Wear State Recognition of NC Tool Based on Multi Sensor

被引：0
|
作者：
Xu, Yanwei [1 ]
Ma, Junda [1 ]
Xie, Tancheng [1 ]
机构：
[1] Henan Univ Sci & Technol, Sch Mechatron Engn, Luoyang 471023, Peoples R China
来源：
2017 5TH INTERNATIONAL CONFERENCE ON MECHANICAL, AUTOMOTIVE AND MATERIALS ENGINEERING (CMAME) | 2017年
基金：
中国国家自然科学基金;
关键词：
multi-sensor; tool wear; wavelet packet analysis; acoustic emission; vibration;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Based on the introduction of the principle of wavelet packet analysis, the acoustic emission sensor and vibration sensor are used to collect the tool wear signal. Then the characteristics of tool wear can be obtained by Orthogonal Experiment. The method of determination about tool wear state can be implemented. Orthogonal Experiment can be used to firstly select the signal feature through this method. Thus, it can obtain the abrasion's influence on signal and analyze other factors' influence on signal. Finally, we can find the feature that properly reflects tool wear degree which is not easy to be influenced by the other factors to improve the discrimination degree of tool wear degree.
引用
收藏
页码：209 / 213
页数：5
相关论文
共 50 条
  • [41] Tool Wear State Recognition Based on Deep Stacking Sparse Denoising Auto-encoder Network
    Guo R.
    Yu W.
    Wang G.
    Huang H.
    Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis, 2024, 44 (02): : 305 - 312
  • [42] Tool wear state recognition study based on an MTF and a vision transformer with a Kolmogorov-Arnold network
    Dong, Shengming
    Meng, Yue
    Yin, Shubin
    Liu, Xianli
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2025, 228
  • [43] Tool wear condition monitoring based on multi-sensor integration and deep residual convolution network
    Zhu, Zhiying
    Liu, Riliang
    Zeng, Yunfei
    ENGINEERING RESEARCH EXPRESS, 2023, 5 (01):
  • [44] Effects of tool wear on the machining accuracy of NC turning
    Liao, WX
    PROGRESS OF MACHINING TECHNOLOGY: WITH SOME TOPICS IN ADVANCED MANUFACTURING TECHNOLOGY, 2000, : 688 - 692
  • [45] Study on tool wear state recognition algorithm based on spindle vibration signals collected by homemade tool condition monitoring ring
    Xue, Zhongling
    Li, Liang
    Wu, Youling
    Yang, Yinfei
    Wu, Wentao
    Zou, Yuhang
    Chen, Ni
    MEASUREMENT, 2023, 223
  • [46] A SENSOR FOR THE DETECTION OF TOOL BREAKAGE IN NC MILLING
    TARNG, YS
    LEE, BY
    JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 1993, 36 (03) : 259 - 272
  • [47] Developing smart multi-sensor monitoring for tool wear in stamping process
    Shanbhag, V. V.
    Pereira, M. P.
    Voss, B.
    Ubhayaratne, I.
    Rolfe, B. F.
    38TH INTERNATIONAL DEEP DRAWING RESEARCH GROUP ANNUAL CONFERENCE (IDDRG 2019), 2019, 651
  • [48] Cutting state monitoring of NC machine tool based on modal interval
    Xie, Fengyun
    Chen, Hongnian
    Jiang, Weiwen
    Xie, Sanmao
    Li, Yong
    Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument, 2017, 38 (12): : 2900 - 2907
  • [49] Intelligent Recognition Method of Turning Tool Wear State Based on Information Fusion Technology and BP Neural Network
    Xu, Yanwei
    Gui, Lin
    Xie, Tancheng
    SHOCK AND VIBRATION, 2021, 2021
  • [50] Tool wear prediction using multi-sensor data fusion and attention-based deep learning
    Kumar, Anuj
    Vasu, Velagapudi
    PRODUCTION ENGINEERING-RESEARCH AND DEVELOPMENT, 2024,
12345