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铣刀破损功率监控方法的研究 被引量:3

Research on the power monitoring method for milling cutter failure
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摘要 对铣削过程中机床主轴电机功率的变化特征进行研究,提出一种实时检测铣刀破损的方法。该方法利用同频采样对功率信号进行采样,采样值进行齿周期平均预处理,然后进行一次差分和二次差分以及归一化处理。其中,一、二次差分运算用于识别刀具的破损,解决目前刀具监控中的实时性问题;归一化处理使报警门限设定不受切削条件变化的影响。 The characteristics of the variation of machine spindle motor power in milling process are studied.A method of real-time monitoring milling cutter failure has been presented.The method samples the signal of power using synchronized sampling technique.The values of sampled singnal are averaged per tooth as the preprocessing,then the first differential and second differential of the average values are calculated,and normalized processing is performed.The first and second differential are used to recognize the breakage of milling cutter and can solve the real-time problems in cutter monitoring.The normalized processing makes the setting of the threshold for alarm independent from variation of cutting conditions.
出处 《清华大学学报(自然科学版)》 EI CAS CSCD 北大核心 1994年第2期40-45,共6页 Journal of Tsinghua University(Science and Technology)
关键词 切削刀具 破损 监控 铣刀 cutting tool breakage monitor on-line
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参考文献3

  • 1梁木养,1991年
  • 2万军,1991年
  • 3团体著者,金属切削理论与实践.中,1985年

同被引文献18

  • 1陈统坚,王卫平,周泽华.铣削过程的约束型智能控制研究[J].华南理工大学学报(自然科学版),1994,22(4):90-96. 被引量:8
  • 2艾长胜,王宝光,董全成,何光伟.基于声信号HMM的刀具磨损程度分级识别[J].组合机床与自动化加工技术,2007(7):26-29. 被引量:9
  • 3Matsumura Takashi, Usui Eiji. On-line tool wear compensation system in milling operation[ J]. Technical Paper Society of Manufacturing Engineers, 1999,172 : 1 - 6.
  • 4Ong Philip K L, Mannan M A. Experimental modelling of cutting forecs as a function of tool wear in end milling[J]. Proceedings of the 2nd International Conference on Advanced Materials Processing, 2003,438:371 -374.
  • 5Ko Tae Jo, Cho Dong Woo. Adaptive modelling of the milling process and application of a neural network for tool wear monitoring[ J]. International Journal of Advanced Manufacturing Technology, 1996, 12( 1 ) :5 - 13.
  • 6Wang G V P. Fault prognostics using dynamic wavelet neural networks [ J ]. AI EDAM-Atificial Intelligence for Engineering Design Analysis and Manufacturing, 2001,15:383 -391.
  • 7Hatzipantelis E, Murray A, Penman J. Comparing hidden markov models with artificial neural network, architectures for condition monitoring applications[ C ]//Fourth International Conference on Artificial Neural Network, 1995:369- 374.
  • 8Rabiner L R. A tutorial on hidden Markov models and selected applications in speech recognition [ J ]. Proc IEEE, 1989,77(2) : 257 -286.
  • 9Atlas L,Ostendorf M, Bernard G D. Hidden markov models for monitoring machining tool-wear[ C ]//IEEE International Conference on Acoustics, Speech, and Signal Processing, Istanbul, Turkey ,2000:3887 - 3890.
  • 10Wang W Y, Wong A K. Some new signal processing approaches for gear fault diagnosis [ C ]//International Symposium on Signal Processing and its Applications, 1999:587 - 590.

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