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A review on deep learning in machining and tool monitoring: methods, opportunities, and challenges

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Abstract

Data-driven methods provided smart manufacturing with unprecedented opportunities to facilitate the transition toward Industry 4.0–based production. Machine learning and deep learning play a critical role in developing intelligent systems for descriptive, diagnostic, and predictive analytics for machine tools and process health monitoring. This paper reviews the opportunities and challenges of deep learning (DL) for intelligent machining and tool monitoring. The components of an intelligent monitoring framework are introduced. The main advantages and disadvantages of machine learning (ML) models are presented and compared with those of deep models. The main DL models, including autoencoders, deep belief networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), were discussed, and their applications in intelligent machining and tool condition monitoring were reviewed. The opportunities of data-driven smart manufacturing approach applied to intelligent machining were discussed to be (1) automated feature engineering, (2) handling big data, (3) handling high-dimensional data, (4) avoiding sensor redundancy, (5) optimal sensor fusion, and (6) offering hybrid intelligent models. Finally, the data-driven challenges in smart manufacturing, including the challenges associated with the data size, data nature, model selection, and process uncertainty, were discussed, and the research gaps were outlined.

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References

  1. Zhong RY, Ge W (2018) Internet of things enabled manufacturing: a review. Int J Agile Syst Manag 11(2):126–154

    Article  Google Scholar 

  2. Yang C, Shen W, Wang X (2018) The internet of things in manufacturing: key issues and potential applications. IEEE Syst Man Cybern Mag 4(1):6–15

    Article  Google Scholar 

  3. Yang C, Shen W, Wang X (2016, May) Applications of Internet of Things in manufacturing. In: 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, pp 670–675

  4. Siderska J, Jadaan KS (2018) Cloud manufacturing: a service-oriented manufacturing paradigm. A review paper. Eng Manag Produc Serv 10(1):22–31

    Google Scholar 

  5. Lee J, Davari H, Singh J, Pandhare V (2018) Industrial artificial intelligence for Industry 4.0-based manufacturing systems. Manuf Lett 18:20–23

    Article  Google Scholar 

  6. Li BH, Hou BC, Yu WT, Lu XB, Yang CW (2017) Applications of artificial intelligence in intelligent manufacturing: a review. Frontiers Inf Technol Electron Eng 18(1):86–96

    Article  Google Scholar 

  7. Kumar SL (2017) State of the art-intense review on artificial intelligence systems application in process planning and manufacturing. Eng Appl Artif Intell 65:294–329

    Article  Google Scholar 

  8. Tao F, Qi Q, Liu A, Kusiak A (2018) Data-driven smart manufacturing. J Manuf Syst 48:157–169

    Article  Google Scholar 

  9. Lin YC, Wu KD, Shih WC, Hsu PK, Hung JP (2020) Prediction of surface roughness based on cutting parameters and machining vibration in end milling using regression method and artificial neural network. Appl Sci 10(11):3941

    Article  Google Scholar 

  10. Bhogal SS, Sindhu C, Dhami SS, Pabla BS (2015) Minimization of surface roughness and tool vibration in CNC milling operation. J Opt 2015:1–13. https://doi.org/10.1155/2015/192030

    Article  MATH  Google Scholar 

  11. Silge, M., & Sattel, T. (2018). Design of contactlessly powered and piezoelectrically actuated tools for non-resonant vibration assisted milling. In Actuators (Vol. 7, 2, p. 19). Multidisciplinary Digital Publishing Institute.

  12. Omair M, Sarkar B, Cárdenas-Barrón LE (2017) Minimum quantity lubrication and carbon footprint: a step towards sustainability. Sustainability 9(5):714

    Article  Google Scholar 

  13. Wang B, Liu Z (2018) Influences of tool structure, tool material and tool wear on machined surface integrity during turning and milling of titanium and nickel alloys: a review. Int J Adv Manuf Technol 98(5-8):1925–1975

    Article  Google Scholar 

  14. Yeganefar A, Niknam SA, Asadi R (2019) The use of support vector machine, neural network, and regression analysis to predict and optimize surface roughness and cutting forces in milling. Int J Adv Manuf Technol 105(1):951–965

    Article  Google Scholar 

  15. Nasir V, Mohammadpanah A, Cool J (2018) The effect of rotation speed on the power consumption and cutting accuracy of guided circular saw: experimental measurement and analysis of saw critical and flutter speeds. Wood Mater Sci Eng 15(3):1–7

    Google Scholar 

  16. 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 108:1811–1825. https://doi.org/10.1007/s00170-020-05505-5

  17. Nasir V, Cool J (2019) Optimal power consumption and surface quality in the circular sawing process of Douglas-fir wood. Eur J Wood Wood Produc 77(4):609–617

    Article  Google Scholar 

  18. 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:1–22

  19. Wang M, Wang J (2012) CHMM for tool condition monitoring and remaining useful life prediction. Int J Adv Manuf Technol 59(5-8):463–471

    Article  Google Scholar 

  20. Brecher C, Esser M, Witt S (2009) Interaction of manufacturing process and machine tool. CIRP Ann 58(2):588–607

    Article  Google Scholar 

  21. Chen W, Liu H, Sun Y, Yang K, Zhang J (2017) A novel simulation method for interaction of machining process and machine tool structure. Int J Adv Manuf Technol 88(9-12):3467–3474

    Article  Google Scholar 

  22. Quintana G, Ciurana J (2011) Chatter in machining processes: a review. Int J Mach Tools Manuf 51(5):363–376

    Article  Google Scholar 

  23. Hegab HA, Darras B, Kishawy HA (2018) Towards sustainability assessment of machining processes. J Clean Prod 170:694–703

    Article  Google Scholar 

  24. Mia M, Gupta MK, Singh G, Królczyk G, Pimenov DY (2018) An approach to cleaner production for machining hardened steel using different cooling-lubrication conditions. J Clean Prod 187:1069–1081

    Article  Google Scholar 

  25. Zhou Z, Yao B, Xu W, Wang L (2017) Condition monitoring towards energy-efficient manufacturing: a review. Int J Adv Manuf Technol 91(9-12):3395–3415

    Article  Google Scholar 

  26. Said Z, Gupta M, Hegab H, Arora N, Khan AM, Jamil M, Bellos E (2019) A comprehensive review on minimum quantity lubrication (MQL) in machining processes using nano-cutting fluids. Int J Adv Manuf Technol 105(5-6):2057–2086

    Article  Google Scholar 

  27. Nasir V, Cool J (2020) Characterization, optimization, and acoustic emission monitoring of airborne dust emission during wood sawing. Int J Adv Manuf Technol 109(9):2365–2375. https://doi.org/10.1007/s00170-020-05842-5

  28. Licow R, Chuchala D, Deja M, Orlowski KA, Taube P (2020) Effect of pine impregnation and feed speed on sound level and cutting power in wood sawing. J Clean Prod 272:122833

    Article  Google Scholar 

  29. Teti R, Jemielniak K, O’Donnell G, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Ann 59(2):717–739

    Article  Google Scholar 

  30. Abellan-Nebot JV, Subirón FR (2010) A review of machining monitoring systems based on artificial intelligence process models. Int J Adv Manuf Technol 47(1-4):237–257

    Article  Google Scholar 

  31. Zhu K, San Wong Y, Hong GS (2009) Wavelet analysis of sensor signals for tool condition monitoring: a review and some new results. Int J Mach Tools Manuf 49(7-8):537–553

    Article  Google Scholar 

  32. Lauro CH, Brandão LC, Baldo D, Reis RA, Davim JP (2014) Monitoring and processing signal applied in machining processes–a review. Measurement 58:73–86

    Article  Google Scholar 

  33. Kusiak A (2019) Fundamentals of smart manufacturing: a multi-thread perspective. Annu Rev Control 47:214–220

    Article  Google Scholar 

  34. Kim DH, Kim TJ, Wang X, Kim M, Quan YJ, Oh JW et al (2018) Smart machining process using machine learning: a review and perspective on machining industry. Int J Precis Eng Manuf Green Technol 5(4):555–568

    Article  Google Scholar 

  35. Ayvaz S, Alpay K (2021) Predictive maintenance system for production lines in manufacturing: a machine learning approach using IoT data in real-time. Expert Syst Appl 173:114598

    Article  Google Scholar 

  36. Morariu C, Morariu O, Răileanu S, Borangiu T (2020) Machine learning for predictive scheduling and resource allocation in large scale manufacturing systems. Comput Ind 120:103244

    Article  Google Scholar 

  37. Adi E, Anwar A, Baig Z, Zeadally S (2020) Machine learning and data analytics for the IoT. Neural Comput & Applic 32:16205–16233

    Article  Google Scholar 

  38. Peng ZK, Chu FL (2004) Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mech Syst Signal Process 18(2):199–221

    Article  Google Scholar 

  39. Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput Electr Eng 40(1):16–28

    Article  Google Scholar 

  40. Nasir V, Cool J, Sassani F (2019) Acoustic emission monitoring of sawing process: artificial intelligence approach for optimal sensory feature selection. Int J Adv Manuf Technol 102(9-12):4179–4197. https://doi.org/10.1007/s00170-019-03526-3

  41. Sick B (2002) On-line and indirect tool wear monitoring in turning with artificial neural networks: a review of more than a decade of research. Mech Syst Signal Process 16(4):487–546

    Article  Google Scholar 

  42. Roth JT, Djurdjanovic D, Yang X, Mears L, Kurfess T (2010) Quality and inspection of machining operations: tool condition monitoring. J Manuf Sci Eng 132(4)

  43. Stavropoulos P, Papacharalampopoulos A, Vasiliadis E, Chryssolouris G (2016) Tool wear predictability estimation in milling based on multi-sensorial data. Int J Adv Manuf Technol 82(1-4):509–521

    Article  Google Scholar 

  44. Nasir V, Kooshkbaghi M, Cool J, Sassani F (2020) Cutting tool temperature monitoring in circular sawing: measurement and multi-sensor feature fusion-based prediction. Int J Adv Manuf Technol 112:2413–2424. https://doi.org/10.1007/s00170-020-06473-6

  45. Nasir V, Cool J, Sassani F (2019) Intelligent machining monitoring using sound signal processed with the wavelet method and a self-organizing neural network. IEEE Robot Autom Lett 4(4):3449–3456

  46. Bhuiyan MSH, Choudhury IA, Dahari M (2014) Monitoring the tool wear, surface roughness and chip formation occurrences using multiple sensors in turning. J Manuf Syst 33(4):476–487

    Article  Google Scholar 

  47. Ahmadi H, Dumont G, Sassani F, Tafreshi R (2003) Performance of informative wavelets for classification and diagnosis of machine faults. Int J Wavelets Multiresolution Inf Process 1(03):275–289

    Article  MathSciNet  MATH  Google Scholar 

  48. Tafreshi R, Sassani F, Ahmadi H, Dumont G (2009) An approach for the construction of entropy measure and energy map in machine fault diagnosis. J Vib Acoust 131(2)

  49. Wuest T, Weimer D, Irgens C, Thoben KD (2016) Machine learning in manufacturing: advantages, challenges, and applications. Produc Manuf Res 4(1):23–45

    Google Scholar 

  50. Hermann G (1990) Artificial intelligence in monitoring and the mechanics of machining. Comput Ind 14(1-3):131–135

    Article  Google Scholar 

  51. Rangwala SS (1987) Integration of sensors via neural networks for detection of tool wear states. Proc Winter Annu Meet ASME 25:109–120

    Google Scholar 

  52. Dornfeld DA, DeVries MF (1990) Neural network sensor fusion for tool condition monitoring. CIRP Ann 39(1):101–105

    Article  Google Scholar 

  53. Rangwala, S., & Dornfeld, D. (1990). Sensor integration using neural networks for intelligent tool condition monitoring, 219-228.

  54. Park KS, Kim SH (1998) Artificial intelligence approaches to determination of CNC machining parameters in manufacturing: a review. Artif Intell Eng 12(1-2):127–134

    Article  Google Scholar 

  55. Dimla DE Jr, Lister PM, Leighton NJ (1997) Neural network solutions to the tool condition monitoring problem in metal cutting—a critical review of methods. Int J Mach Tools Manuf 37(9):1219–1241

    Article  Google Scholar 

  56. Ademujimi TT, Brundage MP, Prabhu VV (2017, September) A review of current machine learning techniques used in manufacturing diagnosis. In: IFIP International Conference on Advances in Production Management Systems. Springer, Cham, pp 407–415

  57. Panchal G, Ganatra A, Shah P, Panchal D (2011) Determination of over-learning and over-fitting problem in backpropagation neural network. Int J Soft Comput 2(2):40–51

    Article  Google Scholar 

  58. Montavon, G., Orr, G., & Müller, K. R. (Eds.). (2012). Neural networks: tricks of the trade (Vol. 7700). springer.

  59. Lopez C (1999) Looking inside the ANN “black box”: classifying individual neurons as outlier detectors. In: IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No. 99CH36339, vol 2. IEEE, pp 1185–1188

  60. Palczewska A, Palczewski J, Robinson RM, Neagu D (2014) Interpreting random forest classification models using a feature contribution method. In: Integration of reusable systems. Springer, Cham, pp 193–218

  61. Nasir V, Kooshkbaghi M, Cool J (2020) Sensor fusion and random forest modeling for identifying frozen and green wood during lumber manufacturing. Manuf Lett 26:53–58

    Article  Google Scholar 

  62. Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828

    Article  Google Scholar 

  63. Faust O, Hagiwara Y, Hong TJ, Lih OS, Acharya UR (2018) Deep learning for healthcare applications based on physiological signals: a review. Comput Methods Prog Biomed 161:1–13

    Article  Google Scholar 

  64. Miotto R, Wang F, Wang S, Jiang X, Dudley JT (2018) Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform 19(6):1236–1246

    Article  Google Scholar 

  65. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  Google Scholar 

  66. Khan S, Yairi T (2018) A review on the application of deep learning in system health management. Mech Syst Signal Process 107:241–265

    Article  Google Scholar 

  67. Wang J, Ma Y, Zhang L, Gao RX, Wu D (2018) Deep learning for smart manufacturing: methods and applications. J Manuf Syst 48:144–156

    Article  Google Scholar 

  68. Zhang N, Ding S, Zhang J, Xue Y (2018) An overview on restricted Boltzmann machines. Neurocomputing 275:1186–1199

    Article  Google Scholar 

  69. Fu Y, Zhang Y, Qiao H, Li D, Zhou H, Leopold J (2015) Analysis of feature extracting ability for cutting state monitoring using deep belief networks. Procedia Cirp 31(Suppl. C):29–34

    Article  Google Scholar 

  70. Chen Y, Jin Y, Jiri G (2018) Predicting tool wear with multi-sensor data using deep belief networks. Int J Adv Manuf Technol 99(5-8):1917–1926

    Article  Google Scholar 

  71. Yu Y, Si X, Hu C, Zhang J (2019) A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput 31(7):1235–1270

    Article  MathSciNet  MATH  Google Scholar 

  72. Xu X, Tao Z, Ming W, An Q, Chen M (2020) Intelligent monitoring and diagnostics using a novel integrated model based on deep learning and multi-sensor feature fusion. Measurement 165:108086

    Article  Google Scholar 

  73. Zhao R, Yan R, Chen Z, Mao K, Wang P, Gao RX (2019) Deep learning and its applications to machine health monitoring. Mech Syst Signal Process 115:213–237

    Article  Google Scholar 

  74. Hahn TV, Mechefske CK (2021) Self-supervised learning for tool wear monitoring with a disentangled-variational-autoencoder. Int J Hydromechatron 4(1):69–98

    Article  Google Scholar 

  75. Xiangyu Z, Lilan L, Xiang W, Bowen F (2021) Tool wear online monitoring method based on DT and SSAE-PHMM. J Comput Inf Sci Eng 21(3):034501

    Article  Google Scholar 

  76. Dou J, Xu C, Jiao S, Li B, Zhang J, Xu X (2020) An unsupervised online monitoring method for tool wear using a sparse auto-encoder. Int J Adv Manuf Technol 106(5):2493–2507

    Article  Google Scholar 

  77. Kim J, Lee H, Jeon JW, Kim JM, Lee HU, Kim S (2020) Stacked auto-encoder based CNC tool diagnosis using discrete wavelet transform feature extraction. Processes 8(4):456

    Article  Google Scholar 

  78. Moldovan OG, Dzitac S, Moga I, Vesselenyi T, Dzitac I (2017) Tool-wear analysis using image processing of the tool flank. Symmetry 9(12):296

    Article  Google Scholar 

  79. Ochoa LEE, Quinde IBR, Sumba JPC, Guevara AV Jr, Morales-Menendez R (2019) New approach based on autoencoders to monitor the tool wear condition in HSM. IFAC-PapersOnLine 52(11):206–211

    Article  Google Scholar 

  80. Proteau A, Zemouri R, Tahan A, Thomas M (2020) Dimension reduction and 2D-visualization for early change of state detection in a machining process with a variational autoencoder approach. Int J Adv Manuf Technol 111(11):3597–3611

    Article  Google Scholar 

  81. Ou J, Li H, Huang G, Zhou Q (2020) A novel order analysis and stacked sparse auto-encoder feature learning method for milling tool wear condition monitoring. Sensors 20(10):2878

    Article  Google Scholar 

  82. Ou J, Li H, Huang G, Yang G (2021) Intelligent analysis of tool wear state using stacked denoising autoencoder with online sequential-extreme learning machine. Measurement 167:108153

    Article  Google Scholar 

  83. Shi C, Panoutsos G, Luo B, Liu H, Li B, Lin X (2018) Using multiple-feature-spaces-based deep learning for tool condition monitoring in ultraprecision manufacturing. IEEE Trans Ind Electron 66(5):3794–3803

    Article  Google Scholar 

  84. He Z, Shi T, Xuan J, Li T (2021) Research on tool wear prediction based on temperature signals and deep learning. Wear 478:203902

    Article  Google Scholar 

  85. Shi C, Luo B, He S, Li K, Liu H, Li B (2019) Tool wear prediction via multidimensional stacked sparse autoencoders with feature fusion. IEEE Trans Ind Informatics 16(8):5150–5159

    Article  Google Scholar 

  86. Sun C, Ma M, Zhao Z, Tian S, Yan R, Chen X (2018) Deep transfer learning based on sparse autoencoder for remaining useful life prediction of tool in manufacturing. IEEE Trans Ind Informatics 15(4):2416–2425

    Article  Google Scholar 

  87. Dun Y, Zhus 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

    Article  Google Scholar 

  88. Yu J, Liu G (2020) Knowledge-based deep belief network for machining roughness prediction and knowledge discovery. Comput Ind 121:103262

    Article  Google Scholar 

  89. Brili N, Ficko M, Klančnik S (2021) Automatic identification of tool wear based on thermography and a convolutional neural network during the turning process. Sensors 21(5):1917

    Article  Google Scholar 

  90. Lee CH, Jwo JS, Hsieh HY, Lin CS (2020) An intelligent system for grinding wheel condition monitoring based on machining sound and deep learning. IEEE Access 8:58279–58289

    Article  Google Scholar 

  91. Gouarir A, Martínez-Arellano G, Terrazas G, Benardos P, Ratchev SJPC (2018) In-process tool wear prediction system based on machine learning techniques and force analysis. Procedia CIRP 77:501–504

    Article  Google Scholar 

  92. Cao XC, Chen BQ, Yao B, He WP (2019) Combining translation-invariant wavelet frames and convolutional neural network for intelligent tool wear state identification. Comput Ind 106:71–84

    Article  Google Scholar 

  93. Song K, Wang M, Liu L, Wang C, Zan T, Yang B (2020) Intelligent recognition of milling cutter wear state with cutting parameter independence based on deep learning of spindle current clutter signal. Int J Adv Manuf Technol 109(3):929–942

    Article  Google Scholar 

  94. Terrazas G, Martínez-Arellano G, Benardos P, Ratchev S (2018) Online tool wear classification during dry machining using real time cutting force measurements and a CNN approach. J Manuf Mater Process 2(4):72

    Google Scholar 

  95. Martínez-Arellano G, Terrazas G, Ratchev S (2019) Tool wear classification using time series imaging and deep learning. Int J Adv Manuf Technol 104(9):3647–3662

    Article  Google Scholar 

  96. Zheng, H., & Lin, J. (2019). A deep learning approach for high speed machining tool wear monitoring. In 2019 3rd International Conference on Robotics and Automation Sciences (ICRAS) (pp. 63-68). IEEE.

  97. Cao X, Chen B, Yao B, Zhuang S (2019) An intelligent milling tool wear monitoring methodology based on convolutional neural network with derived wavelet frames coefficient. Appl Sci 9(18):3912

    Article  Google Scholar 

  98. Mamledesai H, Soriano MA, Ahmad R (2020) A qualitative tool condition monitoring framework using convolution neural network and transfer learning. Appl Sci 10(20):7298

    Article  Google Scholar 

  99. Zhi G, He D, Sun W, Yuqing Z, Pan X, Gao C (2021) An edge-labeling graph neural network method for tool wear condition monitoring using wear image with small samples. Meas Sci Technol 32:064006

    Article  Google Scholar 

  100. Xu X, Wang J, Zhong B, Ming W, Chen M (2021) Deep learning-based tool wear prediction and its application for machining process using multi-scale feature fusion and channel attention mechanism. Measurement 177:109254

    Article  Google Scholar 

  101. Zhang X, Wang S, Li W, Lu X (2021) Heterogeneous sensors-based feature optimisation and deep learning for tool wear prediction. Int J Adv Manuf Technol:1–25

  102. Ambadekar PK, Choudhari CM (2020) CNN based tool monitoring system to predict life of cutting tool. SN Appl Sci 2(5):1–11

    Article  Google Scholar 

  103. Xu X, Wang J, Ming W, Chen M, An Q (2021) In-process tap tool wear monitoring and prediction using a novel model based on deep learning. Int J Adv Manuf Technol 112:453–466

    Article  Google Scholar 

  104. Li P, Jia X, Feng J, Zhu F, Miller M, Chen LY, Lee J (2020) A novel scalable method for machine degradation assessment using deep convolutional neural network. Measurement 151:107106

    Article  Google Scholar 

  105. Huang Z, Zhu J, Lei J, Li X, Tian F (2019) Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations. J Intell Manuf:1–14

  106. Huang Z, Zhu J, Lei J, Li X, Tian F (2019) Tool wear predicting based on multisensory raw signals fusion by reshaped time series convolutional neural network in manufacturing. IEEE Access 7:178640–178651

    Article  Google Scholar 

  107. Wu X, Liu Y, Zhou X, Mou A (2019) Automatic identification of tool wear based on convolutional neural network in face milling process. Sensors 19(18):3817

    Article  Google Scholar 

  108. Tran MQ, Liu MK, Tran QV (2020) Milling chatter detection using scalogram and deep convolutional neural network. Int J Adv Manuf Technol 107(3):1505–1516

    Article  Google Scholar 

  109. 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

    Article  Google Scholar 

  110. Rifai AP, Aoyama H, Tho NH, Dawal SZM, Masruroh NA (2020) Evaluation of turned and milled surfaces roughness using convolutional neural network. Measurement 161:107860

    Article  Google Scholar 

  111. Liu Y, Hu X, Jin J (2019) Remaining useful life prediction of cutting tools based on deep adversarial transfer learning. In: Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition, pp 434–439

  112. Liu H, Liu Z, Jia W, Lin X, Zhang S (2020) A novel transformer-based neural network model for tool wear estimation. Meas Sci Technol 31(6):065106

    Article  Google Scholar 

  113. Zhao R, Wang J, Yan R, Mao K (2016) Machine health monitoring with LSTM networks. In: 2016 10th international conference on sensing technology (ICST), IEEE, pp 1–6

  114. Aghazadeh F, Tahan AS, Thomas M (2019, July) Tool condition monitoring method in milling process using wavelet transform and long short-term memory. In Surveillance, Vishno and AVE conferences

    Google Scholar 

  115. Cai W, Zhang W, Hu X, Liu Y (2020) A hybrid information model based on long short-term memory network for tool condition monitoring. J Intell Manuf 31(6):1497–1510

    Article  Google Scholar 

  116. Zhou JT, Zhao X, Gao J (2019) Tool remaining useful life prediction method based on LSTM under variable working conditions. Int J Adv Manuf Technol 104(9):4715–4726

    Article  Google Scholar 

  117. Gugulothu N, Tv V, Malhotra P, Vig L, Agarwal P, Shroff G (2017) Predicting remaining useful life using time series embeddings based on recurrent neural networks. arXiv preprint arXiv 1709:01073

    Google Scholar 

  118. Yu W, Kim IY, Mechefske C (2019) Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme. Mech Syst Signal Process 129:764–780

    Article  Google Scholar 

  119. Wu X, Li J, Jin Y, Zheng S (2020) Modeling and analysis of tool wear prediction based on SVD and BiLSTM. Int J Adv Manuf Technol 106(9):4391–4399

    Article  Google Scholar 

  120. Wang, J., Yan, J., Li, C., Gao, R. X., & Zhao, R. (2019). Deep heterogeneous GRU model for predictive analytics in smart manufacturing: application to tool wear prediction. Comput Ind, 111, 1-14, 1.

  121. Marani M, Zeinali M, Songmene V, Mechefske CK (2021) Tool wear prediction in high-speed turning of a steel alloy using long short-term memory modelling. Measurement 177:109329

    Article  Google Scholar 

  122. Vashisht RK, Peng Q (2021) 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)

  123. Guo W, Wu C, Ding Z, Zhou Q (2021) Prediction of surface roughness based on a hybrid feature selection method and long short-term memory network in grinding. Int J Adv Manuf Technol 112(9):2853–2871

    Article  Google Scholar 

  124. Chen Q, Xie Q, Yuan Q, Huang H, Li Y (2019) Research on a real-time monitoring method for the wear state of a tool based on a convolutional bidirectional LSTM model. Symmetry 11(10):1233

    Article  Google Scholar 

  125. Ma J, Luo D, Liao X, Zhang Z, Huang Y, Lu J (2021) Tool wear mechanism and prediction in milling TC18 titanium alloy using deep learning. Measurement 173:108554

    Article  Google Scholar 

  126. Zhang X, Lu X, Li W, Wang S (2021) Prediction of the remaining useful life of cutting tool using the Hurst exponent and CNN-LSTM. Int J Adv Manuf Technol 112(7):2277–2299

    Article  Google Scholar 

  127. An Q, Tao Z, Xu X, El Mansori M, Chen M (2020) A data-driven model for milling tool remaining useful life prediction with convolutional and stacked LSTM network. Measurement 154:107461

    Article  Google Scholar 

  128. Babu GS, Zhao P, Li XL (2016) Deep convolutional neural network based regression approach for estimation of remaining useful life. In: International conference on database systems for advanced applications. Springer, Cham, pp 214–228

  129. Qiao H, Wang T, Wang P (2020) A tool wear monitoring and prediction system based on multiscale deep learning models and fog computing. Int J Adv Manuf Technol 108:2367–2384

    Article  Google Scholar 

  130. Niu, J., Liu, C., Zhang, L., & Liao, Y. (2019). Remaining useful life prediction of machining tools by 1D-CNN LSTM network. In 2019 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1056-1063). IEEE.

  131. Zhao R, Yan R, Wang J, Mao K (2017) Learning to monitor machine health with convolutional bi-directional LSTM networks. Sensors 17(2):273

    Article  Google Scholar 

  132. Qiao H, Wang T, Wang P, Qiao S, Zhang L (2018) A time-distributed spatiotemporal feature learning method for machine health monitoring with multi-sensor time series. Sensors 18(9):2932

    Article  Google Scholar 

  133. Wang B, Lei Y, Yan T, Li N, Guo L (2020) Recurrent convolutional neural network: a new framework for remaining useful life prediction of machinery. Neurocomputing 379:117–129

    Article  Google Scholar 

  134. Zhang X, Lu X, Li W, Wang S (2021) Prediction of the remaining useful life of cutting tool using the Hurst exponent and CNN-LSTM. Int J Adv Manuf Technol:1–23

  135. Misaka T, Herwan J, Kano S, Sawada H, Furukawa Y (2020) Deep neural network-based cost function for metal cutting data assimilation. Int J Adv Manuf Technol 107(1):385–398

    Article  Google Scholar 

  136. Qiao H, Wang T, Wang P, Zhang L, Xu M (2019) An adaptive weighted multiscale convolutional neural network for rotating machinery fault diagnosis under variable operating conditions. IEEE Access 7:118954–118964

    Article  Google Scholar 

  137. Jiang G, He H, Yan J, Xie P (2018) Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox. IEEE Trans Ind Electron 66(4):3196–3207

    Article  Google Scholar 

  138. Li DC, Wen IH, Chen WC (2016) A novel data transformation model for small data-set learning. Int J Prod Res 54(24):7453–7463

    Article  Google Scholar 

  139. Kusiak A (2017 Apr) Smart manufacturing must embrace big data. Nature. 544(7648):23–25

    Article  Google Scholar 

  140. Taiebat M, Sassani F (2017 Sep) Distinguishing sensor faults from system faults by utilizing minimum sensor redundancy. Trans Can Soc Mech Eng 41(3):469–487

    Article  Google Scholar 

  141. Nasir V, Cool J (2020) A review on wood machining: characterization, optimization, and monitoring of the sawing process. Wood Material Sci Eng 15(1):1–16

    Article  Google Scholar 

  142. Diez-Olivan A, Del Ser J, Galar D, Sierra B (2019 Oct 1) Data fusion and machine learning for industrial prognosis: trends and perspectives towards Industry 4.0. Inf Fusion 50:92–111

    Article  Google Scholar 

  143. Ferguson MK, Ronay AK, Lee YTT, Law KH (2018) Detection and segmentation of manufacturing defects with convolutional neural networks and transfer learning. Smart Sustain Manuf Systems 2:20180033. https://doi.org/10.1520/SSMS20180033

    Article  Google Scholar 

  144. Imoto K, Nakai T, Ike T, Haruki K, Sato Y (2018) A CNN-based transfer learning method for defect classification in semiconductor manufacturing. In: 2018 International Symposium on Semiconductor Manufacturing (ISSM). IEEE, pp 1–3

  145. Wang P, Gao RX (2020) Transfer learning for enhanced machine fault diagnosis in manufacturing. CIRP Ann 69(1):413–416

    Article  Google Scholar 

  146. Caggiano A (2018) Cloud-based manufacturing process monitoring for smart diagnosis services. Int J Comput Integr Manuf 31(7):612–623

    Article  Google Scholar 

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  1. Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada

    Vahid Nasir & Farrokh Sassani

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  1. Vahid Nasir
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  2. Farrokh Sassani
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Conceptualization: Vahid Nasir (V. N.) and Farrokh Sassani (F. S.); literature review (V. N.); manuscript writing (V. N. and F. S.); editing and final review (F. S.).

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Nasir, V., Sassani, F. A review on deep learning in machining and tool monitoring: methods, opportunities, and challenges. Int J Adv Manuf Technol 115, 2683–2709 (2021). https://doi.org/10.1007/s00170-021-07325-7

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