀, 李威霖. 不完备先验知识下的刀具磨损状态评估方法研究[J]. 机械科学与技术, 2015, 34(3): 418-422. doi: 10.13433/j.cnki.1003-8728.2015.0319
引用本文: 张尔卿, 傅攀, 李威霖. 不完备先验知识下的刀具磨损状态评估方法研究[J]. 机械科学与技术, 2015, 34(3): 418-422. doi: 10.13433/j.cnki.1003-8728.2015.0319
Zhang Erqing, Fu Pan, Li Weilin. Tool Wear Condition Assessment Based on Incomplete Priori Knowledge[J]. Mechanical Science and Technology for Aerospace Engineering, 2015, 34(3): 418-422. doi: 10.13433/j.cnki.1003-8728.2015.0319
Citation: Zhang Erqing, Fu Pan, Li Weilin. Tool Wear Condition Assessment Based on Incomplete Priori Knowledge[J]. Mechanical Science and Technology for Aerospace Engineering, 2015, 34(3): 418-422. doi: 10.13433/j.cnki.1003-8728.2015.0319

不完备先验知识下的刀具磨损状态评估方法研究

doi: 10.13433/j.cnki.1003-8728.2015.0319
基金项目: 

中央高校基本科研业务费专项资金项目(SWJTU12CX039)资助

详细信息
    作者简介:

    张尔卿(1984-),博士研究生,研究方向为设备智能化状态监测与故障诊断,zrq@my.swjtu.edu.cn

    通讯作者:

    傅攀,教授,博士生导师,pfu@home.swjtu.edu.cn

Tool Wear Condition Assessment Based on Incomplete Priori Knowledge

  • 摘要: 在实际切削加工中刀具磨损的全状态先验知识获取困难,而刀具磨钝状态下的先验知识则较易获取。针对这种不完备先验知识情况,以切削力为监测信号,提出基于连续隐马尔可夫模型(CHMM)的刀具磨损状态评估技术。应用小波包分解技术提取信号特征信息,利用刀具磨钝状态下的先验归一化特征信息建立CHMM监测模型;根据刀具未知状态特性向量与监测模型间的对数似然度获取刀具性能指标,实现刀具磨损状态评价。铣刀全寿命磨损实验表明:该方法能在仅具备磨钝状态先验知识情况下,实现对刀具的磨损状态的初步评估,且所需样本数较少,训练速度快。
  • [1] Vallejo Jr A G, Nolazco-Flores J A, Morales-Menéndez R, et al. Tool-wear monitoring based on continuous hidden Markov models[M]. Berlin Heidelberg,Springer,2005:880-890
    [2] Li W, Fu P, Cao W. Tool wear states recognition based on frequency-band energy analysis and fuzzy clustering[C]// 2010 Third International Workshop on Advanced Computational Intelligence,Chengdu,2010
    [3] Teti R, Jemielniak K, O'Donnell G, et al. Advanced monitoring of machining operations[J]. CIRP Annals-Manufacturing Technology,2010,59(2):717-739
    [4] Roth J T, Djurdjanovic D, Yang X, et al. Quality and inspection of machining operations: tool condition monitoring[J]. Journal of Manufacturing Science and Engineering,2010,132(4)
    [5] 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
    [6] Snr D, Dimla E. Sensor signals for tool-wear monitoring in metal cutting operations-a review of methods[J]. International Journal of Machine Tools and Manufacture,2000,40(8):1073-1098
    [7] 王玫,吕俊杰,王杰.基于连续高斯密度混合HMM的刀具磨损状态监测[J].四川大学学报,2010,(3):240-245 Wang M, Lv J J, Wang J. Tool wear condition monitoring based on continuous Gaussian mixture HMM[J]. Journal of Sichuan University,2010,(3):240-245 (in Chinese)
    [8] Zhu K, Wong Y S, Hong G S. Multi-category micro-milling tool wear monitoring with continuous hidden Markov models[J]. Mechanical Systems and Signal Processing,2009,23(2):547-560
    [9] Ertunc H M, Loparo K A, Ocak H. Tool wear condition monitoring in drilling operations using hidden Markov models (HMMs)[J]. International Journal of Machine Tools and Manufacture,2001,41(9):1363-1384
    [10] Rabiner L. A tutorial on hidden Markov models and selected applications in speech recognition[J]. Proceedings of the IEEE,1989,77(2):257-286
    [11] Li X, Parizeau M, Plamondon R. Training hidden markov models with multiple observations-a combinatorial method[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(4):371-377
    [12] Li X, Lim B S, Zhou J H, et al. Fuzzy neural network modelling for tool wear estimation in dry milling operation[C]//Annual Conference of the Prognostics and Health Management Society,2009
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出版历程
  • 收稿日期:  2013-03-28
  • 刊出日期:  2015-03-05

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