Login Register Cart 0
Generic placeholder image

Recent Patents on Engineering

Editor-in-Chief

ISSN (Print): 1872-2121
ISSN (Online): 2212-4047

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Review Article

Advances in Research on Tool Wear Online Monitoring Method

Author(s): Xitong Wu, Guohe Li*, Zhihua Shao, Weijun Liu and Ganzhong Ma

Volume 18, Issue 6, 2024

Published on: 13 September, 2023

Article ID: e100723218612 Pages: 16

DOI: 10.2174/1872212118666230710161401

Price: $65

Abstract

Background: With the continued advancement of industrial internet technology, mechanical manufacturing is increasingly developing towards automation and intelligence. As a result, monitoring the manufacturing process has become an essential requirement for intelligent manufacturing. As one of the fundamental components of cutting processes, tools are inevitably subject to wear and damage during use. Therefore, tool wear monitoring plays a crucial role in modern manufacturing.

Introduction: With the development of the manufacturing industry, the requirement for automation manufacturing is higher and higher. In the process of automatic processing, unmanned processing and adaptive processing, it is not only required to be able to know the accurate wear state of the tool in the process real-time but also required to change the milling parameters according to the wear state of the tool, in order to optimize the productivity and processing quality. The tool monitoring system can effectively reduce the operating cost of workshop production and improve the reliability of intelligent workshop and flexible production lines.

Method: This article summarizes commonly used online monitoring methods mentioned by articles and patents, such as cutting force, vibration, acoustic emission, temperature, current, and power signals. Each monitoring method is analyzed in terms of its principles, advantages and disadvantages, signal acquisition equipment, and research status. The article also identifies current issues and future development directions.

Results: As modern manufacturing technology continues to develop rapidly, unmanned factories have become a significant feature of the manufacturing industry. Consequently, the need for tool wear condition monitoring technology is becoming increasingly urgent. Although tool condition monitoring technology has made significant progress over the past twenty years and has been applied in actual production, several issues need to be addressed to make tool wear condition monitoring systems mo.

Conclusion: This serves as a reference for theoretical research and application of online monitoring of tool wear in intelligent manufacturing systems.

Graphical Abstract

[1]
C.F. Liu, Research on key technology of side milling of titanium thin-walled parts., Northeastern University: Shenyang, 2020.
[2]
J.L. Dong, and Y.B. Dai, "Review of tool wear state identification and intelligent monitoring methods", Fan Technology, vol. 61, no. 6, pp. 67-73, 2019.
[3]
B. Peng, T. Bergs, D. Schraknepper, F. Klocke, and B. Döbbeler, "A hybrid approach using machine learning to predict the cutting forces under consideration of the tool wear", Procedia CIRP, vol. 82, pp. 302-307, 2019.
[http://dx.doi.org/10.1016/j.procir.2019.04.031]
[4]
K. Sato, "Feedforward element design using learning controller for precision control of linear synchronous motor with nonlinear characteristics", Precis. Eng., vol. 72, pp. 870-877, 2021.
[http://dx.doi.org/10.1016/j.precisioneng.2021.08.005]
[5]
S. Bombiński, J. Kossakowska, and K. Jemielniak, "Detection of accelerated tool wear in turning", Mech. Syst. Signal Process., vol. 162, p. 108021, 2022.
[http://dx.doi.org/10.1016/j.ymssp.2021.108021]
[6]
M.U. Dianfang, L.I.U Xianli, Y.U.E. Caixu, L.I.U. Qiang, B.A.I. Zhengyan, S.Y. LIANG, and D.I.N.G. Yunpeng, "On-line tool wear monitoring based on machine learning", J. Adv. Manufacturing Sci. Tech., vol. 1, no. 2, pp. 2021002-1, 2021.
[http://dx.doi.org/10.51393/j.jamst.2021002]
[7]
Z.Y. Liu, and P.F. Ma, "Online monitoring of tool wear status in machining process based on machine tool informatiom", China Mechanical Engg., vol. 30, no. 2, pp. 220-225, 2019.
[8]
Z.X. Jiang, J. Sun, and G.C. Li, "Study on the relationship between tool wear and cutting force and vibration in TC4 milling process", Acta Armamentarii, vol. 36, no. 001, pp. 144-150, 2015.
[9]
X. Q. Wang, Y. Zhang, and H. M. Zhou, "“Continuous monitoring of tool wear based on hidden Markov model”", Modular Mach. Tool & Autom. Mfg. Tech., 2016(10): 87-90.
[http://dx.doi.org/10.13462/j.cnki.mmtamt.2016.10.023.]
[10]
A. Ghasempoor, J. Jeswiet, and T.N. Moore, "Real time implementation of on-line tool condition monitoring in turning", Int. J. Mach. Tools Manuf., vol. 39, no. 12, pp. 1883-1902, 1999.
[http://dx.doi.org/10.1016/S0890-6955(99)00035-8]
[11]
C. Chungchoo, and D. Saini, "The total energy and the total entropy of force signals — new parameters for monitoring oblique turning operations", Int. J. Mach. Tools Manuf., vol. 40, no. 13, pp. 1879-1897, 2000.
[http://dx.doi.org/10.1016/S0890-6955(00)00032-8]
[12]
J.M. Zheng, Y. Li, and P.Y. Li, "Tool wear monitoring based on time-frequency domain feature fusion of cutting force signal", Machinery & Electronics, no. 03, pp. 46-48, 2001.
[13]
C.Q. Xi, Research on drill bit wear condition monitoring technology based on drilling force signal., Xi'an University of Technology, 2005.
[14]
M.C. Cakir, and Y. Isik, "Detecting tool breakage in turning AISI 1050 steel using coated and uncoated cutting tools", J. Mater. Process. Technol., vol. 159, no. 2, pp. 191-198, 2005.
[http://dx.doi.org/10.1016/j.jmatprotec.2004.05.006]
[15]
Q.R. Liu, Research on Tool Wear State Recognition Based on Milling Force Signal., Dalian Jiaotong University, 2007.
[16]
N. Fan, P.Q. Guo, and H. Wang, "Research on spectrum characteristics of cutting force in tool wear process", Modular Machine Tool & Automatic Manufacturing Technique, no. 05, pp. 69-71, 2008.
[17]
Y. Yang, "Tool wear state monitoring based on cutting force wavelet neural network", Machine Tool & Hydraulics, vol. 37, no. 07, pp. 250-269, 2009.
[18]
S. Huang, Research on Intelligent Monitoring of Tool Wear and Cutting Force Prediction Technology in Cutting Process., Southwest Jiaotong University, 2010.
[19]
G.T. Luo, Dynamic Cutting Force Feature Extraction and Surface Integrity Prediction in Hard Turning Process., Harbin University of Science and Technology, 2010.
[20]
M. Rizal, and J.A. Ghani, "The application of i-kaz tm –based method for tool wear monitoring using cutting force signal", Procedia Eng., vol. 68, p. 68, 2013.
[21]
Y.J. Sun, Parameter Modeling and Tool Wear State Prediction of Titanium Alloy Milling Process., Shandong University, 2014.
[22]
D. Wang, Research on On-line Monitoring of Tool Wear Based on Milling Force., Huazhong University of Science and Technology, 2015.
[23]
Z. Liao, D. Gao, Y. Lu, and Z. Lv, "Multi-scale hybrid HMM for tool wear condition monitoring", Int. J. Adv. Manuf. Technol., vol. 84, no. 9-12, pp. 2437-2448, 2016.
[http://dx.doi.org/10.1007/s00170-015-7895-3]
[24]
T. Xu, Tool Condition Monitoring System Based on Main Cutting Force Simulation Sample., Nanjing University of Aeronautics and Astronautics, 2018.
[25]
G. Michau, Y. Hu, T. Palmé, and O. Fink, "Feature learning for fault detection in high-dimensional condition monitoring signals", Proc. Inst. Mech. Eng. O. J. Risk Reliab., vol. 234, no. 1, pp. 104-115, 2020.
[http://dx.doi.org/10.1177/1748006X19868335]
[26]
Y.Q. Zhou, Research on Condition Monitoring and Remaining Effective Life Prediction of End Milling Cutter., Zhejiang University of Technology, 2020.
[27]
F.C. Wang, Research on Tool Life and Cutting Parameters Optimization of Fine Turning Large Pitch Thread., Harbin University of Science and Technology, 2021.
[28]
J.M. Du, Research on Intelligent on-line monitoring of tool wear condition., Lanzhou Institute of Technology of Gansu Province, 2021.
[29]
Q. Wang, Wear State Recognition and Remaining Life Estimation of Ball End Milling Cutters Based on Machine Learning., Lanzhou University of Technology, 2022.
[30]
X. Yang, R. Yuan, Y. Lv, L. Li, and H. Song, "A novel multivariate cutting force-based tool wear monitoring method using one-dimensional convolutional neural network", Sensors, vol. 22, no. 21, p. 8343, 2022.
[http://dx.doi.org/10.3390/s22218343] [PMID: 36366041]
[31]
K. Zhang, Intelligent Control System for Milling Based on Real-time Monitoring of Cutting Force., Shandong University, 2022.
[32]
D.L. Dong, Research on Tool Wear Monitoring Technology Based on Data Drive., University of Jinan, 2022.
[33]
J. F. Sun, S. H. Ma, and H. T. Ding, "An online monitoring method for tool wear based on cutting force", C. N Patent 110576335B .
[34]
O.B. Abouelatta, and J. Mádl, "Surface roughness prediction based on cutting parameters and tool vibrations in turning operations", J. Mater. Process. Technol., vol. 118, no. 1-3, pp. 269-277, 2001.
[http://dx.doi.org/10.1016/S0924-0136(01)00959-1]
[35]
D.E. Dimla, "Snr. The correlation of vibration signal features to cutting tool wear in a metal turning operation", Int. J. Adv. Manuf. Technol., vol. 19, no. 10, pp. 705-713, 2002.
[http://dx.doi.org/10.1007/s001700200080]
[36]
C.W. Xu, and H.L. Chen, "Fractal dimension characteristics of vibration signals of milling tools at different wear periods", Nongye Jixie Xuebao, no. 06, pp. 164-168, 2007.
[37]
C.W. Xu, H.L. Cheng, and L.M. Liu, "The fractal characteristic of vibration signals in different milling tool wear periods", In: Lanzhou. Polytechnical, College. (China): Xi'an. Jiaotong. Univ., 2008.
[38]
M. Wang, and D.F. Gao, "Wear state identification of milling cutter based on vibration signal", Manufacturing Automation, vol. 32, no. 12, pp. 96-99, 2010.
[39]
M. Elangovan, V. Sugumaran, K.I. Ramachandran, and S. Ravikumar, "Effect of SVM kernel functions on classification of vibration signals of a single point cutting tool", Expert Syst. Appl., vol. 38, no. 12, pp. 15202-15207, 2011.
[http://dx.doi.org/10.1016/j.eswa.2011.05.081]
[40]
X.Y. Wang, L. Long, and M. Guo, "Research on tool condition monitoring technology based on vibration signal", J. Nanchang Hangkong University, vol. 25, no. 03, pp. 42-47, 2011.
[41]
Z.H. Ren, Research on PCB Microdrill Tool Wear Condition Monitoring Based on Vibration Signal., Shanghai Jiao Tong University, 2012.
[42]
W. Rmili, A. Ouahabi, R. Serra, and R. Leroy, "An automatic system based on vibratory analysis for cutting tool wear monitoring", Measurement, vol. 77, no. 77, pp. 117-123, 2016.
[http://dx.doi.org/10.1016/j.measurement.2015.09.010]
[43]
Y. Fu, Research on Intelligent Monitoring Technology of Vibration State and Tool Wear in Cutting Process., Huazhong University of Science and Technology, 2017.
[44]
N. Ambhore, D. Kamble, and S. Chinchanikar, "Behaviour of cutting tool vibrations with the progress of tool wear in turning hardened AISI 52100 steel: An approach to tool condition monitoring system", IOP Conf. Series Mater. Sci. Eng., vol. 455, no. 1, p. 012062, 2018.
[http://dx.doi.org/10.1088/1757-899X/455/1/012062]
[45]
B. Li, Tool Wear State Recognition of Gear Hobbing Machine Based on Vibration Signal Analysis., Chongqing University, 2020.
[46]
S. Swain, and I. Panigrahi, "Effect of tool vibration on flank wear and surface roughness during high-speed machining of 1040 steel", J. Fail. Anal. Prev., vol. 20, pp. 976-994, 2020.
[47]
Z.W. Huang, and J.M. Zhu, "Tool wear monitoring with vibration signals based on short-time fourier transform and deep convolutional neural network in milling", Math. Probl. Eng., vol. 2021, pp. 1-14, 2021.
[48]
M.L. Bouhalais, and M. Nouioua, "The analysis of tool vibration signals by spectral kurtosis and ICEEMDAN modes energy for insert wear monitoring in turning operation", J. Adv. Manuf. Technol, vol. 115, no. 9-10, pp. 2989-3001, 2021.
[49]
T. Li, Research on Tool Wear Condition Monitoring and Remaining Life Prediction., East China Jiaotong University, 2022.
[50]
N. Kyumin, and Y. Heonjun, "PERL:Probabilistic energy-ratiobased localization for boiler tube leaks using descriptors of acoustic emission signals", Reliab. Eng. Syst. Saf., vol. 230, p. 108923, 2023. neural network in milling", Math. Probl. Eng, vol. 2021.
[51]
T. J. Zhu, W. Z. Cao, and J. Y. Wu, "Tool condition monitoring system and method based on machine tool vibration signal", C.N Patent 113894617A, 2022.
[52]
I. Marinescu, and D.A. Axinte, "A critical analysis of effectiveness of acoustic emission signals to detect tool and workpiece malfunctions in milling operations", Int. J. Mach. Tools Manuf., vol. 48, no. 10, pp. 1148-1160, 2008.
[http://dx.doi.org/10.1016/j.ijmachtools.2008.01.011]
[53]
X.C. Zhang, "Experimental research on tool wear condition monitoring of vibration drilling based on acoustic emission signal", Machine Tool & Hydraulics, vol. 48, no. 13, pp. 189-192, 2020.
[54]
C.Y. Liu, Research on Milling Cutter Wear Condition Monitoring Based on Acoustic Emission Signal and Machine Vision., Xiangtan University, 2021.
[55]
Z.M. Wang, and X.Y. Wang, "A New method for on-line Tool State Monitoring Based on Fractal Dimension", Transactions of Beijing Institute of Technology, no. 04, pp. 441-444, 2000.
[56]
X. Li, "A brief review: Acoustic emission method for tool wear monitoring during turning", Int. J. Mach. Tools Manu., vol. 42, no. 2, pp. 157-165, 2002.
[57]
J. Yang, Research on Acoustic Emission Signal Processing and Analysis Technology., Jilin University, 2005.
[58]
D.L. Wang, and P. Nie, "Selection of small and medium wave base for acoustic emission signal wavelet analysis of tool wear", China Test Technology, no. 06, pp. 112-115, 2008.
[59]
T.K. Gong, and X.Y. Wang, "High Speed Milling Process Monitoring Technology based on Acoustic Emission Signal", Aeronautical Manufacturing Technology, no. 07, pp. 80-83, 2009.
[60]
P. Chen, and X.Y. Wang, "Fault Diagnosis of High Speed Milling Tool Based on Acoustic Emission", Manufacturing Technology & Machine Tool, no. 11, pp. 22-24, 2010.
[61]
S. Guan, Tool Wear Classification and Prediction Technology Based on Acoustic Emission Signal Multi-feature Analysis and Fusion., Jilin University, 2011.
[62]
Research on Tool Wear State Based on Acoustic Emission Method., Zhengzhou University, 2011.
[63]
M. Bhuiyan, I.A. Choudhury, and Y. Nukman, "Tool condition monitoring using acoustic emission and vibration signature in turning", Lecture Notes in Engineering & Computer Science, vol. 2199, no. 1, pp. 531-538, 2012.
[64]
H. Li, Y. Wang, and S. Yang, "Operational reliability evaluation of milling cutter based on acoustic emission signal", Journal of Dalian University of Technology, vol. 54, no. 04, pp. 418-423, 2014.
[65]
S. Guan, and C. Peng, "Chaotic characteristics of tool wear signal in metal cutting process", Journal of Vibration and Shock, vol. 34, no. 10, pp. 28-33, 2015.
[66]
H.Y. Zhu, "H, B. Li. Research on Acoustic Emission Signal of TBM Tool Based on Improved CRITIC Method", Journal of Vibration and Shock, vol. 35, no. 06, pp. 197-202, 2016.
[67]
Y.Y. Xie, "Research on Tool State Recognition Based on Acoustic Emission and Deep Learning", Nanjing university of information science & technology, 2017.
[68]
H. Wu, "Tool Wear State Recognition and Prediction Based on Acoustic Emission", University of electronic science and technology of China, 2017.
[69]
X.M. Xie, "Acoustic Emission based Tool Wear Monitoring for Hole Making of Laminated Materials", Nanjing University of Aeronautics and Astronautics, 2018.
[70]
Y. Zhang, G. Yang, D. Zhang, and T. Wang, "Investigation on recognition method of acoustic emission signal of the compressor valve based on the deep learning method", Energy Rep., vol. 7, no. S7, pp. 62-71, 2021.
[http://dx.doi.org/10.1016/j.egyr.2021.10.053]
[71]
T. Liu, Y.R. Wu, and K. Zhang, "Research on metal milling process based on acoustic emission signal", Equip. Manufact. Tech, no. 12, pp. 203-206, 2022.
[72]
Z.F. Zhou, Q. Hong, and C.J. Huang, "Research on on-line monitoring of tool wear state based on acoustic emission signal analysis", Tool Technology, vol. 56, no. 12, pp. 51-55, 2022.
[73]
L. Li, and Q.S. Yan, On-line monitoring of tool wear in shear machining of iron base nanocrystalline alloy strip based on acoustic emission signal., Mechanical. & Electrical. Engineering, pp. 1-10. 2023
[74]
F.A. Al-Sulaiman, M.A. Baseer, and A.K. Sheikh, "Use of electrical power for online monitoring of tool condition", J. Mater. Process. Technol., vol. 166, no. 3, pp. 364-371, 2005.
[http://dx.doi.org/10.1016/j.jmatprotec.2004.07.104]
[75]
W. Wang, and J.S. Yao, "Tool wear modeling of profiled screw machining based on power signal", Modular Machine Tool & Automatic Manufacturing Technique, no. 07, pp. 55-57, 2006.
[76]
A. M. Bassiuny, and Xiaoli Li, "Flute breakage detection during end milling using Hilbert-Huang transform and smoothed nonlinear energy operator", Int. J. Mach. Tool. Manu: Design. Research. And. application, vol. 47, no. 6, pp. 1011-1020, 2007.
[77]
D.A. Tobon-Mejia, K. Medjaher, and N. Zerhouni, "CNC Machine Tools wear diagnostic and prognostic by using dynamic bayesian networks", Mechanical. Systems. & Signal. Processing, vol. 28, pp. 167-182, 2012.
[78]
P.P. Qiao, "Fuzzy Pattern Recognition of Tool Wear State Based on Current Signal", Tool Technology, vol. 47, no. 11, pp. 73-74, 2013.
[79]
D.L. Zhang, R. Mo, and H.B. Sun, "Tool Wear State Recognition based on Chaotic Time Sequence Analysis Method and Support Vector Machine", Jisuanji Jicheng Zhizao Xitong, vol. 21, no. 08, pp. 2138-2146, 2015.
[80]
N. Xie, F. Ma, and M.L. Duan, "Tool Wear State Monitoring based on Principal Component Analysis and C-Support Vector Machine", Journal of Tongji University, vol. 44, no. 03, pp. 434-439, 2016. [J]. [Natural Science].
[81]
N. Xie, M.L. Duan, and Y.Q. Gao, "Tool Wear Prediction Method Based on Power Sensor", Journal of Tongji University, vol. 45, no. 03, pp. 420-426, 2017. [J]. [Natural Science].
[82]
Y.L. Fu, Research and development of multi-eigenvalue tool wear monitoring system based on power signal., Southwest Jiaotong University, 2018.
[83]
W.B. Wan, J.X. Li, and Y.B. Bi, "Drilling Tool monitoring and System Development based on power Signal", Jisuanji Jicheng Zhizao Xitong, vol. 25, no. 09, pp. 2140-2148, 2019.
[84]
C.A. Zhou, Research on On-line Monitoring and Vibration Measuring Tool Shank System and Signal Singularity Analysis of Milling Tool Wear State., Shandong University, 2020.
[85]
W. Dong, X. Xiong, Y. Ma, and X. Yue, "Woodworking tool wear condition monitoring during milling based on power signals and a particle swarm optimization-back propagation neural network", Appl. Sci. (Basel), vol. 11, no. 19, p. 9026, 2021.
[http://dx.doi.org/10.3390/app11199026]
[86]
H. Ma, and Y.H. Cheng, "“Application of improved wavelet threshold denoising method in motor current signal processing”, Mechanical. &. Electrical. Engineering", Technology, vol. 51, no. 11, p. 55, 2022.
[87]
Y. Wu, "Tool wear condition monitoring based on spindle current signal multi-feature fusion", Manufacturing Technology & Machine Tool, no. 03, pp. 44-48, 2022.
[88]
K.D. Pradeep, X.C. Luo, and Y. Qin, "A novel current sensor indicator enabled WAFTR model for tool wear prediction under variable operating conditions", J. Manuf. Process., vol. 82, pp. 777-791, 2022.
[89]
Q. Y. Wang, "A tool wear prediction method for drilling process based on power signal", C.N Patent 115358044A.
[90]
W. Fan, M. Wang, and Z. Xie, "On-line monitoring of milling cutter wear based on infrared temperature", Tool. Engineering, vol. 42, no. 9, 2008.
[91]
F.X. Kong, and D.Y. Zhang, "Temperature and tool wear of micro- lubricated vibration drilling in superalloy", J. Beijing University of Aeronautics & Astronsutics, vol. 38, no. 06, pp. 849-852, 2012.
[92]
N. Ding, M.L. Zhang, and D. Guo, "Research on tool wear state based on cutting temperature analysis", Light Industry Machinery, vol. 37, no. 05, pp. 34-38, 2019.
[93]
J. Yang, Research on Cutting Temperature and Tool Wear of Titanium Alloy Complex Surface Machining., Nanjing University of Science and Technology, 2020.
[94]
R.C. Xiang, Design and Implementation of Tool Shank System for On-line Monitoring of Milling Temperature., Huazhong University of Science and Technology, 2020.
[95]
D. Wu, "Design and application of online monitoring system for Shield Cutter wear and Temperature", Renmin Changjiang, vol. 51, no. 06, pp. 154-158, 2020.
[96]
K.W. Cao, Research on Tool Condition Monitoring Based on Cutting Temperature., Huazhong University of Science and Technology, 2021.
[97]
D. Liu, Z. Liu, J. Zhao, Q. Song, X. Ren, and H. Ma, "Tool wear monitoring through online measured cutting force and cutting temperature during face milling Inconel 718", Int. J. Adv. Manuf. Technol., vol. 122, no. 2, pp. 729-740, 2022.
[http://dx.doi.org/10.1007/s00170-022-09950-2]
[98]
X. Zhang, Research on Tool Wear of High Speed Turning Superalloy Based on Fractal Theory., Shandong University of Technology, 2020.
[99]
W.L. Cui, Experimental Study on Friction and Wear Performance of Ultrasonic Motor., Harbin Institute of Technology, 2011.
[100]
H.X. Jiang, Research on On-line Monitoring System of Disc Hob Wear Based on Ultrasonic Distance Measurement., North China Electric Power University: Beijing, 2016.
[101]
X. Li, Research on Tool Wear and Surface Roughness Prediction of H13 Steel in Machining Process., Shandong University, 2020.
[102]
Y.X. Gao, Research on Tool Wear Detection Technology Based on Computer Vision., Hebei University of Technology, 2018.
[103]
H.S. Miao, Research on tool wear condition monitoring technology based on workpiece surface texture., Zhejiang University of Technology, 2003.
[104]
S.C. Xiong, "Tool state monitoring based on markov random field surface texture model", Zhongguo Jixie Gongcheng, no. 08, pp. 22-24, 2004.
[105]
B.Q. Zhou, "Tool wear condition monitoring technology based on texture analysis", Automation Tech. & Appli, no. 10, pp. 17-19, 2006.
[106]
L. Bai, "Research on Tool Wear State Monitoring Technology Based on Workpiece Surface Texture Image", Xi 'an. University. Of. Technology, 2009.
[107]
X.L. Zheng, Z.H. Xu, and L. Zhang, "Tool condition monitoring technology based on texture feature", Mechanical &Electrical Engg, vol. 26, no. 08, pp. 27-29, 2009.
[108]
Z.R. Wang, Y.F. Zou, and F. Zhang, "A machine vision approach to tool wear monitoring based on the image of workpiece surface texture", Adv. Mat. Res., vol. 154-155, no. 154-155, pp. 412-416, 2010.
[http://dx.doi.org/10.4028/www.scientific.net/AMR.154-155.412]
[109]
W.D. Xu, and X.H. Ren, "Research of tool wear condition recognition diagnosis system based on the machined workpiece surface texture image", Appl. Mech. Mater., vol. 130-134, no. 130-134, 2011.
[110]
W.J. Song, S. Guan, and H.Y. Pang, "“Tool wear state monitoring based on hilbert-yellow transform and equidistant feature mapping”, Modular Machine Tool & Automatic Manufactu", Tech, no. 06, pp. 114-118, 2018.
[111]
L. Zoupas, "M. Wodtke, C.I. Papadopoulos, and M. Wasilczuk, “Effect of manufacturing errors of the pad sliding surface on the performance of the hydrodynamic thrust bearing”", Tribol. Int., vol. 134, pp. 211-220, .
[112]
Y.H. Liu, Research on In-machine Detection System of Milling Tool Wear Based on Machine Vision., Harbin University of Science and Technology, 2020.
[113]
J. Yang, J. Duan, T. Li, C. Hu, J. Liang, and T. Shi, "Tool wear monitoring in milling based on fine-grained image classification of machined surface images", Sensors (Basel), vol. 22, no. 21, p. 8416, 2022.
[http://dx.doi.org/10.3390/s22218416] [PMID: 36366114]
[114]
R.Z. Jin, Research on Tool Wear Classification Based on Workpiece Surface Texture., Huazhong University of Science and Technology, 2021.
[115]
H. Yu, K. Wang, R. Zhang, X. Wu, Y. Tong, R. Wang, and D. He, "An improved tool wear monitoring method using local image and fractal dimension of workpiece", Math. Probl. Eng., vol. 2021, pp. 1-11, 2021.
[http://dx.doi.org/10.1155/2021/9913581]
[116]
Y. Lu, and Y.D. Gong, "Research on grinding wheel condition monitoring method based on multi-sensor fusion", Machinery Design & Manufacture, no. 06, pp. 90-91, 2001.
[117]
H.L. Gao, Research on Intelligent Monitoring Technology of Tool Wear in Cutting Process., Southwest Jiaotong University: Chengdu, 2005.
[118]
W. Amer, R. Grosvenor, and P. Prickett, "Machine tool condition monitoring using sweeping filter techniques", Proc. Inst. Mech. Eng., Part I, J. Syst. Control Eng., vol. 221, no. 1, pp. 103-117, 2007.
[http://dx.doi.org/10.1243/09596518JSCE133]
[119]
S. Cho, S. Binsaeid, and S. Asfour, "Design of multisensor fusion-based tool condition monitoring system in end milling", Int. J. Adv. Manuf. Technol., vol. 46, no. 5-8, pp. 681-694, 2010.
[http://dx.doi.org/10.1007/s00170-009-2110-z]
[120]
J. Yu, "Machine tool condition monitoring based on an adaptive gaussian mixture model", J. Manuf. Sci. Eng., vol. 134, no. 3, p. 031004, 2012.
[http://dx.doi.org/10.1115/1.4006093]
[121]
O. Geramifard, J.X. Xu, J.H. Zhou, and X. Li, "A physically segmented hidden markov model approach for continuous tool condition monitoring: diagnostics and prognostics", IEEE Trans. Industr. Inform., vol. 8, no. 4, pp. 964-973, 2012.
[http://dx.doi.org/10.1109/TII.2012.2205583]
[122]
N. Xie, L. Chen, B. Zheng, and X. Liu, "A rough set-based effective state identification method of multisensor tool condition monitoring system", Adv. Mech. Eng., vol. 6, no. Pt. 7, p. 634107, 2014.
[http://dx.doi.org/10.1155/2014/634107]
[123]
J. Gerald Anto Arulraj, and K. Leo Dev Wins, "Artificial neural network assisted sensor fusion model for predicting surface roughness during hard turning of H13 steel with minimal cutting fluid application", Procedia. Materials. Science, vol. 5, no. C, 2014.
[124]
D.W. Liu, Research on Milling Cutter Damage Monitoring Method Based on Multi-Sensor Fusion., Harbin Institute of Technology, 2015.
[125]
K.F. Zhang, Research on Tool Wear Condition Monitoring Method Based on Multi-Sensor Information Fusion., Northeastern University, 2016.
[126]
H.C. Yang, and F.T. Cheng, "Tool wear monitoring and predicting method", U.S Patent 10695884, 2022.
[127]
B. Niu, J. Sun, and B. Yang, "Multisensory based tool wear monitoring for practical applications in milling of titanium alloy", Mater. Today Proc., vol. 22, no. Pt 3, pp. 1209-1217, 2020.
[http://dx.doi.org/10.1016/j.matpr.2019.12.126]
[128]
M. Wu, Research on Intelligent Monitoring Method of Tool Wear Condition Based on Power and Vibration Signal., Beijing Forestry University, 2020.
[129]
X.F. Meng, Research on Tool Wear Monitoring Based on Multi-Sensor Fusion., Qilu University of Technology, 2021.
[130]
H. Shi, Y. Li, X. Bai, K. Zhang, and X. Sun, "A two-stage sound-vibration signal fusion method for weak fault detection in rolling bearing systems", Mech. Syst. Signal Process., vol. 172, p. 109012, 2022.
[http://dx.doi.org/10.1016/j.ymssp.2022.109012]
[131]
Y.F. Zeng, Research on tool condition monitoring based on multi-sensor fusion and attention mechanism., Shandong University, 2022.
[132]
Q. Q. Huang, and C. Y. Qian, "Tool remaining useful life prediction method based on multi-sensor fusion under variable working conditions", Machines, vol. 10, no. 10, 2022.
[133]
Y.Q. Wang, "Parts surface roughness and cutting tool wear prediction method based on multi-task learning", W.O Patent 2021174525, 2021.

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy