Tool Wear Online Monitoring Based on Multi-sensor Information Decision-making Level Fusion

doi:10.3969/j.issn.1000-1093.2021.09.023

Abstract

Abstract: An online dynamic monitoring model of tool wear based on multi-sensor information decision-making level fusion is proposed for accurately controlling the tool wear state in the machining process. After extracting the time, frequency and time-frequency features from the collected vibration, force and acoustic emission sensor signals, the monitoring model divides the sensor signal features into independent samples according to the sensor type. The same tool wear extent is regressively predicted using the independent samples, respectively. Then the tool wear extent predicted from the signal characteristics of each sensor is comprehensively determined. Finally, the tool wear extent is determined. The experimental results show that the on-line dynamic monitoring model of tool wear can effectively improve the accuracy of tool wear dynamic prediction, and the average prediction accuracy is 97.9%. Compared with existing research methods, the proposed method is used to increase the prediction accuracy rate by at least 4%, and the prediction time is only 0.016 s.

Key words: toolwear, onlinemonitoring, informationdecision-makinglevelfusion, multi-layerneuralnetwork, featureextraction

CLC Number:

TG702

LI Heng, YE Zukun, ZHA Wenbin, WANG Yulin. Tool Wear Online Monitoring Based on Multi-sensor Information Decision-making Level Fusion[J]. Acta Armamentarii, 2021, 42(9): 2024-2031.

References

［1］王凯，孙剑飞，杜大喜，等.硬质合金刀具月牙洼磨损定量化实验研究［J］.兵工学报,2017,38(8): 1578-1585.
WANG K,SUN J F,DU D X,et al.Quantitative experimental ana-lysis on crater wear of cemented carbide tools［J］.Acta Armamentarii,2017,38(8):1578-1585.(in Chinese)
［2］ CHEN N,HAO B J,GUO Y L,et al.Research on tool wear monitoring in drilling process based on APSO-LS-SVM approach［J］. International Journal of Advanced Manufacturing Technology, 2020, 108: 2091-2101.
［3］金成哲，贾春德，庞思勤，等.正交车铣高强度钢刀具磨损的研究［J］.兵工学报,2005,26(3):397-400.
JIN C Z,JIA C D,PANG S Q,et al.Wearing mechanism of tools in the orthogonal turn-milling of high strength steel［J］.Acta Armamentarii,2005,26(3):397-400.(in Chinese)
［4］ PENG R T,PANG H L,JIANG H J,et al.Study of tool wear monitoring using machine vision［J］.Automatic Control and Computer Sciences,2020,54(3):259-270.
［5］ SHI X C,WANG X B,JIAO L,et al.A real-time tool failure monitoring system based on cutting force analysis［J］.The International Journal of Advanced Manufacturing Technology,2018, 95：2567-2583.
［6］张锴锋,袁惠群,聂鹏.基于广义维数与优化BP神经网络的刀具磨损量预测［J］.东北大学学报(自然科学版),2013,34(9): 1292-1295.
ZHANG K F,YUAN H Q,NIE P.Prediction of tool wear based on generalized dimensions and optimized BP neural network［J］.Journal of Northeastern University(Natural Science), 2013,34(9): 1292-1295. (in Chinese)
［7］ PETER M,KATARINA M,MAREK U,et al. Vibrodiagnostics as the tool of a tap wear monitoring［J］.Procedia Structural Integrity,2018,13:959-964.
［8］ AKBARI A,DANESH M,KHALILI K.A method based on spindle motor current harmonic distortion measurements for tool wear monitoring［J］.Journal of The Brazilian Society of Mechanical Sciences and Engineering, 2017,39:5049-5055.
［9］ FU P,HOPE A D,GAO H L.A neural-fuzzy pattern recognition algorithm based cutting tool condition monitoring procedure［C］∥Proceedings of the 3rd International Conference on Natural Computation. Haikou, China: IEEE, 2007.
［10］ DUO A,BASAGOITI R,ARRAZOLA P J,et al.The capacity of statistical features extracted from multiple signals to predict tool wear in the drilling process［J］.The International Journal of Advanced Manufacturing Technology, 2019,102: 2133-2146.
［11］ HUANG Z W, ZHU J M, LEI J T, et al. Tool wear predicting based on multi domain feature fusion by deep convolutional neural network in milling operations［J］. Journal of Intelligent Manufacturing,2020,31(4): 953-966.
［12］ ZHAO R,WANG J J,YAN R Q,et al.Machine health monitoring with LSTM networks［C］∥Proceedings of the 2016 10th International Conference on Sensing Technology. Nanjing, China：IEEE, 2016.
［13］ ZHANG K F,YUAN H Q,NIE P.A method for tool condition monitoring based on sensor fusion［J］. Journal of Intelligent Manufacturing,2015,26(5):1011-1026.
［14］ LI X H,LIM B S,ZHOU J H, et al. Fuzzy neural network modelling for tool wear estimation in dry milling operation［C］∥Proceedings of the Annual Conference of the Prognostics and Health Management Society. Montreal,QC,Canada: the Prognostics and Health Management Society, 2009.
［15］曹大理,孙惠斌,张纪铎,等.基于卷积神经网络的刀具磨损在线监测［J］.计算机集成制造系统,2020,26(1):74-80.
CAO D L,SUN H B,ZHANG J D,et al.In-process tool condition monitoring based on convolution neural network［J］.Computer Integrated Manufacturing Systems,2020,26(1):74-80. (in Chinese)

[1]	QIN Guohua， GAO Jie， YE Haichao， JIANG Guojie， HUANG Shuai， LAI Xiaochuun. Tool Wear Prediction Based on Fusion Evaluation Index and Neural Network [J]. Acta Armamentarii, 2021, 42(9): 2013-2023.
[2]	WANG Cheng， WU Yan， YANG Tingfei. Improving the Detection Accuracy of Wake Targets by Improved One-class Support Vector Machine [J]. Acta Armamentarii, 2020, 41(9): 1887-1893.
[3]	YU Yongwei， PENG Xi， DU Liuqing， CHEN Tianhao. Real-time Detection of Parts by Assembly Robot Based on Deep Learning Framework [J]. Acta Armamentarii, 2020, 41(10): 2122-2130.
[4]	TONG Rui, KANG Jianshe, SUN Jian, YANG Wen, LI Baochen. Gear Performance Degradation Feature Extraction Based on Local Characteristic-scale Decomposition and Modified CompositeSpectrum Analysis [J]. Acta Armamentarii, 2019, 40(5): 1093-1102.
[5]	DAI Jian, LI Ze, HAO Xinhong, LI Ping. Anti-jamming Method for Pulse Doppler Fuze Based on Joint Feature Extraction of Target Signal [J]. Acta Armamentarii, 2019, 40(2): 225-233.
[6]	YANG Da-wei， ZHAO Yong-dong， FENG Fu-zhou， JIANG Peng-cheng， DING Chuang. Planetary Gearbox Fault Feature Extraction Based on Parameter Optimized Variational Mode Decomposition and Partial Mean ofMulti-scale Entropy [J]. Acta Armamentarii, 2018, 39(9): 1683-1691.
[7]	DING Chuang, FENG Fu-zhou, ZHANG Bing-zhi, WU Shou-jun. Feature of Linear Quantum Information Entropy for Vibration Signals of Planetary Transmission Gearbox [J]. Acta Armamentarii, 2018, 39(12): 2306-2312.
[8]	LI Guan-fang, CUI Jie, YUAN Fu-yu. Passive Sonar Target Motion Analysis Based on Line-spectrum Instantaneous Frequency Estimation [J]. Acta Armamentarii, 2017, 38(7): 1395-1401.
[9]	FENG Hai-chao， ZHAO Zhi-yong， ZHANG Jin-yuan， PAN Guo-qiang. An Evaluation Method for Fusion Feature of Digital Pattern Painting Based on Super-efficiency DEA Model [J]. Acta Armamentarii, 2017, 38(11): 2214-2219.