IDENTIFICATION OF CUTTING CHATTER THROUGH DEEP LEARNING AND CLASSIFICATION

被引:14
作者
Gao, H. N. [1 ]
Shen, D. H. [1 ]
Yu, L. [1 ]
Zhang, W. C. [1 ]
机构
[1] Huanghuai Univ, Coll Mech & Energy Engn, Zhumadian 463000, Peoples R China
关键词
Cutting Chatter; Chatter Identification; Deep Residual Convolutional Neural Network (DR-CNN); Support Vector Machine (SVM); Variational Mode Decomposition (VMD); STABILITY; PREDICTION; MODEL; TOOL; RECOGNITION; PERFORMANCE; SVM;
D O I
10.2507/IJSIMM19-4-CO16
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The traditional analytical method has difficulty in accurately modelling cutting chatter. This paper constructs the vibration datasets of different chatter states and establishes a machine learning (ML) model for chatter identification, treating physical vibration signal as the input. Specifically, the cutting vibration signal was converted into the time-frequency spectrum, which was then classified by a self-designed deep residual convolutional neural network (DR-CNN). After that, the cutting vibration signal was broken down into chatter bands through variational mode decomposition (VMD). The information entropies of the chatter bands were calculated as cutting chatter features. Next, support vector machine (SVM) was introduced to classify the extracted features and used to create an online cutting chatter identification algorithm. The proposed method achieved a much higher mean identification accuracy (92.57 %) than the traditional identification method.
引用
收藏
页码:667 / 677
页数:11
相关论文
共 26 条
[1]  
Afify Heba M., 2020, J. Syst. Manage. Sci., V10, P53, DOI DOI 10.33168/JSMS.2020.0204
[2]   Identification of dynamic cutting force coefficients and chatter stability with process damping [J].
Altintas, Y. ;
Eynian, M. ;
Onozuka, H. .
CIRP ANNALS-MANUFACTURING TECHNOLOGY, 2008, 57 (01) :371-374
[3]   Optimal Energy Resources Allocation Method of Wireless Sensor Networks for Intelligent Railway Systems [J].
Bin, Sheng ;
Sun, Gengxin .
SENSORS, 2020, 20 (02)
[4]   A new cutting force model for predicting stability of interrupted turning [J].
Chen, L. H. ;
Xu, Y. W. ;
Zhang, L. H. ;
Man, J. .
MATERIALS RESEARCH INNOVATIONS, 2015, 19 :108-110
[5]   Analysis of acoustic emission in chatter vibration with tool wear effect in turning [J].
Chiou, RY ;
Liang, SY .
INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, 2000, 40 (07) :927-941
[6]  
Cui Z., 2019, INSTRUMENTATION MESU, V18, P289, DOI [10.18280/i2m.180309, DOI 10.18280/I2M.180309]
[7]   Histogram of oriented gradients based off-line handwritten devanagari characters recognition using SVM, K-NN and NN classifiers [J].
Deore S.P. ;
Pravin A. .
Revue d'Intelligence Artificielle, 2019, 33 (06) :441-446
[8]   Tool wear condition monitoring in drilling operations using hidden Markov models (HMMs) [J].
Ertunc, HM ;
Loparo, KA ;
Ocak, H .
INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, 2001, 41 (09) :1363-1384
[9]   Chatter Stability of General Turning Operations With Process Damping [J].
Eynian, M. ;
Altintas, Y. .
JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME, 2009, 131 (04) :0410051-04100510
[10]   GKP Signal Processing Using Deep CNN and SVM for Tongue-Machine Interface [J].
Gorur, Kutlucan ;
Bozkurt, Mehmet Recep ;
Bascil, Muhammet Serdar ;
Temurtas, Feyzullah .
TRAITEMENT DU SIGNAL, 2019, 36 (04) :319-329