基于信息融合的刀具磨损状态智能识别

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振动与冲击 ›› 2017, Vol. 36 ›› Issue (21) : 257-264.

论文

基于信息融合的刀具磨损状态智能识别

徐彦伟1 陈立海1 袁子皓1,2 颉潭成1

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Intelligent Recognition of Tool Wear Condition Based on Information Fusion

Xu Yanwei1 Chen Lihai1 Yuan Zihao1,2 Xie Tancheng1

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摘要

采集声发射和振动加速度信号，搭建了数控车床刀具磨损状态多信息数据采集系统；用正交试验法采集相关数据并分析了不同切削条件、不同刀具磨损程度下数控车削加工过程中的声发射和振动信号；用小波包分解法提取了声发射和振动信号的最佳特征频段作为识别刀具磨损的特征参量，采用BP神经网络将数控切削过程中刀具磨损的声发射与振动信号特征信息进行融合，研究了数控车削刀具磨损状态的智能识别技术。

Abstract

The multi-information data acquisition system of tool wear condition of CNC lathe is built by acquiring the acoustic emission and vibration acceleration signals. The data of acoustic emission and vibration acceleration signals during the process of CNC machine tool processing under the conditions of different tool wear degree and different cutting condition is acquired and analyzed using the orthogonal experimental method. The optimum characteristic frequency band of acoustic emission and vibration acceleration signals were extracted by the wavelet envelope decomposition method so as to recognize tool wear condition as the characteristic parameters. The characteristic information of acoustic emission and vibration acceleration signals during the process of CNC machine tool processing were fused. And the intelligent recognition of tool wear condition during the process of machine tool processing was researched.

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徐彦伟1 陈立海1 袁子皓1,2 颉潭成1. 基于信息融合的刀具磨损状态智能识别[J]. 振动与冲击, 2017, 36(21): 257-264

Xu Yanwei1 Chen Lihai1 Yuan Zihao1,2 Xie Tancheng1 . Intelligent Recognition of Tool Wear Condition Based on Information Fusion[J]. Journal of Vibration and Shock, 2017, 36(21): 257-264

参考文献

1 任建平，白恩远，王俊元，等. 现代数控机床故障诊断与维修[M]. 北京: 国防工业出版社，2002.
Ren Jianping, Bai Enyuan, Wang Junyuan, et al. Fault diagnosis and maintenance of modern CNC machine tools [M]. Beijing, National Defence Industry Press, 2002.
2 徐玲，杨丹，王时龙，等. 基于进化神经网络的刀具寿命预测[J]. 计算机集成制造系统，2008, 14(1): 167-171.
Xu Ling, Yang Dan, Wang Shilong, et al. Prediction of cutting tool life based on evolutionary neural network [J]. Computer Integrated Manufacturing Systems, 2008, 14(1): 167-171.
3 朱坚民，战汉，张统超，等. 基于切削声发射信号测量的刀具磨损状态判别[J]. 计量学报，2015, 36(3):268-272.
Zhu Jianmin, Zhan Han, Zhang Tongchao, et al. Tool wear state recognition based on cutting acoustic emission signal measurement [J]. Acta Metrologica Sinica, 2015, 36(3):268-272. 4 张锴锋，袁惠群，聂鹏. 基于切削声信号与优化SVM的刀具磨损状态监测[J]. 振动、诊断与测试，2015, 35(4):727-732.
Zhang Kaifeng, Yuan Huiqun, Niepeng. Tool wear condition monitoring based on cutting sound signal and optimized SVM [J]. Journal of Vibration, Measurement & Diagnosis2015, 35(4):727-732.
5 李鹏阳，郝重阳，祝双武. 基于图像连通区域数的刀具磨损状态特征提取实验研究[J]. 中国图象图形学报，2008, 13(8):1476-1480.
Li Pengyang, Hao Chongyang, Zhu Shuangwu. Experimental studies on feature extraction of tool wear condition based on image connected components integer [J]. Journal of Image and Graphics, 2008, 13(8):1476-1480.
6 谢厚正，黄民. 基于振动测试的数控机床刀具磨损监测方法[J]. 仪表技术与传感器，2008, (2):73-76.
Xie Houzheng, Huang Min. Research of numerical control machine tools wear monitoring method based on vibration testing[J]. Instrument Technique and Sensor, 2008, (2):73-76.
7 Nouri, Mehdi; Fussell, Barry K.; Ziniti, Beth L.; et al. Real-time tool wear monitoring in milling using a cutting condition independent method [J]. International Journal of Machine Tools & Manufacture, 2015, 89:1-13.
8 徐创文，陈花玲，刘彦国，等. 铣削刀具不同磨损期振动信号的分维特征[J]. 农业机械学报，2007, 38(6):164-168.
Xu Chuangwen, Chen Hualing, Liu Yanguo, et al. Fractal characteristic of vibration signals in different milling tool wear periods[J]. Transactions of the Chinese Society for Agricultural Machinery, 2007, 38(6):164-168.
9 关山，彭昶, 刀具磨损声发射信号的混沌特性分析[J]. 农业工程学报，2015, 31(11):60-65.
Guan Shan, Peng Chang. Chaotic characteristic analysis of tool wear acoustic emission signal [J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(11):60-65.
10 谢春燕，吴达科，王朝勇，等. 基于图像和光谱信息融合的病虫害叶片检测系统[J]. 农业机械学报，2013, 44(Supp1):269-272.
Xie Chunyan, Wu Dake, Wang Chaoyong, et al. Insect pest leaf detection system based on information fusion of image and spectrum [J]. Transactions of the Chinese Society for