Abstract
Tool condition monitoring and machine tool diagnostics are performed using advanced sensors and computational intelligence to predict and avoid adverse conditions for cutting tools and machinery. Undesirable conditions during machining cause chatter, tool wear, and tool breakage, directly affecting the tool life and consequently the surface quality, dimensional accuracy of the machined parts, and tool costs. Tool condition monitoring is, therefore, extremely important for manufacturing efficiency and economics. Acoustic emission, vibration, power, and temperature sensors monitor the stability and efficiency of the machining process, collecting large amounts of data to detect tool wear, breakage, and chatter. Studies on monitoring the vibrations and acoustic emissions from machine tools have provided information and data regarding the detection of undesirable conditions. Herein, studies on tool condition monitoring are reviewed and classified. As Industry 4.0 penetrates all manufacturing sectors, the amount of manufacturing data generated has reached the level of big data, and classical artificial intelligence analyses are no longer adequate. Nevertheless, recent advances in deep learning methods have achieved revolutionary success in numerous industries. Deep multi-layer perceptron (DMLP), long-short-term memory (LSTM), convolutional neural network (CNN), and deep reinforcement learning (DRL) are among the most preferred methods of deep learning in recent years. As data size increases, these methods have shown promising performance improvement in prediction and learning, compared to classical artificial intelligence methods. This paper summarizes tool condition monitoring first, then presents the underlying theory of some of the most recent deep learning methods, and finally, attempts to identify new opportunities in tool condition monitoring, toward the realization of Industry 4.0.
Similar content being viewed by others
References
Byrne G, Dornfeld D, Inasaki I, Ketteler G, König W, Teti R (1995) Tool condition monitoring (TCM) - the status of research and industrial application. CIRP Ann Manuf Technol 44(2):541–567
Zhang C, Yao X, Zhang J, Jin H (2016) Tool condition monitoring and remaining useful life prognostic based on a wireless sensor in dry milling operations. Sensors (Switzerland) 16(6)
Roth JT, Djurdjanovic D, Yang X, Mears L, Kurfess T (2010) Quality and inspection of machining operations: tool condition monitoring. J Manuf Sci Eng Trans ASME 132(4):0410151–04101516
Siddhpura A, Paurobally R (2013) A review of flank wear prediction methods for tool condition monitoring in a turning process. Int J Adv Manuf Technol 65(1–4):371–393
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
Dan L, Mathew J (1990) Tool wear and failure monitoring techniques for turning-a review. Int J Mach Tools Manuf 30(4):579–598
Ghani JA, Rizal M, Nuawi MZ, Ghazali MJ, Haron CHC (2011) Monitoring online cutting tool wear using low-cost technique and user-friendly GUI. Wear 271(9–10):2619–2624
Dutta S, Pal SK, Sen R (2016) Tool condition monitoring in turning by applying machine vision. J Manuf Sci Eng Trans ASME 138(5):1–17
Ambhore N, Kamble D, Chinchanikar S, Wayal V (2015) Tool condition monitoring system: a review. Mater Today Proc 2(4–5):3419–3428
Teti R, Jemielniak K, O’Donnell G, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Ann Manuf Technol 59(2):717–739
Rao CHS, Rao DN, Rao RNS (2006) Online prediction of diffusion wear on the flank through tool tip temperature in turning using artificial neural networks. Proc Inst Mech Eng B J Eng Manuf 220(12):2069–2076
Serin G, Gudelek MU, Ozbayoglu AM, Unver HO (2017) Estimation of parameters for the free-form machining with deep neural network. In: 2017 IEEE International Conference on Big Data (Big Data), pp 2102–2111. https://doi.org/10.1109/BigData.2017.8258158
Widodo A, Yang BS (2007) Support vector machine in machine condition monitoring and fault diagnosis. Mech Syst Signal Process 21(6):2560–2574
Saimurugan M, Ramachandran KI, Sugumaran V, Sakthivel NR (2011) Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine. Expert Syst Appl 38(4):3819–3826
Haykin SS (1999) Neural networks : a comprehensive foundation. Prentice Hall
Lauro CH, Brandão LC, Baldo D, Reis RA, Davim JP (2014) Monitoring and processing signal applied in machining processes - a review. Meas J Int Meas Confed 58:73–86
Kothuru A, Nooka SP, Liu R (2018) Audio-based condition monitoring in milling of the workpiece material with the hardness variation using support vector machines and convolutional neural networks, ASME 2018 13th Int. Manuf. Sci. Eng. Conf. MSEC 2018, vol. 4, no. November 2018, pp 1–9
Kothuru A, Nooka SP, Liu R (2018) Application of audible sound signals for tool wear monitoring using machine learning techniques in end milling. Int J Adv Manuf Technol 95(9–12):3797–3808
Olufayo O, Abou-El-Hossein K (2015) Tool life estimation based on acoustic emission monitoring in end-milling of H13 mould-steel. Int J Adv Manuf Technol 81(1–4):39–51
Li X (2002) A brief review: acoustic emission method for tool wear monitoring during turning. Int J Mach Tools Manuf 42(2):157–165
Kakade S, Vijayaraghavan L, Krishnamurthy R (1994) In-process tool wear and chip-form monitoring in face milling operation using acoustic emission. J Mater Process Technol 44(3–4):207–214
Marinescu I, Axinte DA (2008) A critical analysis of effectiveness of acoustic emission signals to detect tool and workpiece malfunctions in milling operations. Int J Mach Tools Manuf 48(10):1148–1160
Antić A, Popović B, Krstanović L, Obradović R, Milošević M (2018) Novel texture-based descriptors for tool wear condition monitoring. Mech Syst Signal Process 98:1–15
Du Kim J, Choi IH (1996) Development of a tool failure detection system using multi-sensors. Int J Mach Tools Manuf 36(8):861–870
Wu D, Jennings C, Terpenny J, Kumara S, Gao RX (2018) Cloud-based parallel machine learning for tool wear prediction. J Manuf Sci Eng Trans ASME 140(4):1–10
Zhang XY, Lu X, Wang S, Wang W, Li WD (2018) A multi-sensor based online tool condition monitoring system for milling process. Procedia CIRP 72:1136–1141
Chen SL, Jen YW (2000) Data fusion neural network for tool condition monitoring in CNC milling machining. Int J Mach Tools Manuf 40(3):381–400
Mali R, Telsang MT, Gupta TVK (2017) Real time tool wear condition monitoring in hard turning of Inconel 718 using sensor fusion system. Mater Today Proc 4(8):8605–8612
Kious M, Ouahabi A, Boudraa M, Serra R, Cheknane A (2010) Detection process approach of tool wear in high speed milling. Meas J Int Meas Confed 43(10):1439–1446
Rmili W, Ouahabi A, Serra R, Leroy R (2016) An automatic system based on vibratory analysis for cutting tool wear monitoring. Meas J Int Meas Confed 77:117–123
Liang SY, Hecker RL, Landers RG (2002) Machining process monitoring and control: the state-of-the-art. ASME Int Mech Eng Congr Expo Proc 126(May 2004):599–610
Abellan-Nebot JV, Romero Subirón F (2010) A review of machining monitoring systems based on artificial intelligence process models. Int J Adv Manuf Technol 47(1–4):237–257
Dimla DE, Lister PM (2000) On-line metal cutting tool condition monitoring. II: tool-state classification using multi-layer perceptron neural networks. Int J Mach Tools Manuf 40(5):769–781
Bombiński S, Błazejak K, Nejman M, Jemielniak K (2016) Sensor signal segmentation for tool condition monitoring. Procedia CIRP 46:155–160
Nouri M, Fussell BK, Ziniti BL, Linder E (2015) Real-time tool wear monitoring in milling using a cutting condition independent method. Int J Mach Tools Manuf 89:1–13
Cho S, Asfour S, Onar A, Kaundinya N (2005) Tool breakage detection using support vector machine learning in a milling process. Int J Mach Tools Manuf 45(3):241–249
Wang J, Xie J, Zhao R, Zhang L, Duan L (2017) Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing. Robot Comput Integr Manuf 45:47–58
Caggiano A, Angelone R, Napolitano F, Nele L, Teti R (2018) Dimensionality reduction of sensorial features by principal component analysis for ANN machine learning in tool condition monitoring of CFRP drilling. Procedia CIRP 78:307–312
Rizal M, Ghani JA, Nuawi MZ, Haron CHC (2013) Online tool wear prediction system in the turning process using an adaptive neuro-fuzzy inference system. Appl Soft Comput J 13(4):1960–1968
Venkata Rao K, Murthy BSN, Mohan Rao N (2013) Cutting tool condition monitoring by analyzing surface roughness, work piece vibration and volume of metal removed for AISI 1040 steel in boring. Meas J Int Meas Confed 46(10):4075–4084
Özel T, Karpat Y (Apr. 2005) Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. Int J Mach Tools Manuf 45(4–5):467–479
Korkut I, Acir A, Boy M (Sep. 2011) Application of regression and artificial neural network analysis in modelling of tool-chip interface temperature in machining. Expert Syst Appl 38(9):11651–11656
Onal AC, Berat Sezer O, Ozbayoglu M, Dogdu E (2018) MIS-IoT: modular intelligent server based Internet of Things framework with big data and machine learning, in Proceedings - 2018 IEEE International Conference on Big Data. Big Data 2019:2270–2279
Smola A, Vishwanathan SVN, Clara S (2008) Introduction to machine learning, 1st edn. Cambridge University Press, United Kingdom
Russell SJ, Norvig P, Canny JF, Malik JM, Edwards DD (1995) Artificial intelligence: a modern approach, 3rd edn. Pearson Education Inc., USA
I. Goodfellow, Y. Bengio, and A Courville, (2016) Deep learning. Available: https://www.deeplearningbook.org/.
Singh N, Singh DP, Pant B (2018) A comprehensive study of big data machine learning approaches and challenges. Proc - 2017 Int Conf Next Gener Comput Inf Syst ICNGCIS 2017:58–63
Soniya SP, Singh L (2016) A review on advances in deep learning. 2015 IEEE Work Comput Intell Theor Appl Futur Dir WCI 2015:1–6
Deshmukh PS (2018) Travel time prediction using neural networks: a literature review, 2018 Int. Conf. Information, Commun. Eng. Technol. ICICET 2018, no. xi, pp 1–5
Bhandare A, Bhide M, Gokhale P, Chandavarkar R (2016) Applications of convolutional neural networks. Int J Comput Sci Inf Technol 7(5):2206–2215
Ramos S, Gehrig S, Pinggera P, Franke U, Rother C (2016) Detecting unexpected obstacles for self-driving cars: fusing deep learning and geometric modeling. IEEE Intelligent Vehicles Symposium
Pak M, Kim S (2018) A review of deep learning in image recognition, Proc. 2017 4th Int. Conf. Comput. Appl. Inf. Process. Technol. CAIPT 2017, vol. 2018-Janua, pp 1–3
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Adv Neural Inf Proces Syst. https://doi.org/10.1145/3065386
Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks, Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 8689 LNCS, no. PART 1, pp 818–833
Szegedy C, Liu W, Jia YQ, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 07-12-June, pp 1–9
Molchanov P, Gupta S, Kim K, Kautz J (2015) Hand gesture recognition with 3D convolutional neural networks. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol 2015-October, pp 1–7
Molchanov P, Yang X, Gupta S, Kim K, Tyree S, Kautz J (2016) Online detection and classification of dynamic hand gestures with recurrent 3D convolutional neural networks. In: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-Decem, pp 4207–4215
X. Chai, Z. Liu, F. Yin, Z. Liu, and X. Chen, Two streams recurrent neural networks for large-scale continuous gesture recognition, Proc Int Conf Pattern Recogn, vol. 0, pp. 31–36, 2016.
Wang P, Li W, Liu S, Gao Z, Tang C, Ogunbona P (2017) Large-scale isolated gesture recognition using convolutional neural networks. https://doi.org/10.1109/ICPR.2016.7899599
Khalajzadeh H, Mansouri M, Teshnehlab M (2014) Face recognition using convolutional neural network and simple logistic classifier, in Advances in Intelligent Systems and. Computing 223:197–207
Zhang X, Peng M, Chen T (2016) Face recognition from near-infrared images with convolutional neural network, 2016 8th Int. Conf Wirel Commun Signal Process WCSP 2016:1–5
Ramaiah NP, Ijjina EP, Mohan CK (2015) Illumination invariant face recognition using convolutional neural networks. In: 2015 IEEE Int. Conf. Signal Process. Informatics, Commun. Energy Syst. SPICES 2015, pp 1–4
Pinheiro PO, Com RC (2014) B1b_CRFasRNN 32
Wang P, Cao Y, Shen C, Liu L, Shen HT (2015) Temporal pyramid pooling based convolutional neural networks for action recognition. IEEE Trans Circ Syst Video Technol. https://doi.org/10.1109/TCSVT.2016.2576761
Simonyan K, Zisserman A Two-stream convolutional networks for action recognition in videos. Adv Neural Inf Proces Syst
Yang Q, He Z, Ge F, Zhang Y (2017) Sequence-to-sequence prediction of personal computer software by recurrent neural network. In: Proceedings of the International Joint Conference on Neural Networks, vol 2017-May, pp 934–940
Sharma P, Singh A (2017) Era of deep neural networks: a review, 8th Int. Conf Comput Commun Netw Technol 2017
N. Neverova, C. Wolf, G. Paci, G. Sommavilla, G. W. Taylor, and F. Nebout, A multi-scale approach to gesture detection and recognition, Proc IEEE Int Conf Comput Vis, pp. 484–491, 2013.
Tsironi E, Barros P, Wermter S (2016) Gesture recognition with a convolutional long short-term memory recurrent neural network, ESANN 2016 - 24th Eur. Symp. Artif. Neural Networks, pp 213–218
Hochreiter S, Schmidhuber J (Nov. 1997) Long short-term memory. Neural Comput 9(8):1735–1780
A. Graves, A. Mohamed, and G. Hinton (2013) Speech recognition with deep recurrent neural networks, IEEE International Conference on Acoustics, Speech and Signal Processing. https://doi.org/10.1109/ICASSP.2013.6638947
Sundermeyer M, Ney H, Schluter R (2015) From feedforward to recurrent LSTM neural networks for language modeling. IEEE Trans Audio Speech Lang Process 23(3):517–529
Mayer NM (2017) Echo state condition at the critical point. Entropy 19(1)
Ororbia A, Mikolov T, Reitter D (2017) Learning simpler language models with the delta recurrent neural network framework. Arxiv:1–27
Day MY, Da Lin Y (2017) Deep learning for sentiment analysis on Google play consumer review. In: Proc. - 2017 IEEE Int. Conf. Inf. Reuse Integr. IRI 2017, vol 2017-Janua, pp 382–388
Arbib MA (1995) The handbook of brain theory and neural networks. MIT Press
Sutton RS, Barto AG (2017) Reinforcement learning an introduction. The MIT press
Aghazadeh F, Tahan A, Thomas M (2018) Tool condition monitoring using spectral subtraction and convolutional neural networks in milling process. Int J Adv Manuf Technol 98(9–12):3217–3227
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
Shao SY, Sun WJ, Yan RQ, Wang P, Gao RX (2017) A deep learning approach for fault diagnosis of induction motors in manufacturing. Chin J Mech Eng (English Ed) 30(6):1347–1356
Riera-Guasp M, Antonino-Daviu JA, Capolino GA (2015) Advances in electrical machine, power electronic, and drive condition monitoring and fault detection: state of the art. IEEE Trans Ind Electron 62(3):1746–1759
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
Li X, Zhang W, Ding Q, Sun JQ (2018) Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation. J Intell Manuf
Fu Y, Zhang Y, Gao H, Mao T, Zhou H, Sun R, Li D (2019) Automatic feature constructing from vibration signals for machining state monitoring. J Intell Manuf 30(3):995–1008
Liu H, Li L, Ma J (2016) Rolling bearing fault diagnosis based on STFT-deep learning and sound signals. Shock Vib. https://doi.org/10.1155/2016/6127479
Jia F, Lei Y, Guo L, Lin J, Xing S (Jan. 2018) A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines. Neurocomputing 272:619–628
Chen Z, Deng S, Chen X, Li C, Sanchez RV, Qin H (2017) Deep neural networks-based rolling bearing fault diagnosis. Microelectron Reliab 75:327–333
Deng S, Cheng Z, Li C, Yao X, Chen Z, Sanchez RV (2017) Rolling bearing fault diagnosis based on deep Boltzmann machines. In: Proceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016
Li C, Sánchez RV, Zurita G, Cerrada M, Cabrera D (2016) Fault diagnosis for rotating machinery using vibration measurement deep statistical feature learning. Sensors (Switzerland) 16(6)
Guo X, Chen L, Shen C (Nov. 2016) Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis. Meas J Int Meas Confed 93:490–502
Fuan W, Hongkai J, Haidong S, Wenjing D, Shuaipeng W (2017) An adaptive deep convolutional neural network for rolling bearing fault diagnosis. Meas Sci Technol 28(9)
Dornfeld DA, DeVries MF (1990) Neural network sensor fusion for tool condition monitoring. CIRP Ann Manuf Technol 39(1):101–105
Hsieh WH, Lu MC, Chiou SJ (2012) Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling. Int J Adv Manuf Technol 61(1–4):53–61
Srinivasan A, Dornfeld D, Bhinge R (2016) Integrated vibration and acoustic data fusion for chatter and tool condition classification in milling, in International Symposium on Flexible Automation, ISFA 201, pp 263–266
Quiza R, Figueira L, Davim JP (2008) Comparing statistical models and artificial neural networks on predicting the tool wear in hard machining D2 AISI steel. Int J Adv Manuf Technol 37(7–8):641–648
Silva RG, Wilcox SJ, Reuben RL (2006) Development of a system for monitoring tool wear using artificial intelligence techniques. Proc Inst Mech Eng B J Eng Manuf 220(8):1333–1346
Rangwala S, Dornfeld D (1990) Sensor integration using neural networks for intelligent tool condition monitoring. J Manuf Sci Eng Trans ASME 112(3):219–228
Griffin JM, Doberti AJ, Hernández V, Miranda NA, Vélez MA (2017) Multiple classification of the force and acceleration signals extracted during multiple machine processes: part 1 intelligent classification from an anomaly perspective. Int J Adv Manuf Technol 93(1-4):811–823
Griffin JM, Doberti AJ, Hernández V, Miranda NA, Vélez MA (2017) Multiple classification of the force and acceleration signals extracted during multiple machine processes: part 2 intelligent classification from an anomaly perspective. Int J Adv Manuf Technol 92(9-12):3207–3217
Rehorn AG, Jiang J, Orban PE (2005) State-of-the-art methods and results in tool condition monitoring: a review. Int J Adv Manuf Technol 26(7-8):693–710
T. Mohanraj, S. Shankar, R. Rajasekar, N.R. Sakthivel, and A. Pramanik, Tool condition monitoring techniques in milling process—a review, J Mater Res Technol, In Press, 2019.
Zhou Y, Xue W (2018) Review of tool condition monitoring methods in milling processes. Int J Adv Manuf Technol 96(5-8):2509–2523
Duro JA, Padget JA, Bowen CR, Kim HA, Nassehi A (2016) Multi-sensor data fusion framework for CNC machining monitoring. Mech Syst Signal Process 66:505–520
Li X, Hu Y, Li M, Zheng J (2020) Fault diagnostics between different type of components: a transfer learning approach. Appl Soft Comput 86(105950)
Funding
This study was funded by The Scientific and Technological Research Council of Turkey (TUBITAK) through project grant no. 118M414.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Serin, G., Sener, B., Ozbayoglu, A.M. et al. Review of tool condition monitoring in machining and opportunities for deep learning. Int J Adv Manuf Technol 109, 953–974 (2020). https://doi.org/10.1007/s00170-020-05449-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-020-05449-w