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Review of AI-based methods for chatter detection in machining based on bibliometric analysis

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Abstract

To improve the finish and efficiency of machining processes, researchers set out to develop techniques to detect, suppress, or avoid vibration chatter. This work involves tracing chatter detection techniques, from time–frequency signal processing methods (FFT, HHT, STFT, etc.), decomposition (WPD, EMD, VMD, etc.) to the combination with machine learning or deep learning models. A cartographic analysis was carried out to discover the limits of these different techniques and to propose possible solutions in perspective to detect chattering in the machining processes. The fact that human expert detects chatter using simple spectrograms is confronted with the variety of signal processing methods used in the literature and lead to possible optimal detecting techniques. For this purpose, the bibliometric tool R-Tool was used to facilitate a bibliometric analysis using specific means for quantitative bibliometric research and visualization. Data were collected from the Web of Science (WoS 2022) using particular queries on chatter detection. Most documents collected detect chatter with either transformation or decomposition techniques.

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References

  1. Taylor FW (1906) On the art of cutting metals. Trans ASME 29:31–350

    Google Scholar 

  2. Tlustý J, Špaček L (1954) Samobuzené kmity v obráběcích strojích. ČSAV

  3. Tobias S, Fishwick W (1958) Theory of regenerative machine tool chatter. Engineer 205(7):199–203

    Google Scholar 

  4. Tlusty J (1963) The stability of the machine tool against self-excited vibration in machining. Proc Int Res Prod Eng Pittsburgh ASME 465

  5. Merrit H (1965) Theory of self-excited machine-tool chatter. Trans ASME J Eng Ind 87(4):447

    Article  Google Scholar 

  6. Abele E, Altintas Y, Brecher C (2010) Machine tool spindle units. CIRP Ann 59(2):781–802. https://doi.org/10.1016/j.cirp.2010.05.002

    Article  Google Scholar 

  7. Jantunen E (2002) A summary of methods applied to tool condition monitoring in drilling. Int J Mach Tools Manuf 42(9):997–1010. https://doi.org/10.1016/S0890-6955(02)00040-8

    Article  Google Scholar 

  8. Al-Regib E, Ni J (2010) Chatter detection in machining using nonlinear energy operator. J Dyn Syst Meas Control 132(3). https://doi.org/10.1115/1.4001331

    Article  Google Scholar 

  9. Sun Y, Ding L, Liu C, Xiong Z, Zhu X (2020) Beat effect in machining chatter: analysis and detection. J Manuf Sci Eng 143(1). https://doi.org/10.1115/1.4047736

    Article  Google Scholar 

  10. Kolluru K, Axinte D (2013) Coupled interaction of dynamic responses of tool and workpiece in thin wall milling. J Mater Process Technol 213(9):1565–1574. https://doi.org/10.1016/j.jmatprotec.2013.03.018

    Article  Google Scholar 

  11. Thaler T, Potočnik P, Bric I, Govekar E (2014) Chatter detection in band sawing based on discriminant analysis of sound features. Appl Acoust 77:114–121. https://doi.org/10.1016/j.apacoust.2012.12.004

    Article  Google Scholar 

  12. Wang L, Liang M (2009) Chatter detection based on probability distribution of wavelet modulus maxima. Robot Comput-Integr Manuf 25(6):989–998. https://doi.org/10.1016/j.rcim.2009.04.011

    Article  Google Scholar 

  13. Liu Y, Wang X, Lin J, Zhao W (2016) Early chatter detection in gear grinding process using servo feed motor current. Int J Adv Manuf Technol 83(9):1801–1810. https://doi.org/10.1007/s00170-015-7687-9

    Article  Google Scholar 

  14. Zhang Z, Li H, Meng G, Tu X, Cheng C (2016) Chatter detection in milling process based on the energy entropy of VMD and WPD. Int J Mach Tools Manuf 108:106–112. https://doi.org/10.1016/j.ijmachtools.2016.06.002

    Article  Google Scholar 

  15. Cao H, Lei Y, He Z (2013) Chatter identification in end milling process using wavelet packets and Hilbert-Huang transform. Int J Mach Tools Manuf 69:11–19. https://doi.org/10.1016/j.ijmachtools.2013.02.007

    Article  Google Scholar 

  16. Fu Y et al (2016) Timely online chatter detection in end milling process. Mech Syst Signal Process 75:668–688. https://doi.org/10.1016/j.ymssp.2016.01.003

    Article  Google Scholar 

  17. Rusinek R, Lajmert P (2020) Chatter detection in milling of carbon fiber-reinforced composites by improved Hilbert-Huang transform and recurrence quantification analysis. Materials 13(18):4105. https://doi.org/10.3390/ma13184105

    Article  Google Scholar 

  18. Shrivastava Y, Singh B (2019) A comparative study of EMD and EEMD approaches for identifying chatter frequency in CNC turning. Eur J Mech - A/Solids 73:381–393. https://doi.org/10.1016/j.euromechsol.2018.10.004

    Article  Google Scholar 

  19. Chen Y, Li H, Hou L, Wang J, Bu X (2018) An intelligent chatter detection method based on EEMD and feature selection with multi-channel vibration signals. Measurement 127:356–365. https://doi.org/10.1016/j.measurement.2018.06.006

    Article  Google Scholar 

  20. Liu C, Zhu L, Ni C (2018) Chatter detection in milling process based on VMD and energy entropy. Mech Syst Signal Process 105:169–182. https://doi.org/10.1016/j.ymssp.2017.11.046

    Article  Google Scholar 

  21. Liu T, Deng Z, Luo C, Li Z, Lv L, Zhuo R (2022) Chatter detection in camshaft high-speed grinding process based on VMD parametric optimization. Measurement 187:110133. https://doi.org/10.1016/j.measurement.2021.110133

    Article  Google Scholar 

  22. Rother A, Jelali M, Söffker D (2015) A brief review and a first application of time-frequency-based analysis methods for monitoring of strip rolling mills. J Process Control 35:65–79. https://doi.org/10.1016/j.jprocont.2015.08.010

    Article  Google Scholar 

  23. Lu S, He Q, Wang J (2019) A review of stochastic resonance in rotating machine fault detection. Mech Syst Signal Process 116:230–260. https://doi.org/10.1016/j.ymssp.2018.06.032

    Article  Google Scholar 

  24. Serin G, Sener B, Ozbayoglu AM, Unver HO (2020) Review of tool condition monitoring in machining and opportunities for deep learning. Int J Adv Manuf Technol 109(3):953–974. https://doi.org/10.1007/s00170-020-05449-w

    Article  Google Scholar 

  25. Munoa J et al (2016) Chatter suppression techniques in metal cutting. CIRP Ann 65(2):785–808. https://doi.org/10.1016/j.cirp.2016.06.004

    Article  Google Scholar 

  26. Yesilli MC, Khasawneh FA, Otto A (2020) On transfer learning for chatter detection in turning using wavelet packet transform and ensemble empirical mode decomposition. CIRP J Manuf Sci Technol 28:118–135. https://doi.org/10.1016/j.cirpj.2019.11.003

    Article  Google Scholar 

  27. Afazov S, Scrimieri D (2020) Chatter model for enabling a digital twin in machining. Int J Adv Manuf Technol 110(9–10):2439–2444. https://doi.org/10.1007/s00170-020-06028-9

    Article  Google Scholar 

  28. Lei Y, Yang B, Jiang X, Jia F, Li N, Nandi AK (2020) Applications of machine learning to machine fault diagnosis: a review and roadmap. Mech Syst Signal Process 138:106587. https://doi.org/10.1016/j.ymssp.2019.106587

    Article  Google Scholar 

  29. Prasad PS, Senthilrajan A (2021) Leaf features extraction for plant classification using CNN. Int J Adv Res Sci Commun Technol 148–154. https://doi.org/10.48175/IJARSCT-807

    Article  Google Scholar 

  30. Aria M, Cuccurullo C (2017) bibliometrix: an R-tool for comprehensive science mapping analysis. J Informetr 11(4):959–975

    Article  Google Scholar 

  31. Sugimoto CR, Robinson-Garcia N, Murray D, Yegros-Yegros SA, Costas R, Larivière V (2017) Scientists have most impact when they’re free to move. Nature 550(7674):29–31. https://doi.org/10.1038/550029a

  32. Ming Z, Kuanmin M, Bin L, Weiwei X (2014) An analytical method to select spindle speed variation parameters for chatter suppression in NC machining. J Vibroeng 16(1):447–463

    Google Scholar 

  33. Gao J, Song Q, Liu Z (2018) Chatter detection and stability region acquisition in thin-walled workpiece milling based on CMWT. Int J Adv Manuf Technol 98(1–4):699–713. https://doi.org/10.1007/s00170-018-2306-1

    Article  Google Scholar 

  34. Farahani ND, Altintas Y (2022) Chatter stability of serrated milling tools in frequency domain. J Manuf Sci Eng 144(3):031013. https://doi.org/10.1115/1.4052007

    Article  Google Scholar 

  35. Shao Q, Feng CJ (2011) Pattern recognition of chatter gestation based on hybrid PCA-SVM. Appl Mech Mater 120:190–194. https://doi.org/10.4028/www.scientific.net/AMM.120.190

    Article  Google Scholar 

  36. Pan J, Liu Z, Wang X, Chen C, Pan X (2020) Boring chatter identification by multi-sensor feature fusion and manifold learning. Int J Adv Manuf Technol 109(3):1137–1151. https://doi.org/10.1007/s00170-020-05611-4

    Article  Google Scholar 

  37. Hirsch JE (2005) An index to quantify an individual’s scientific research output. Proc Natl Acad Sci 102(46):16569–16572. https://doi.org/10.1073/pnas.0507655102

    Article  MATH  Google Scholar 

  38. Egghe L (2006) Theory and practise of the g-index. Scientometrics 69(1):131–152. https://doi.org/10.1007/s11192-006-0144-7

    Article  MathSciNet  Google Scholar 

  39. Und Halbach OV (2011) How to judge a book by its cover? How useful are bibliometric indices for the evaluation of “scientific quality” or “scientific productivity”? Ann Anat - Anat Anz 193(3):191–196. https://doi.org/10.1016/j.aanat.2011.03.011

    Article  Google Scholar 

  40. Altintas Y, Chan PK (1992) In-process detection and suppression of chatter in milling. Int J Mach Tools Manuf 32(3):329–347. https://doi.org/10.1016/0890-6955(92)90006-3

    Article  Google Scholar 

  41. Teti R, Jemielniak K, O’Donnell G, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Ann 59(2):717–739. https://doi.org/10.1016/j.cirp.2010.05.010

    Article  Google Scholar 

  42. Altintas Y, Weck M (2004) Chatter stability of metal cutting and grinding. CIRP Ann 53(2):619–642. https://doi.org/10.1016/S0007-8506(07)60032-8

    Article  Google Scholar 

  43. Siddhpura M, Paurobally R (2012) A review of chatter vibration research in turning. Int J Mach Tools Manuf 61:27–47. https://doi.org/10.1016/j.ijmachtools.2012.05.007

    Article  Google Scholar 

  44. Delio T, Tlusty J, Smith S (1992) Use of audio signals for chatter detection and control. J Eng Ind 114(2):146–157. https://doi.org/10.1115/1.2899767

    Article  Google Scholar 

  45. Yao Z, Mei D, Chen Z (2010) On-line chatter detection and identification based on wavelet and support vector machine. J Mater Process Technol 210(5):713–719. https://doi.org/10.1016/j.jmatprotec.2009.11.007

    Article  Google Scholar 

  46. Bravo U, Altuzarra O, López de Lacalle LN, Sánchez JA, Campa FJ (2005) Stability limits of milling considering the flexibility of the workpiece and the machine. Int J Mach Tools Manuf 45(15):1669–1680. https://doi.org/10.1016/j.ijmachtools.2005.03.004

    Article  Google Scholar 

  47. Sims ND (2007) Vibration absorbers for chatter suppression: a new analytical tuning methodology. J Sound Vib 301(3):592–607. https://doi.org/10.1016/j.jsv.2006.10.020

    Article  Google Scholar 

  48. Cen L, Melkote SN, Castle J, Appelman H (2018) A method for mode coupling chatter detection and suppression in robotic milling. J Manuf Sci Eng 140(8). https://doi.org/10.1115/1.4040161

    Article  Google Scholar 

  49. Rusinek R, Weremczuk A, Warminski J (2014) Dynamics aspect of chatter suppression in milling. In 11th World Congress on Computational Mechanics; 5th European Conference on Computational Mechanics; 6th European Conference on Computational Fluid Dynamics, Vols Ii - Iv, 08034 Barcelona, p. 3056–3067

  50. Gouskov AM, Voronov SA, Novikov VV, Ivanov II (2017) Chatter suppression in boring with tool position feedback control. J Vibroeng 19(5):3512–3521. https://doi.org/10.21595/jve.2017.17777

    Article  Google Scholar 

  51. Xi S, Cao H, Zhang X, Chen X (2019) Zoom synchrosqueezing transform-based chatter identification in the milling process. Int J Adv Manuf Technol 101(5–8):1197–1213. https://doi.org/10.1007/s00170-018-3002-x

    Article  Google Scholar 

  52. Liu M-K, Tran M-Q, Chung C, Qui Y-W (2020) Hybrid model- and signal-based chatter detection in the milling process. J Mech Sci Technol 34(1):1–10. https://doi.org/10.1007/s12206-019-1201-5

    Article  Google Scholar 

  53. Tao J, Qin C, Liu C (2019) A synchroextracting-based method for early chatter identification of robotic drilling process. Int J Adv Manuf Technol 100(1–4):273–285. https://doi.org/10.1007/s00170-018-2739-6

    Article  Google Scholar 

  54. Tran M-Q, Liu M-K, Tran Q-V (2020) Milling chatter detection using scalogram and deep convolutional neural network. Int J Adv Manuf Technol 107(3):1505–1516. https://doi.org/10.1007/s00170-019-04807-7

    Article  Google Scholar 

  55. Unver HO, Sener B (2021) A novel transfer learning framework for chatter detection using convolutional neural networks. J Intell Manuf. https://doi.org/10.1007/s10845-021-01839-3

    Article  Google Scholar 

  56. Cao H, Zhang X, Chen X (2017) The concept and progress of intelligent spindles: a review. Int J Mach Tools Manuf 112:21–52. https://doi.org/10.1016/j.ijmachtools.2016.10.005

    Article  Google Scholar 

  57. Lamraoui M, Barakat M, Thomas M, Badaoui ME (2015) Chatter detection in milling machines by neural network classification and feature selection. J Vib Control 21(7):1251–1266. https://doi.org/10.1177/1077546313493919

    Article  Google Scholar 

  58. Tansel IN, Li M, Demetgul M, Bickraj K, Kaya B, Ozcelik B (2012) Detecting chatter and estimating wear from the torque of end milling signals by using Index Based Reasoner (IBR). Int J Adv Manuf Technol 58(1–4):109–118. https://doi.org/10.1007/s00170-010-2838-5

    Article  Google Scholar 

  59. Wang M, Fei RY (2001) On-line chatter detection and control in boring based on an electrorheological fluid. Mechatronics 11(7):779–792. https://doi.org/10.1016/S0957-4158(00)00044-1

    Article  Google Scholar 

  60. Cardi AA, Firpi HA, Bement MT, Liang SY (2008) Workpiece dynamic analysis and prediction during chatter of turning process. Mech Syst Signal Process 22(6):1481–1494. https://doi.org/10.1016/j.ymssp.2007.11.026

    Article  Google Scholar 

  61. HongQi L, QingHai C, Bin L, XinYong M, KuanMin M, FangYu P (2011) On-line chatter detection using servo motor current signal in turning. Sci China-Technol Sci 54(12):3119–3129. https://doi.org/10.1007/s11431-011-4595-6

    Article  MATH  Google Scholar 

  62. Chen GS, Zheng QZ (2018) Online chatter detection of the end milling based on wavelet packet transform and support vector machine recursive feature elimination. Int J Adv Manuf Technol 95(1–4):775–784. https://doi.org/10.1007/s00170-017-1242-9

    Article  Google Scholar 

  63. Wan S, Li X, Chen W, Hong J (2018) Investigation on milling chatter identification at early stage with variance ratio and Hilbert-Huang transform. Int J Adv Manuf Technol 95(9–12):3563–3573. https://doi.org/10.1007/s00170-017-1410-y

    Article  Google Scholar 

  64. Nasir V, Cool J (2020) Intelligent wood machining monitoring using vibration signals combined with self-organizing maps for automatic feature selection. Int J Adv Manuf Technol 108(5–6):1811–1825. https://doi.org/10.1007/s00170-020-05505-5

    Article  Google Scholar 

  65. Tarng Y, Li T, Chen M (1994) Online drilling chatter recognition and avoidance using an Art2 - a neural-network. Int J Mach Tools Manuf 34(7):949–957. https://doi.org/10.1016/0890-6955(94)90027-2

    Article  Google Scholar 

  66. Liu J, Hu Y, Wu B, Jin C (2017) A hybrid health condition monitoring method in milling operations. Int J Adv Manuf Technol 92(5–8):2069–2080. https://doi.org/10.1007/s00170-017-0252-y

    Article  Google Scholar 

  67. Wang Y, Bo Q, Liu H, Hu L, Zhang H (2018) Mirror milling chatter identification using Q-factor and SVM. Int J Adv Manuf Technol 98(5–8):1163–1177. https://doi.org/10.1007/s00170-018-2318-x

    Article  Google Scholar 

  68. Zhu W, Zhuang J, Guo B, Teng W, Wu F (2020) An optimized convolutional neural network for chatter detection in the milling of thin-walled parts. Int J Adv Manuf Technol 106(9):3881–3895. https://doi.org/10.1007/s00170-019-04899-1

    Article  Google Scholar 

  69. Dun Y, Zhu L, Yan B, Wang S (2021) A chatter detection method in milling of thin-walled TC4 alloy workpiece based on auto-encoding and hybrid clustering. Mech Syst Signal Process 158:107755. https://doi.org/10.1016/j.ymssp.2021.107755

    Article  Google Scholar 

  70. Plaza EG, López PN, González EZ (2018) Multi-sensor data fusion for real time surface quality control in automated machining systems. Sensors 18(12):4381. https://doi.org/10.3390/s18124381

    Article  Google Scholar 

  71. Shi F, Cao H, Zhang X, Chen X (2020) A reinforced k-nearest neighbors method with application to chatter identification in high-speed milling. IEEE Trans Ind Electron 67(12):10844–10855. https://doi.org/10.1109/TIE.2019.2962465

    Article  Google Scholar 

  72. Bonda AGY, Nanda BK, Jonnalagadda S (2020) Vibration signature-based stability studies in internal turning with a wavelet denoising preprocessor. Measurement 154:107520. https://doi.org/10.1016/j.measurement.2020.107520

    Article  Google Scholar 

  73. Werner RF, Farrelly T (2019) Uncertainty from Heisenberg to today. Found Phys 49(6):460–491. https://doi.org/10.1007/s10701-019-00265-z

    Article  MathSciNet  MATH  Google Scholar 

  74. Mallat S (1989)A theory for multiresolution signal decomposition - the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, No. 7. IEEE Computer Soc 10662 Los Vaqueros circle, PO Box 3014, Los Alamitos, CA 90720–1264, p. 674‑693. https://doi.org/10.1109/34.192463

  75. Susanto A, Yamada K, Tanaka R, Handoko YA, Subhan MF (2020) Chatter identification in turning process based on vibration analysis using Hilbert-Huang transform. J Mech Eng Sci 14(2):6856–6868. https://doi.org/10.15282/14.2.2020.25.0537

    Article  Google Scholar 

  76. Aggarwal S, Chugh N (2019) Signal processing techniques for motor imagery brain computer interface: a review. Array 1–2:100003. https://doi.org/10.1016/j.array.2019.100003

  77. Seyrek P, Şener B, Özbayoğlu AM, Ünver HÖ (2022) An evaluation study of EMD, EEMD, and VMD for chatter detection in milling. Procedia Comput Sci 200:160–174. https://doi.org/10.1016/j.procs.2022.01.215

    Article  Google Scholar 

  78. Liu C, Zhu L, Ni C (2017) The chatter identification in end milling based on combining EMD and WPD. Int J Adv Manuf Technol 91(9):3339–3348. https://doi.org/10.1007/s00170-017-0024-8

    Article  Google Scholar 

  79. Susanto A, Liu C-H, Yamada K, Hwang Y-R, Tanaka R, Sekiya K (2018) Application of Hilbert-Huang transform for vibration signal analysis in end-milling. Precis Eng 53:263–277. https://doi.org/10.1016/j.precisioneng.2018.04.008

    Article  Google Scholar 

  80. Wang H, Ji Y (2018) A revised Hilbert-Huang transform and its application to fault diagnosis in a rotor system. Sensors 18(12):4329. https://doi.org/10.3390/s18124329

    Article  Google Scholar 

  81. Peng W, Hu Z, Yuan L, Zhu P (2013) Chatter identification using HHT for boring process. Beijing, China 904316. https://doi.org/10.1117/12.2037988

  82. Chen Y, Li H, Hou L, Bu X (2019) Feature extraction using dominant frequency bands and time-frequency image analysis for chatter detection in milling. Precis Eng 56:235–245. https://doi.org/10.1016/j.precisioneng.2018.12.004

    Article  Google Scholar 

  83. Uekita M, Takaya Y (2017) Tool condition monitoring technique for deep-hole drilling of large components based on chatter identification in time–frequency domain. Measurement 103:199–207. https://doi.org/10.1016/j.measurement.2017.02.035

    Article  Google Scholar 

  84. Caliskan H, Kilic ZM, Altintas Y (2018) On-line energy-based milling chatter detection. J Manuf Sci Eng 140(11). https://doi.org/10.1115/1.4040617

    Article  Google Scholar 

  85. Lu L, Kurfess T, Saldana C (2021) Effects of extrinsic noise factors on machine learning-based chatter detection in machining. Smart Sustain Manuf Syst 5(1):167–180. https://doi.org/10.1520/SSMS20210007

    Article  Google Scholar 

  86. Fu Y et al (2017) Machining vibration states monitoring based on image representation using convolutional neural networks. Eng Appl Artif Intell 65:240–251. https://doi.org/10.1016/j.engappai.2017.07.024

    Article  Google Scholar 

  87. Zheng Q, Chen G, Jiao A (2022) Chatter detection in milling process based on the combination of wavelet packet transform and PSO-SVM. Int J Adv Manuf Technol. Springer London Ltd, 236 Grays Inn Rd, 6th Floor, London Wc1x 8hl, England. https://doi.org/10.1007/s00170-022-08856-3

  88. Cao H, Zhou K, Chen X, Zhang X (2017) Early chatter detection in end milling based on multi-feature fusion and 3 sigma criterion. Int J Adv Manuf Technol 92(9–12):4387–4397. https://doi.org/10.1007/s00170-017-0476-x

    Article  Google Scholar 

  89. Tarng Y, Chen M (1994) An intelligent sensor for detection of milling chatter. J Intell Manuf 5(3):193–200. https://doi.org/10.1007/BF00123923

    Article  Google Scholar 

  90. Arriaza OV, Tumurkhuyagc Z, Kim D-W (2018) Chatter identification using multiple sensors and multi-layer neural networks. Procedia Manuf 17:150–157. https://doi.org/10.1016/j.promfg.2018.10.030

    Article  Google Scholar 

  91. Kuljanic E, Totis G, Sortino M (2009) Development of an intelligent multisensor chatter detection system in milling. Mech Syst Signal Process 23(5):1704–1718. https://doi.org/10.1016/j.ymssp.2009.01.003

    Article  Google Scholar 

  92. Kumar TP, Saimurugan M, Haran RBH, Siddharth S, Ramachandran KI (2021) A multi-sensor information fusion for fault diagnosis of a gearbox utilizing discrete wavelet features. Meas Sci Technol 30(8):085101. https://doi.org/10.1088/1361-6501/ab0737

    Article  Google Scholar 

  93. Shi W, Jia DK, Liu XL, Yan FG, Li YF (2011) Application of continuous wavelet features and multi-class sphere SVM to chatter prediction. High-Speed Mach Stafa-Zurich 188:675–680. https://doi.org/10.4028/www.scientific.net/AMR.188.675

    Article  Google Scholar 

  94. Shao Q, Feng CJ (2012) Pattern recognition of chatter gestation based on hybrid PCA-SVM. Appl Mech Mater 120:190–194. https://doi.org/10.4028/www.scientific.net/AMM.120.190

    Article  Google Scholar 

  95. Wan S, Li X, Yin Y, Hong J (2021) Milling chatter detection by multi-feature fusion and Adaboost-SVM. Mech Syst Signal Process 156:107671. https://doi.org/10.1016/j.ymssp.2021.107671

    Article  Google Scholar 

  96. Wang Y, Zhang M, Tang X, Peng F, Yan R (2021) A kMap optimized VMD-SVM model for milling chatter detection with an industrial robot. J Intell Manuf. https://doi.org/10.1007/s10845-021-01736-9

    Article  Google Scholar 

  97. Yesilli MC, Khasawneh FA, Otto A (2022) Topological feature vectors for chatter detection in turning processes. Int J Adv Manuf Technol. https://doi.org/10.1007/s00170-021-08242-5

    Article  Google Scholar 

  98. Li D-D, Zhang W-M, Li Y-S, Xue F, Fleischer J (2021) Chatter identification of thin-walled parts for intelligent manufacturing based on multi-signal processing. Adv Manuf 9(1):22–33. https://doi.org/10.1007/s40436-020-00299-x

    Article  Google Scholar 

  99. Khasawneh FA, Munch E, Perea JA (2018) Chatter classification in turning using machine learning and topological data analysis. IFAC-Pap 51(14):195–200. https://doi.org/10.1016/j.ifacol.2018.07.222

    Article  Google Scholar 

  100. E. Wang, P. Yan, Liu J (2020) A Hybrid chatter detection method based on WPD, SSA, and SVM-PSO. Shock Vib 2020:e7943807. https://doi.org/10.1155/2020/7943807

  101. Yesilli MC, Khasawneh FA, Otto A (2022) Chatter detection in turning using machine learning and similarity measures of time series via dynamic time warping. J Manuf Process 77:190–206. https://doi.org/10.1016/j.jmapro.2022.03.009

    Article  Google Scholar 

  102. Schmidt J, Marques MRG, Botti S, Marques MAL (2019) Recent advances and applications of machine learning in solid-state materials science. Npj Comput Mater 5(1):83. https://doi.org/10.1038/s41524-019-0221-0

    Article  Google Scholar 

  103. Glaeser A et al (2021) Applications of deep learning for fault detection in industrial cold forging. Int J Prod Res 59(16):4826–4835. https://doi.org/10.1080/00207543.2021.1891318

    Article  Google Scholar 

  104. Gao HN, Shen DH, Yu L, Zhang WC (2020) Identification of cutting chatter through deep learning and classification. Int J Simul Model 19(4):667–677. https://doi.org/10.2507/IJSIMM19-4-CO16

    Article  Google Scholar 

  105. Xu Y, Li Z, Wang S, Li W, Sarkodie-Gyan T, Feng S (2021) A hybrid deep-learning model for fault diagnosis of rolling bearings. Measurement 169:108502. https://doi.org/10.1016/j.measurement.2020.108502

    Article  Google Scholar 

  106. Rahimi MH, Huynh HN, Altintas Y (2021) On-line chatter detection in milling with hybrid machine learning and physics-based model. CIRP J Manuf Sci Technol 35:25–40. https://doi.org/10.1016/j.cirpj.2021.05.006

    Article  Google Scholar 

  107. Sener B, Gudelek MU, Ozbayoglu AM, Unver HO (2021) A novel chatter detection method for milling using deep convolution neural networks. Measurement 182:109689. https://doi.org/10.1016/j.measurement.2021.109689

    Article  Google Scholar 

  108. Vashisht RK, Peng Q (2020) Online chatter detection for milling operations using LSTM neural networks assisted by motor current signals of ball screw drives. J Manuf Sci Eng 143(1). https://doi.org/10.1115/1.4048001

  109. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  110. Arrieta AB et al (2020) Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf Fusion 58:82–115. https://doi.org/10.1016/j.inffus.2019.12.012

    Article  Google Scholar 

  111. Baltrusaitis T, Ahuja C, Morency L-P (2019) Multimodal machine learning: a survey and taxonomy. IEEE Trans Pattern Anal Mach Intell 41(2):423–443. https://doi.org/10.1109/TPAMI.2018.2798607

    Article  Google Scholar 

  112. Cheng K, Niu Z-C, Wang RC, Rakowski R, Bateman R (2017) Smart cutting tools and smart machining: development approaches, and their implementation and application perspectives. Chin J Mech Eng 30(5):1162–1176. https://doi.org/10.1007/s10033-017-0183-4

    Article  Google Scholar 

  113. Wang C, Cheng K, Rakowski R, Soulard J (2018) An experimental investigation on ultra-precision instrumented smart aerostatic bearing spindle applied to high-speed micro-drilling. J Manuf Process 31:324–335. https://doi.org/10.1016/j.jmapro.2017.11.022

    Article  Google Scholar 

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Authors and Affiliations

  1. Laboratoire Génie de Production, École Nationale d’Ingénieurs de Tarbes, 47 Avenue Azereix, BP 1629, 65016, Tarbes Cedex, France

    Cheick Abdoul Kadir A Kounta, Lionel Arnaud & Bernard Kamsu-Foguem

  2. Faculté Des Sciences Et Techniques, Université Des Sciences, Des Techniques Et Des Technologies de Bamako (USTTB), B.P.E.: 423, Bamako, Mali

    Cheick Abdoul Kadir A Kounta & Fana Tangara

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  1. Cheick Abdoul Kadir A Kounta
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  2. Lionel Arnaud
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  3. Bernard Kamsu-Foguem
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  4. Fana Tangara
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Correspondence to Cheick Abdoul Kadir A Kounta.

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Highlights

• Bibliometric analysis on chatter detection techniques in machining processes.

• Effectiveness of AI methods combined with transformation and decomposition techniques.

• Research areas mainly cover manufacturing, mechanics, and automation control systems.

• Application of signal processing techniques in chatter detection with their advantages.

• Challenges of deep learning models to solve problems of performance and explainability.

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Kounta, C.A.K.A., Arnaud, L., Kamsu-Foguem, B. et al. Review of AI-based methods for chatter detection in machining based on bibliometric analysis. Int J Adv Manuf Technol 122, 2161–2186 (2022). https://doi.org/10.1007/s00170-022-10059-9

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  • DOI: https://doi.org/10.1007/s00170-022-10059-9

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