Volume 37 Issue 3
Mar.  2011
Turn off MathJax
Article Contents
Nie Peng, Chen Xin. Prediction of tool VB value based on PCA and BP neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2011, 37(3): 364-367,373. (in Chinese)
Citation: Nie Peng, Chen Xin. Prediction of tool VB value based on PCA and BP neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2011, 37(3): 364-367,373. (in Chinese)

Prediction of tool VB value based on PCA and BP neural network

  • Received Date: 20 Apr 2010
  • Publish Date: 31 Mar 2011
  • Five layers of wavelet decomposition was applied on acoustic emission signals for extracting the acoustic emission(AE) signals energy value of six bands. Energy value and cutting speed, cutting depth, feed rate, cutting time were turned into state feature vectors of tool wear. The principal component analysis was used to reduce dimension and eliminate the correlation between the feature vectors. The principal components were seen as back propagation(BP) neural network input vector. Improved Levenberg-Marquart (LM) algorithm was used to BP neural network for learning, input vectors were trained for BP neural network. Then, the BP neural network would realize the forecast of tool flank wear VB value. The results indicate that the VB value forecast system based on principal component analysis (PCA) and the improved BP neural network with LM algorithm can accurately predict the tool flank wear VB value within the error range 0.03. The different states of tool wear can be judged according to the VB value.

     

  • loading
  • [1] 康晶,冯长建,胡红英.刀具磨损监测及破损模式的识别[J].振动、测试与诊断,2009,29(1):5-9 Kang Jing,Feng Changjian,Hu Hongying.Tool wear monitoring and pattern recognition of tool failure[J].Journal of Vibration,Measurement & Diagnosis,2009,29(1):5-9(in Chinese) [2] Tamura M,Tsujita S.A study on the number of principal components and sensitivity of fault detection using PCA[J].Computers and Chemical Engineering,2007,31(9):1035-1046 [3] Balazinski M,Czogala E,Jemielniak K,et al.Tool condition monitoring using artificial intelligence methods[J].Engineering Applications of Artificial Intelligence ,2002,15:73-80 [4] 郑金兴,张铭钧,孟庆鑫.多传感器数据融合技术在刀具状态监测中的应用[J].传感器与微系统,2007,26(4):90-93 Zheng Jinxing,Zhang Mingjun,Meng Qingxin.Application of multi-sensor data fusion in tool wear monitoring[J].Transducer and Microsystem Technologies,2007,26(4):90-93(in Chinese) [5] Li R Y,Rong G.Fault isolation by partial dynamic principal component analysis in dynamic process[J].Chinese Journal of Chemical Engineering,2006,14(4):486-493 [6] 朱松青,史金飞.状态监测与故障诊断中的主元分析法[J].机床与液压,2007,35(1):241-243 Zhu Songqing,Shi Jinfei.PCA approach to condition monitoring and fault diagnosis[J].Machine Tool & Hydraulics,2007,35(1):241-243(in Chinese) [7] Ghosh N,Ravi Y B,Patra A,et al.Estimation of tool wear during CNC milling using neural network-based sensor fusion [J].Mechanical Systems and Signal Processing,2007,21(1):466-479 [8] 张德丰.MATLAB神经网络仿真与应用[M].北京:电子工业出版社,2009:168-174 Zhang Defeng.MATLAB neural network simulator and application [M].Beijing:Electronics Industry Press,2009:168-174(in Chinese)
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views(3119) PDF downloads(814) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return