Tool wear intelligent monitoring techniques in cutting: a review
Tool wear is inevitable in cutting process. If tool wear failure is not detected in time, it will lead to abnormal cutting process and affect the machining efficiency and quality seriously. The intelligent monitoring of tool wear can make the machining system perceive the real-time status of tools i...
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| Published in | Journal of mechanical science and technology Vol. 37; no. 1; pp. 289 - 303 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Seoul
Korean Society of Mechanical Engineers
01.01.2023
Springer Nature B.V 대한기계학회 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1738-494X 1976-3824 |
| DOI | 10.1007/s12206-022-1229-9 |
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| Abstract | Tool wear is inevitable in cutting process. If tool wear failure is not detected in time, it will lead to abnormal cutting process and affect the machining efficiency and quality seriously. The intelligent monitoring of tool wear can make the machining system perceive the real-time status of tools in advance and make early warning and decision-making, which is an effective way to ensure the efficient operation of machining and manufacturing system. By reviewing the research status of intelligent monitoring of tool wear, the key technical principles and methods of multisource-correlation signal selection, feature extraction and pattern recognition are classified. On the basis, the current application status of tool wear monitoring is discussed. In view of its shortcomings, this paper puts forward the prospect of the future, in order to provide a theoretical basis and reference for the development of tool wear intelligent monitoring technology and intelligent manufacturing industry. |
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| AbstractList | Tool wear is inevitable in cutting process. If tool wear failure is not detected in time, it will lead to abnormal cutting process and affect the machining efficiency and quality seriously. The intelligent monitoring of tool wear can make the machining system perceive the real-time status of tools in advance and make early warning and decision-making, which is an effective way to ensure the efficient operation of machining and manufacturing system. By reviewing the research status of intelligent monitoring of tool wear, the key technical principles and methods of multisource-correlation signal selection, feature extraction and pattern recognition are classified. On the basis, the current application status of tool wear monitoring is discussed. In view of its shortcomings, this paper puts forward the prospect of the future, in order to provide a theoretical basis and reference for the development of tool wear intelligent monitoring technology and intelligent manufacturing industry. Tool wear is inevitable in cutting process. If tool wear failure is not detected in time, it will lead to abnormal cutting process and affect the machining efficiency and quality seriously. The intelligent monitoring of tool wear can make the machining system perceive the real-time status of tools in advance and make early warning and decision-making, which is an effective way to ensure the efficient operation of machining and manufacturing system. By reviewing the research status of intelligent monitoring of tool wear, the key technical principles and methods of multisource-correlation signal selection, feature extraction and pattern recognition are classified. On the basis, the current application status of tool wear monitoring is discussed. In view of its shortcomings, this paper puts forward the prospect of the future, in order to provide a theoretical basis and reference for the development of tool wear intelligent monitoring technology and intelligent manufacturing industry. KCI Citation Count: 0 |
| Author | Jin, Yingbo Ding, Ya Gai, Xiaoyu Cheng, Yaonan Lu, Mengda Guan, Rui |
| Author_xml | – sequence: 1 givenname: Yaonan surname: Cheng fullname: Cheng, Yaonan email: yaonancheng@163.com organization: College of Mechanical and Power Engineering, Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology – sequence: 2 givenname: Xiaoyu surname: Gai fullname: Gai, Xiaoyu organization: College of Mechanical and Power Engineering, Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology – sequence: 3 givenname: Rui surname: Guan fullname: Guan, Rui organization: College of Mechanical and Power Engineering, Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology – sequence: 4 givenname: Yingbo surname: Jin fullname: Jin, Yingbo organization: College of Mechanical and Power Engineering, Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology – sequence: 5 givenname: Mengda surname: Lu fullname: Lu, Mengda organization: College of Mechanical and Power Engineering, Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology – sequence: 6 givenname: Ya surname: Ding fullname: Ding, Ya organization: College of Mechanical and Power Engineering, Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology |
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| Cites_doi | 10.1109/TIE.2013.2274422 10.1007/s12206-013-0982-1 10.1177/1369433218818921 10.1007/s00170-012-4177-1 10.3390/ma12193091 10.1007/s00170-011-3703-x 10.1016/j.precisioneng.2021.07.019 10.3901/JME.2018.17.124 10.5120/5082-7268 10.1016/j.jmrt.2019.10.031 10.1016/j.precisioneng.2020.09.025 10.1016/j.ifacol.2020.12.1310 10.1007/s12206-015-0834-2 10.1016/j.ijmecsci.2019.105254 10.1177/16878140211026720 10.1007/s00170-015-7441-3 10.1016/j.cirp.2021.03.024 10.1016/j.measurement.2016.05.022 10.1016/j.procir.2021.11.315 10.1109/JAS.2016.7510190 10.30880/ijie.2018.10.05.015 10.1109/TII.2017.2723943 10.1016/j.ijmachtools.2009.02.003 10.1007/s00170-011-3536-7 10.1109/TIM.2019.2952476 10.1243/0954405001518233 10.1007/s00170-019-04880-y 10.20473/jisebi.6.1.70-78 10.1016/j.sna.2014.01.004 10.4028/www.scientific.net/AMM.787.907 10.1007/s00170-018-1768-5 10.1088/1757-899X/598/1/012088 10.1109/TIM.2011.2161938 10.3390/electronics8111354 10.1007/s00170-019-03906-9 10.1109/TII.2012.2205583 10.1016/j.wear.2020.203479 10.1007/s00170-015-8230-8 10.1016/j.patrec.2011.07.016 10.1080/10910344.2016.1191026 10.1016/j.ymssp.2017.11.021 10.1016/j.procir.2017.03.320 10.1016/j.ymssp.2017.02.004 10.3390/app6060166 10.1080/00207543.2021.1956675 10.3390/su12114424 10.1016/j.ymssp.2005.10.010 10.1016/j.engappai.2013.06.005 10.1109/PHM-Qingdao46334.2019.8942820 |
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| Keywords | Intelligent monitoring Multisource-correlation sensor signal Feature extraction Pattern recognition Tool wear |
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| References | Wu, Meng, Wu, Zhang (CR19) 2018; 16 Zhu, Yu (CR9) 2017; 93 Kong, Chen, Li, Duan (CR64) 2019; 69 Silva, Silva, Hassui (CR32) 2016; 20 Malmstrm, Skog, Axehill, Gustafsson (CR44) 2020; 53 Bhat, Dutta, Vashisth, Pal, Pal, Sen (CR60) 2016; 83 Xu, Chen, Guo, Yan (CR58) 2007; 21 Hsieh, Lu, Chiou (CR38) 2012; 61 Yu, Shuang, Tang, Liu (CR24) 2017; 91 Li, Bao, Gu, Chen (CR35) 2019; 22 CR75 CR74 Wang, Li, Wang, Zhang, Liu (CR47) 2019; 8 Wang, Wang (CR14) 2012; 59 Huffaker, Bittelli, Rosa (CR37) 2017 Fu (CR11) 2017 Yang (CR15) 2009; 37 Zhu (CR54) 2012; 36 Goharrizi, Sepehri (CR41) 2012; 61 Wang, Pan, Pei, Yang, Yi (CR2) 2020; 12 Long, Wang, Li, Fan (CR39) 2017; 4 You, Gao, Guo, Liu (CR10) 2020; 460–461 Wu (CR79) 2020 Xu (CR67) 2016; 14 Liu, Liu, Wang (CR77) 2016; 38 Zhu, Wong, Hong (CR40) 2009; 49 Retrialisca, Chotijah (CR1) 2020; 6 Zhou, Xue (CR8) 2018; 96 Dutta, Kiran, Paul (CR29) 2000; 214 Adam, Yusof, Iliyas, Saif, Hatem (CR4) 2018; 10 Twardowski, Tabaszewski, Wiciak-Pikua, Felusiak-Czyryca (CR26) 2021; 72 Huang, Zhu, Lei, Li, Tian (CR23) 2021; 2021 CR48 Kong, Chen, Li (CR68) 2018; 104 Mohanraj, Shankar, Rajasekar, Sakthivel, Pramanik (CR43) 2020; 9 Wu, Fu, Guan (CR61) 2016; 6 Liao, Zhou, Zhang, Lu, Ma (CR66) 2019; 104 Sun, Zhang, Niu (CR30) 2016; 86 Li, Cheng, Jin, Yao (CR53) 2009; 19 Yudha, Surjandari, Zulkarnain (CR36) 2019; 598 Wang, Yang, Zhang, Xie (CR20) 2014; 209 Huynh, Won (CR45) 2011; 32 Siddhpura, Paurobally (CR33) 2013; 65 Cerce, Pusavec, Kopac (CR12) 2015; 29 Lu, Yao (CR55) 2003; 41 Zhuang (CR78) 2009 Cui (CR49) 2018; 56 CR13 Xu, Huang, Li, Wang, Liu, Wang (CR57) 2020; 32 CR50 Shan, Zhu (CR63) 2018; 54 Hassan, Sadek, Attia (CR76) 2021; 70 Zhu, Liu (CR18) 2017; 14 Gomes, Brito, Silva, Duarte (CR62) 2021; 67 Shi, Chuah, Yap (CR34) 2020; 107 Baig, Javed, Khaisar, Mwafak, Purushothaman (CR51) 2021; 13 Ma, Wang (CR59) 2001; 28 Uchikata, Beaucamp, Takeuchi (CR7) 2017; 63 Cao, Fu, Zhang (CR56) 2014; 33 Vukadinovic, Majstorovic, Zivkovic, Stojadinovic, Djurdjanovic (CR5) 2021; 104 Gadgay, Kulkarni, Chandrasekhar (CR46) 2012; 39 Bhaskaran (CR25) 2015; 787 Bhat, Dutta, Pal, Pal (CR72) 2016; 90 Xie, Wang, Zhang, Wu, Zeng (CR27) 2016; 38 CR69 Jamshidi, Rimpault, Balazinski, Chatelain (CR17) 2020; 106 Geramifard, Xu, Zhou, Li (CR73) 2014; 61 Ghosh, Ravi, Patra, Mukhopadhyay, Paul, Mohanty, Chattopadhyay (CR28) 2007; 21 Zhu, Hu, Tang (CR31) 2021; 30 Geramifard, Xu, Panda (CR70) 2013; 26 CR21 CR65 Wang, Guo, Lei (CR16) 2014; 28 Rai, Tiwari, Ivanov, Dolgui (CR3) 2021; 59 Zhou (CR6) 2015; 26 Geramifard, Xu, Zhou, Li (CR71) 2012; 8 Wang, Cao (CR22) 2019; 38 Twardowski, Wiciak-Pikuła (CR52) 2019; 12 Zhou, Yang, Guo, Liu, Sun, Song, Zhu, Sun, Jiang (CR42) 2019; 166 M Wu (1229_CR79) 2020 R U Baig (1229_CR51) 2021; 13 1229_CR69 N Ghosh (1229_CR28) 2007; 21 Y Liu (1229_CR77) 2016; 38 Y Fu (1229_CR11) 2017 G Q Zhu (1229_CR31) 2021; 30 A Siddhpura (1229_CR33) 2013; 65 M Jamshidi (1229_CR17) 2020; 106 1229_CR21 R Huffaker (1229_CR37) 2017 1229_CR65 J S Yu (1229_CR24) 2017; 91 N N Bhat (1229_CR60) 2016; 83 N N Bhat (1229_CR72) 2016; 90 Z J Li (1229_CR53) 2009; 19 X P Liao (1229_CR66) 2019; 104 R H L Silva (1229_CR32) 2016; 20 M Wang (1229_CR14) 2012; 59 P Twardowski (1229_CR52) 2019; 12 1229_CR13 C A Zhou (1229_CR42) 2019; 166 L F Wu (1229_CR61) 2016; 6 V Vukadinovic (1229_CR5) 2021; 104 L Cerce (1229_CR12) 2015; 29 Z C You (1229_CR10) 2020; 460–461 H Li (1229_CR35) 2019; 22 A Adam (1229_CR4) 2018; 10 J J Wang (1229_CR22) 2019; 38 1229_CR50 J F Ma (1229_CR59) 2001; 28 N Cui (1229_CR49) 2018; 56 J Zhou (1229_CR6) 2015; 26 J Y Xie (1229_CR27) 2016; 38 G F Wang (1229_CR16) 2014; 28 W Q Cao (1229_CR56) 2014; 33 F Wu (1229_CR19) 2018; 16 M Hassan (1229_CR76) 2021; 70 P Twardowski (1229_CR26) 2021; 72 M Malmstrm (1229_CR44) 2020; 53 Y Wang (1229_CR2) 2020; 12 1229_CR48 R K Dutta (1229_CR29) 2000; 214 A Y Goharrizi (1229_CR41) 2012; 61 S Uchikata (1229_CR7) 2017; 63 K Zhu (1229_CR40) 2009; 49 Y W Shi (1229_CR34) 2020; 107 Z W Huang (1229_CR23) 2021; 2021 Y Q Zhou (1229_CR8) 2018; 96 Z J Zhuang (1229_CR78) 2009 O Geramifard (1229_CR71) 2012; 8 Y Lu (1229_CR55) 2003; 41 Y Zhu (1229_CR54) 2012; 36 M A Yudha (1229_CR36) 2019; 598 D D Kong (1229_CR64) 2019; 69 R Rai (1229_CR3) 2021; 59 G F Wang (1229_CR20) 2014; 209 K P Zhu (1229_CR18) 2017; 14 T Mohanraj (1229_CR43) 2020; 9 B Gadgay (1229_CR46) 2012; 39 O Geramifard (1229_CR73) 2014; 61 Z D Shan (1229_CR63) 2018; 54 1229_CR74 Y Yang (1229_CR15) 2009; 37 C W Xu (1229_CR58) 2007; 21 X Xu (1229_CR67) 2016; 14 J Bhaskaran (1229_CR25) 2015; 787 1229_CR75 M C Gomes (1229_CR62) 2021; 67 H Sun (1229_CR30) 2016; 86 W H Hsieh (1229_CR38) 2012; 61 J Long (1229_CR39) 2017; 4 D D Kong (1229_CR68) 2018; 104 K Zhu (1229_CR9) 2017; 93 H T Huynh (1229_CR45) 2011; 32 L H Xu (1229_CR57) 2020; 32 O Geramifard (1229_CR70) 2013; 26 F Retrialisca (1229_CR1) 2020; 6 G J Wang (1229_CR47) 2019; 8 |
| References_xml | – volume: 61 start-page: 2900 issue: 6 year: 2014 end-page: 2911 ident: CR73 article-title: Multimodal hidden markov model-based approach for tool wear monitoring publication-title: IEEE Transactions on Industrial Electronics doi: 10.1109/TIE.2013.2274422 – year: 2009 ident: CR78 publication-title: Research on Tool Wearing Condition Monitoring based on AE and Vibration – volume: 19 start-page: 103 issue: 5 year: 2009 end-page: 106 ident: CR53 article-title: BP algorithm improvement based on sample expected training number publication-title: Computer Technology and Development – volume: 28 start-page: 317 issue: 1 year: 2014 end-page: 322 ident: CR16 article-title: Tool wear prediction considering uncovered data based on partial least square regression publication-title: Journal of Mechanical Science and Technology doi: 10.1007/s12206-013-0982-1 – volume: 22 start-page: 1519 issue: 7 year: 2019 end-page: 1530 ident: CR35 article-title: Vibration feature extraction based on the improved variational mode decomposition and singular spectrum analysis combination algorithm publication-title: Advances in Structural Engineering doi: 10.1177/1369433218818921 – volume: 65 start-page: 371 issue: 1–4 year: 2013 end-page: 393 ident: CR33 article-title: A review of flank wear prediction methods for tool condition monitoring in a turning process publication-title: International Journal of Advanced Manufacturing Technology doi: 10.1007/s00170-012-4177-1 – volume: 33 start-page: 1682 issue: 11 year: 2014 end-page: 1687 ident: CR56 article-title: Application of GA-fuzzy-neural networks in tool wear diagnosis publication-title: Mechanical Science and Technology for Aerospace Engineering – ident: CR74 – volume: 38 start-page: 103 issue: 2 year: 2019 end-page: 108 ident: CR22 article-title: Research on milling cutter wear monitoring based on spindle vibration signal publication-title: Foreign Electronic Measurement Technology – start-page: 51 year: 2017 end-page: 82 ident: CR37 article-title: Phase space reconstruction publication-title: Nonlinear Time Series Analysis with R – volume: 12 start-page: 3091 issue: 19 year: 2019 end-page: 3106 ident: CR52 article-title: Prediction of tool wear using artificial neural networks during turning of hardened steel publication-title: Materials doi: 10.3390/ma12193091 – volume: 61 start-page: 53 issue: 1–4 year: 2012 end-page: 61 ident: CR38 article-title: Application of back-propagation neural network for spindle vibration-based tool wear monitoring in micro-milling publication-title: International Journal of Advanced Manufacturing Technology doi: 10.1007/s00170-011-3703-x – volume: 38 start-page: 137 issue: 5 year: 2016 end-page: 143 ident: CR77 article-title: An intelligent tool wear estimation technology based on multi-feature and support vector machine publication-title: Manufacturing Automation – volume: 72 start-page: 738 year: 2021 end-page: 744 ident: CR26 article-title: Identification of tool wear using acoustic emission signal and machine learning methods publication-title: Precision Engineering doi: 10.1016/j.precisioneng.2021.07.019 – volume: 54 start-page: 124 issue: 17 year: 2018 end-page: 132 ident: CR63 article-title: Wear mechanism and prediction model of polycrystalline diamond tool in milling sand mould publication-title: Journal of Mechanical Engineering doi: 10.3901/JME.2018.17.124 – volume: 39 start-page: 37 issue: 18 year: 2012 end-page: 45 ident: CR46 article-title: Novel ensemble neural network models for better prediction using variable input approach publication-title: International Journal of Computer Applications doi: 10.5120/5082-7268 – volume: 26 start-page: 2273 issue: 17 year: 2015 end-page: 2284 ident: CR6 article-title: Intelligent manufacturing-main direction of “made in China 2025” publication-title: Journal of Mechanical Engineering – volume: 9 start-page: 1032 issue: 1 year: 2020 end-page: 1042 ident: CR43 article-title: Tool condition monitoring techniques in milling process-a review publication-title: Journal of Materials Research and Technology doi: 10.1016/j.jmrt.2019.10.031 – volume: 2021 start-page: 1 issue: 6 year: 2021 end-page: 14 ident: CR23 article-title: Tool wear monitoring with vibration signals based on short-time fourier transform and deep convolutional neural network in milling publication-title: Mathematical Problems in Engineering – volume: 21 start-page: 219 issue: 3 year: 2007 end-page: 224 ident: CR58 article-title: Recognizing tool wear based on radical basic function network publication-title: Journal of Test and Measurement Technology – volume: 67 start-page: 137 year: 2021 end-page: 151 ident: CR62 article-title: Tool wear monitoring in micromilling using support vector machine with vibration and sound sensors publication-title: Precision Engineering doi: 10.1016/j.precisioneng.2020.09.025 – ident: CR21 – volume: 56 start-page: 74 issue: 7 year: 2018 end-page: 77 ident: CR49 article-title: Research on tool wear state monitoring based on BP neural network publication-title: Agricultural Equipment and Vehicle Engineering – volume: 53 start-page: 1108 issue: 2 year: 2020 end-page: 1113 ident: CR44 article-title: Asymptotic prediction error variance for feedforward neural networks publication-title: IFAC-PapersOnLine doi: 10.1016/j.ifacol.2020.12.1310 – volume: 29 start-page: 3885 issue: 9 year: 2015 end-page: 3895 ident: CR12 article-title: 3D cutting tool-wear monitoring in the process publication-title: Journal of Mechanical Science and Technology doi: 10.1007/s12206-015-0834-2 – volume: 166 start-page: 105254 year: 2019 ident: CR42 article-title: Vibration singularity analysis for milling tool condition monitoring publication-title: International Journal of Mechanical Sciences doi: 10.1016/j.ijmecsci.2019.105254 – volume: 13 start-page: 1 issue: 6 year: 2021 end-page: 14 ident: CR51 article-title: Development of an ANN model for prediction of tool wear in turning EN9 and EN24 steel alloy publication-title: Advances in Mechanical Engineering doi: 10.1177/16878140211026720 – ident: CR75 – volume: 83 start-page: 1487 issue: 9–12 year: 2016 end-page: 1502 ident: CR60 article-title: Tool condition monitoring by SVM classification of machined surfaceimages in turning publication-title: International Journal of Advanced Manufacturing Technology doi: 10.1007/s00170-015-7441-3 – year: 2020 ident: CR79 publication-title: Research on Tool Wear Status Intelligent Monitoring based on the Power and Vibration Signals – ident: CR50 – volume: 70 start-page: 1 issue: 1 year: 2021 end-page: 4 ident: CR76 article-title: Novel sensor-based tool wear monitoring approach for seamless implementation in high speed milling applications publication-title: CIRP Annals-Manufacturing Technology doi: 10.1016/j.cirp.2021.03.024 – volume: 90 start-page: 500 year: 2016 end-page: 509 ident: CR72 article-title: Tool condition classification in turning process using hidden Markov model based on texture analysis of machined surface images publication-title: Measurement doi: 10.1016/j.measurement.2016.05.022 – volume: 104 start-page: 1867 year: 2021 end-page: 1872 ident: CR5 article-title: Digital manufacturing as a basis for the development of the industry 4.0 model publication-title: Procedia CIRP doi: 10.1016/j.procir.2021.11.315 – volume: 38 start-page: 1 issue: 7 year: 2016 end-page: 4 ident: CR27 article-title: Tool wear predicting based on multi-sensing and data fusion publication-title: Manufacturing Automation – volume: 4 start-page: 734 issue: 4 year: 2017 end-page: 750 ident: CR39 article-title: Applications of fractional lower order time-frequency representation to machine bearing fault diagnosis publication-title: IEEE/CAA Journal of Automatica Sinica doi: 10.1109/JAS.2016.7510190 – volume: 10 start-page: 93 issue: 5 year: 2018 end-page: 98 ident: CR4 article-title: Review on manufacturing for advancement of industrial revolution 4.0 publication-title: International Journal of Integrated Engineering doi: 10.30880/ijie.2018.10.05.015 – volume: 14 start-page: 69 issue: 1 year: 2017 end-page: 78 ident: CR18 article-title: On-line tool wear monitoring via Hidden Semi-Markov model with dependent durations publication-title: IEEE Transactions on Industrial Informatics doi: 10.1109/TII.2017.2723943 – volume: 41 start-page: 47 issue: 3 year: 2003 end-page: 49 ident: CR55 article-title: Applications of the fuzzy neural network in tool wear detection publication-title: Machinery Manufacturing – volume: 49 start-page: 537 issue: 7–8 year: 2009 end-page: 553 ident: CR40 article-title: Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results publication-title: International Journal of Machine Tools and Manufacture doi: 10.1016/j.ijmachtools.2009.02.003 – volume: 107 start-page: 4837 issue: 11–12 year: 2020 end-page: 4857 ident: CR34 article-title: Technical data-driven tool condition monitoring challenges for CNC milling: a review publication-title: International Journal of Advanced Manufacturing Technology – volume: 59 start-page: 463 issue: 5–8 year: 2012 end-page: 471 ident: CR14 article-title: CHMM for tool condition monitoring and remaining useful life prediction publication-title: International Journal of Advanced Manufacturing Technology doi: 10.1007/s00170-011-3536-7 – volume: 69 start-page: 5219 issue: 7 year: 2019 end-page: 5232 ident: CR64 article-title: Tool wear estimation in end-milling of titanium alloy using NPE and a novel WOA-SVM model publication-title: IEEE Transactions on Instrumentation and Measurement doi: 10.1109/TIM.2019.2952476 – volume: 214 start-page: 535 issue: 7 year: 2000 end-page: 546 ident: CR29 article-title: Assessment of machining features for tool condition monitoring in face milling using an artificial neural network publication-title: Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture doi: 10.1243/0954405001518233 – volume: 106 start-page: 3859 issue: 9 year: 2020 end-page: 3868 ident: CR17 article-title: Fractal analysis implementation for tool wear monitoring based on cutting force signals during CFRP/titanium stack machining publication-title: International Journal of Advanced Manufacturing Technology doi: 10.1007/s00170-019-04880-y – volume: 6 start-page: 70 issue: 1 year: 2020 end-page: 78 ident: CR1 article-title: The maturity measurement of big data adoption in manufacturing companies using the TDWI maturity model publication-title: Journal of Information Systems Engineering and Business Intelligence doi: 10.20473/jisebi.6.1.70-78 – volume: 30 start-page: 1 year: 2021 end-page: 15 ident: CR31 article-title: Prediction of tool wear in CFRP drilling based on neural network with multicharacteristics and multisignal sources publication-title: Advances in Material Processing Mechanism and Monitoring Technology – volume: 209 start-page: 24 year: 2014 end-page: 32 ident: CR20 article-title: Vibration sensor based tool condition monitoring using v-support vector machine and locality preserving projection publication-title: Sensors and Actuators A Physical doi: 10.1016/j.sna.2014.01.004 – volume: 32 start-page: 77 issue: 1–4 year: 2020 end-page: 90 ident: CR57 article-title: Estimation of tool wear and optimization of cutting parameters based on novel ANFIS-PSO method toward intelligent machining publication-title: Journal of Intelligent Manufacturing – volume: 36 start-page: 1252 issue: 6 year: 2012 end-page: 1255 ident: CR54 article-title: Improved BP neural network prediction model and its application publication-title: Journal of Wuhan University of Technology – volume: 37 start-page: 250 issue: 7 year: 2009 end-page: 251 ident: CR15 article-title: Monitoring of cutting tool wear based on cutting force by using wavelet neural network publication-title: Machine Tools and Hydraulics – volume: 787 start-page: 907 year: 2015 end-page: 911 ident: CR25 article-title: On-line monitoring the hard turning using distribution parameters of acoustic emission signal publication-title: Applied Mechanics and Materials doi: 10.4028/www.scientific.net/AMM.787.907 – ident: CR69 – volume: 96 start-page: 2509 issue: 4 year: 2018 end-page: 2532 ident: CR8 article-title: Review of tool condition monitoring methods in milling processes publication-title: International Journal of Advanced Manufacturing Technology doi: 10.1007/s00170-018-1768-5 – ident: CR48 – volume: 598 start-page: 012088 issue: 1 year: 2019 ident: CR36 article-title: Feature extraction o condition monitoring data on heavy equipment’s component using principal component analysis (PCA) publication-title: IOP Conference Series: Materials Science and Engineering doi: 10.1088/1757-899X/598/1/012088 – volume: 61 start-page: 368 issue: 2 year: 2012 end-page: 378 ident: CR41 article-title: Internal leakage detection in hydraulic actuators using empirical mode decomposition and hilbert spectrum publication-title: IEEE Transactions on Instrumentation and Measurement doi: 10.1109/TIM.2011.2161938 – volume: 28 start-page: 12 issue: 5 year: 2001 end-page: 13 ident: CR59 article-title: Tool wear monitoring on milling based on RBF neural network publication-title: Mechanics – ident: CR65 – volume: 91 start-page: 1 issue: 1–4 year: 2017 end-page: 11 ident: CR24 article-title: A weighted hidden Markov model approach for continuous-state tool wear monitoring and tool life prediction publication-title: International Journal of Advanced Manufacturing Technology – volume: 8 start-page: 1354 issue: 11 year: 2019 ident: CR47 article-title: Elderly fall detection with an accelerometer using lightweight neural networks publication-title: Electronics doi: 10.3390/electronics8111354 – year: 2017 ident: CR11 publication-title: Research on Intelligent Monitoring Method for Vibration States and Tool Wear in Machining – ident: CR13 – volume: 104 start-page: 1051 issue: 1 year: 2019 end-page: 1063 ident: CR66 article-title: Tool wear state recognition based on GWO-SVM with feature selection of genetic algorithm publication-title: International Journal of Advanced Manufacturing Technology doi: 10.1007/s00170-019-03906-9 – volume: 14 start-page: 97 issue: 11 year: 2016 end-page: 99 ident: CR67 article-title: Gaussian process regression method and its prediction model publication-title: Science and Technology Information – volume: 8 start-page: 964 issue: 4 year: 2012 end-page: 973 ident: CR71 article-title: A physically segmented hidden markov model approach for continuous tool condition monitoring, diagnostics and prognostics publication-title: IEEE Transactions on Industrial Informatics doi: 10.1109/TII.2012.2205583 – volume: 460–461 start-page: 203479 year: 2020 ident: CR10 article-title: Online milling cutter wear monitoring in a wide field-of-view camera publication-title: Wear doi: 10.1016/j.wear.2020.203479 – volume: 86 start-page: 841 issue: 1–4 year: 2016 end-page: 851 ident: CR30 article-title: In-process cutting tool remaining useful life evaluation based on operational reliability assessment publication-title: International Journal of Advanced Manufacturing Technology doi: 10.1007/s00170-015-8230-8 – volume: 32 start-page: 1930 issue: 14 year: 2011 end-page: 1935 ident: CR45 article-title: Regularized online sequential learning algorithm for single-hidden layer feedforward neural networks publication-title: Pattern Recognition Letters doi: 10.1016/j.patrec.2011.07.016 – volume: 16 start-page: 76 issue: 2 year: 2018 end-page: 80 ident: CR19 article-title: Measurement and analysis system of rotor vibration and noise of rotating machinery publication-title: Digital Manufacturing Science – volume: 20 start-page: 26 issue: 3 year: 2016 end-page: 29 ident: CR32 article-title: A probabilistic neural network applied in monitoring tool wear in the end milling operation via acoustic emission and cutting power signals publication-title: Machining Science and Technology doi: 10.1080/10910344.2016.1191026 – volume: 104 start-page: 556 issue: 51 year: 2018 end-page: 574 ident: CR68 article-title: Gaussian process regression for tool wear prediction publication-title: Mechanical Systems and Signal Processing doi: 10.1016/j.ymssp.2017.11.021 – volume: 63 start-page: 313 year: 2017 end-page: 317 ident: CR7 article-title: 5-Axis control finishing for decreased tool wear publication-title: Procedia CIRP doi: 10.1016/j.procir.2017.03.320 – volume: 93 start-page: 80 year: 2017 end-page: 91 ident: CR9 article-title: The monitoring of micro milling tool wear conditions by wear area estimation publication-title: Mechanical Systems & Signal Processing doi: 10.1016/j.ymssp.2017.02.004 – volume: 6 start-page: 166 issue: 6 year: 2016 end-page: 176 ident: CR61 article-title: Review of the remaining useful life prognostics of vehicle lithium-Ion batteries using data-driven methodologies publication-title: Applied Sciences doi: 10.3390/app6060166 – volume: 59 start-page: 4773 issue: 16 year: 2021 end-page: 4778 ident: CR3 article-title: Machine learning in manufacturing and industry 4.0 applications publication-title: International Journal of Production Research doi: 10.1080/00207543.2021.1956675 – volume: 12 start-page: 1 issue: 11 year: 2020 end-page: 25 ident: CR2 article-title: A framework for assessing green capacity utilization considering CO emissions in China’s high-tech manufacturing industry publication-title: Sustainability doi: 10.3390/su12114424 – volume: 21 start-page: 466 issue: 1 year: 2007 end-page: 479 ident: CR28 article-title: Estimation of tool wear during CNC milling using neural network-based sensor fusion publication-title: Mechanical Systems and Signal Processing doi: 10.1016/j.ymssp.2005.10.010 – volume: 26 start-page: 1919 issue: 8 year: 2013 end-page: 1929 ident: CR70 article-title: Fault detection and diagnosis in synchronous motors using hidden Markov model-based semi-nonparametric approach publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2013.06.005 – volume: 26 start-page: 2273 issue: 17 year: 2015 ident: 1229_CR6 publication-title: Journal of Mechanical Engineering – volume: 10 start-page: 93 issue: 5 year: 2018 ident: 1229_CR4 publication-title: International Journal of Integrated Engineering doi: 10.30880/ijie.2018.10.05.015 – volume: 38 start-page: 103 issue: 2 year: 2019 ident: 1229_CR22 publication-title: Foreign Electronic Measurement Technology – volume: 61 start-page: 368 issue: 2 year: 2012 ident: 1229_CR41 publication-title: IEEE Transactions on Instrumentation and Measurement doi: 10.1109/TIM.2011.2161938 – volume: 104 start-page: 556 issue: 51 year: 2018 ident: 1229_CR68 publication-title: Mechanical Systems and Signal Processing doi: 10.1016/j.ymssp.2017.11.021 – volume-title: Research on Tool Wearing Condition Monitoring based on AE and Vibration year: 2009 ident: 1229_CR78 – volume: 19 start-page: 103 issue: 5 year: 2009 ident: 1229_CR53 publication-title: Computer Technology and Development – volume: 598 start-page: 012088 issue: 1 year: 2019 ident: 1229_CR36 publication-title: IOP Conference Series: Materials Science and Engineering doi: 10.1088/1757-899X/598/1/012088 – volume: 53 start-page: 1108 issue: 2 year: 2020 ident: 1229_CR44 publication-title: IFAC-PapersOnLine doi: 10.1016/j.ifacol.2020.12.1310 – ident: 1229_CR75 – volume: 29 start-page: 3885 issue: 9 year: 2015 ident: 1229_CR12 publication-title: Journal of Mechanical Science and Technology doi: 10.1007/s12206-015-0834-2 – volume: 22 start-page: 1519 issue: 7 year: 2019 ident: 1229_CR35 publication-title: Advances in Structural Engineering doi: 10.1177/1369433218818921 – volume: 12 start-page: 1 issue: 11 year: 2020 ident: 1229_CR2 publication-title: Sustainability doi: 10.3390/su12114424 – start-page: 51 volume-title: Nonlinear Time Series Analysis with R year: 2017 ident: 1229_CR37 – ident: 1229_CR13 – volume: 33 start-page: 1682 issue: 11 year: 2014 ident: 1229_CR56 publication-title: Mechanical Science and Technology for Aerospace Engineering – volume: 787 start-page: 907 year: 2015 ident: 1229_CR25 publication-title: Applied Mechanics and Materials doi: 10.4028/www.scientific.net/AMM.787.907 – volume: 20 start-page: 26 issue: 3 year: 2016 ident: 1229_CR32 publication-title: Machining Science and Technology doi: 10.1080/10910344.2016.1191026 – volume-title: Research on Intelligent Monitoring Method for Vibration States and Tool Wear in Machining year: 2017 ident: 1229_CR11 – volume: 69 start-page: 5219 issue: 7 year: 2019 ident: 1229_CR64 publication-title: IEEE Transactions on Instrumentation and Measurement doi: 10.1109/TIM.2019.2952476 – volume: 9 start-page: 1032 issue: 1 year: 2020 ident: 1229_CR43 publication-title: Journal of Materials Research and Technology doi: 10.1016/j.jmrt.2019.10.031 – volume: 56 start-page: 74 issue: 7 year: 2018 ident: 1229_CR49 publication-title: Agricultural Equipment and Vehicle Engineering – volume: 59 start-page: 4773 issue: 16 year: 2021 ident: 1229_CR3 publication-title: International Journal of Production Research doi: 10.1080/00207543.2021.1956675 – volume: 59 start-page: 463 issue: 5–8 year: 2012 ident: 1229_CR14 publication-title: International Journal of Advanced Manufacturing Technology doi: 10.1007/s00170-011-3536-7 – ident: 1229_CR48 – volume: 32 start-page: 77 issue: 1–4 year: 2020 ident: 1229_CR57 publication-title: Journal of Intelligent Manufacturing – volume: 104 start-page: 1867 year: 2021 ident: 1229_CR5 publication-title: Procedia CIRP doi: 10.1016/j.procir.2021.11.315 – volume: 72 start-page: 738 year: 2021 ident: 1229_CR26 publication-title: Precision Engineering doi: 10.1016/j.precisioneng.2021.07.019 – volume: 13 start-page: 1 issue: 6 year: 2021 ident: 1229_CR51 publication-title: Advances in Mechanical Engineering doi: 10.1177/16878140211026720 – ident: 1229_CR69 – volume: 90 start-page: 500 year: 2016 ident: 1229_CR72 publication-title: Measurement doi: 10.1016/j.measurement.2016.05.022 – volume: 14 start-page: 69 issue: 1 year: 2017 ident: 1229_CR18 publication-title: IEEE Transactions on Industrial Informatics doi: 10.1109/TII.2017.2723943 – volume: 104 start-page: 1051 issue: 1 year: 2019 ident: 1229_CR66 publication-title: International Journal of Advanced Manufacturing Technology doi: 10.1007/s00170-019-03906-9 – volume: 30 start-page: 1 year: 2021 ident: 1229_CR31 publication-title: Advances in Material Processing Mechanism and Monitoring Technology – volume: 4 start-page: 734 issue: 4 year: 2017 ident: 1229_CR39 publication-title: IEEE/CAA Journal of Automatica Sinica doi: 10.1109/JAS.2016.7510190 – volume: 83 start-page: 1487 issue: 9–12 year: 2016 ident: 1229_CR60 publication-title: International Journal of Advanced Manufacturing Technology doi: 10.1007/s00170-015-7441-3 – volume: 65 start-page: 371 issue: 1–4 year: 2013 ident: 1229_CR33 publication-title: International Journal of Advanced Manufacturing Technology doi: 10.1007/s00170-012-4177-1 – volume: 6 start-page: 166 issue: 6 year: 2016 ident: 1229_CR61 publication-title: Applied Sciences doi: 10.3390/app6060166 – volume: 460–461 start-page: 203479 year: 2020 ident: 1229_CR10 publication-title: Wear doi: 10.1016/j.wear.2020.203479 – volume: 36 start-page: 1252 issue: 6 year: 2012 ident: 1229_CR54 publication-title: Journal of Wuhan University of Technology – volume: 28 start-page: 12 issue: 5 year: 2001 ident: 1229_CR59 publication-title: Mechanics – volume: 63 start-page: 313 year: 2017 ident: 1229_CR7 publication-title: Procedia CIRP doi: 10.1016/j.procir.2017.03.320 – ident: 1229_CR50 – volume: 16 start-page: 76 issue: 2 year: 2018 ident: 1229_CR19 publication-title: Digital Manufacturing Science – volume: 91 start-page: 1 issue: 1–4 year: 2017 ident: 1229_CR24 publication-title: International Journal of Advanced Manufacturing Technology – volume: 61 start-page: 53 issue: 1–4 year: 2012 ident: 1229_CR38 publication-title: International Journal of Advanced Manufacturing Technology doi: 10.1007/s00170-011-3703-x – volume: 54 start-page: 124 issue: 17 year: 2018 ident: 1229_CR63 publication-title: Journal of Mechanical Engineering doi: 10.3901/JME.2018.17.124 – volume: 2021 start-page: 1 issue: 6 year: 2021 ident: 1229_CR23 publication-title: Mathematical Problems in Engineering – volume: 67 start-page: 137 year: 2021 ident: 1229_CR62 publication-title: Precision Engineering doi: 10.1016/j.precisioneng.2020.09.025 – volume: 12 start-page: 3091 issue: 19 year: 2019 ident: 1229_CR52 publication-title: Materials doi: 10.3390/ma12193091 – volume-title: Research on Tool Wear Status Intelligent Monitoring based on the Power and Vibration Signals year: 2020 ident: 1229_CR79 – volume: 106 start-page: 3859 issue: 9 year: 2020 ident: 1229_CR17 publication-title: International Journal of Advanced Manufacturing Technology doi: 10.1007/s00170-019-04880-y – volume: 26 start-page: 1919 issue: 8 year: 2013 ident: 1229_CR70 publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2013.06.005 – volume: 38 start-page: 137 issue: 5 year: 2016 ident: 1229_CR77 publication-title: Manufacturing Automation – volume: 32 start-page: 1930 issue: 14 year: 2011 ident: 1229_CR45 publication-title: Pattern Recognition Letters doi: 10.1016/j.patrec.2011.07.016 – volume: 166 start-page: 105254 year: 2019 ident: 1229_CR42 publication-title: International Journal of Mechanical Sciences doi: 10.1016/j.ijmecsci.2019.105254 – ident: 1229_CR65 doi: 10.1109/PHM-Qingdao46334.2019.8942820 – volume: 8 start-page: 964 issue: 4 year: 2012 ident: 1229_CR71 publication-title: IEEE Transactions on Industrial Informatics doi: 10.1109/TII.2012.2205583 – volume: 214 start-page: 535 issue: 7 year: 2000 ident: 1229_CR29 publication-title: Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture doi: 10.1243/0954405001518233 – ident: 1229_CR21 – volume: 8 start-page: 1354 issue: 11 year: 2019 ident: 1229_CR47 publication-title: Electronics doi: 10.3390/electronics8111354 – volume: 6 start-page: 70 issue: 1 year: 2020 ident: 1229_CR1 publication-title: Journal of Information Systems Engineering and Business Intelligence doi: 10.20473/jisebi.6.1.70-78 – volume: 107 start-page: 4837 issue: 11–12 year: 2020 ident: 1229_CR34 publication-title: International Journal of Advanced Manufacturing Technology – volume: 70 start-page: 1 issue: 1 year: 2021 ident: 1229_CR76 publication-title: CIRP Annals-Manufacturing Technology doi: 10.1016/j.cirp.2021.03.024 – volume: 41 start-page: 47 issue: 3 year: 2003 ident: 1229_CR55 publication-title: Machinery Manufacturing – volume: 61 start-page: 2900 issue: 6 year: 2014 ident: 1229_CR73 publication-title: IEEE Transactions on Industrial Electronics doi: 10.1109/TIE.2013.2274422 – volume: 93 start-page: 80 year: 2017 ident: 1229_CR9 publication-title: Mechanical Systems & Signal Processing doi: 10.1016/j.ymssp.2017.02.004 – volume: 37 start-page: 250 issue: 7 year: 2009 ident: 1229_CR15 publication-title: Machine Tools and Hydraulics – ident: 1229_CR74 – volume: 28 start-page: 317 issue: 1 year: 2014 ident: 1229_CR16 publication-title: Journal of Mechanical Science and Technology doi: 10.1007/s12206-013-0982-1 – volume: 86 start-page: 841 issue: 1–4 year: 2016 ident: 1229_CR30 publication-title: International Journal of Advanced Manufacturing Technology doi: 10.1007/s00170-015-8230-8 – volume: 49 start-page: 537 issue: 7–8 year: 2009 ident: 1229_CR40 publication-title: International Journal of Machine Tools and Manufacture doi: 10.1016/j.ijmachtools.2009.02.003 – volume: 21 start-page: 466 issue: 1 year: 2007 ident: 1229_CR28 publication-title: Mechanical Systems and Signal Processing doi: 10.1016/j.ymssp.2005.10.010 – volume: 209 start-page: 24 year: 2014 ident: 1229_CR20 publication-title: Sensors and 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| Snippet | Tool wear is inevitable in cutting process. If tool wear failure is not detected in time, it will lead to abnormal cutting process and affect the machining... |
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| SubjectTerms | Control Cutting wear Decision making Dynamical Systems Engineering Feature extraction Industrial and Production Engineering Intelligent manufacturing systems Machining Mechanical Engineering Monitoring Original Article Pattern recognition Tool wear Vibration 기계공학 |
| Title | Tool wear intelligent monitoring techniques in cutting: a review |
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