Detecting tool wear conditions in milling process is of significance to enhance the reliability of machining equipment. However, traditional methods have run into difficulties due to interference from strong noise and other unknown vibration sources. To solve this problem, an intrinsic timescale decomposition (ITD) technique is combined with a kernel extreme learning machine (KELM) technique. In this method, ITD is firstly employed to decompose multiple sensor signals into several sets of proper rotation (PR) components. Next, the optimal PR component of each set is selected by correlation coefficient analysis. A series of feature sets are then constructed according to the data indicators extracted from the selected PR components in time and frequency domains. Finally, the feature sets are fed into the KELM, which classifies the tool wear conditions. Experimental investigations are conducted to determine three stages of tool wear in the milling process; the ITD-KELM method achieved 93.28% classification accuracy, which verifies its feasibility and effectiveness for detecting tool wear. The superior performance of the proposed method is further demonstrated by comparing it with four other methods: ITD-based SVM, EEMD-based KELM, VMD-based KELM, and KELM.
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Xu GD, Zhou HC, Chen JH (2018) CNC internal data based incremental cost-sensitive support vector machine method for tool breakage monitoring in end milling. Eng Appl Artif Intell 74:90–103
Yu JS, Shuang L, Tang DY, Liu H (2016) A weighted hidden Markov model approach for continuous-state tool wear monitoring and tool life prediction. Int Adv Manuf Tech 91:1–11
Kong DD, Chen YJ, Li N, Duan CQ, Lu LX, Chen DX (2019) Relevance vector machine for tool wear prediction. Mech Syst Signal Pr 127:573–594
Jain AK, Lad BK (2017) A novel integrated tool condition monitoring system. J Intell Manuf 3:1–14
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
Xu GD, Chen JH, Zhou HC (2018) A tool breakage monitoring method for end milling based on the indirect electric data of CNC system. Int J Adv Manuf Technol 101:419–434
Garcia-Ordas MT, Alegre-Gutierrez E, Alaiz-Rodriguez R, Gonzalez-Castro V (2018) Tool wear monitoring using an online, automatic and low cost system based on local texture. Mech Syst Signal Pr 112:98–112
Abellan-Nebot JV, Subiron FR (2010) A review of machining monitoring systems based on artificial intelligence process models. Int J Adv Manuf Technol 47(1-4):237–257
Klaic M, Murat Z, Staroveski T, Brezak D (2018) Tool wear monitoring in rock drilling applications using vibration signals. Wear 408:222–227
Benkedjouh T, Zerhouni N, Rechak S (2018) Tool wear condition monitoring based on continuous wavelet transform and blind source separation. Int J Adv Manuf Technol 97(9-12):3311–3323
Ravikumar S, Ramachandran KI (2018) Tool wear monitoring of multipoint cutting tool using sound signal features signals with machine learning techniques. Mater Today: Proceedings 5:25720–25729
Wang CD, Bao ZL, Zhang PQ, Ming WW, Chen M (2019) Tool wear evaluation under minimum quantity lubrication by clustering energy of acoustic emission burst signals. Measurement 138:256–265
Kovac P, Gostimirovic M, Rodic D, Savkovic B (2019) Using the temperature method for the prediction of tool life in sustainable production. Measurement 133:320–327
Rizal M, Ghani JA, Nuawi MZ, Haron CHC (2017) Cutting tool wear classification and detection using multi-sensor signals and Mahalanobis-Taguchi system. Wear 376:1759–1765
Albertelli P, Goletti M, Torta M, Salehi M, Monno M (2016) Model-based broadband estimation of cutting forces and tool vibration in milling through in-process indirect multiple-sensors measurements. Int J Adv Manuf Technol 82:779–796
Uekita M, Takaya Y (2017) Tool condition monitoring for form milling of large parts by combining spindle motor current and acoustic emission signals. Int J Adv Manuf Technol 89(1-4):65–75
Salimiasl A, Erdem A, Rafighi M (2017) Applying a multi sensor system to predict and simulate the tool wear using of artificial neural networks. Sci Iran 24:2864–2874
Zhou YQ, Xue W (2018) Review of tool condition monitoring methods in milling processes. Int J Adv Manuf Technol 96(5-8):2509–2523
Weichert D, Link P, Stoll A, Rüping S, Ihlenfeldt S, Wrobel S (2019) A review of machine learning for the optimization of production processes. Int J Adv Manuf Technol
Zhao R, Yan R, Chen Z, Mao K, Wang P, Gao RX (2016) Deep learning and its applications to machine health monitoring: a survey. Mech Syst Signal Process 115:213–237
Palanisamy P, Rajendran I, Shanmugasundaram S (2008) Prediction of tool wear using regression and ANN models in end-milling operation. Int J Adv Manuf Technol 37(1-2):29–41
Yu JS, Liang S, Tang DY, Liu H (2017) A weighted hidden Markov model approach for continuous-state tool wear monitoring and tool life prediction. Int J Adv Manuf Technol 91(1-4):201–211
Kong DD, Chen YJ, Li N (2017) Hidden semi-Markov model-based method for tool wear estimation in milling process. Int J Adv Manuf Technol 92(9-12):3647–3657
Lin XK, Zhou B, Zhu L (2017) Sequential spindle current-based tool condition monitoring with support vector classifier for milling process. Int J Adv Manuf Technol 92(9-12):3319–3328
Hsueh YW, Yang CY (2008) Prediction of tool breakage in face milling using support vector machine. Int J Adv Manuf Technol 37(9-10):872–880
Kong DD, Chen YJ, Li N, Tan SL (2017) Tool wear monitoring based on kernel principal component analysis and v-support vector regression. Int J Adv Manuf Technol 89(1-4):175–190
Zhang N and Ding S F 2017 Unsupervised and semi-supervised extreme learning machine with wavelet kernel for high dimensional data Memet. Comput. 9129-39
Yu H, Li HR, Zai K et al (2017) Rolling bearing fault trend prediction based on composite weighted KELM Int. J Acoust Vib 23:217–225
Long XF, Yang P, Guo HX, Zhao ZL, Wu XW (2019) A CBA-KELM-based recognition method for fault diagnosis of wind turbines with time-domain analysis and multisensor data fusion. Shock Vib 11:1–14
Chi YJ, Dai W, Lu ZY, Wang MQ, Zhao Y (2018) Real-time estimation for cutting tool wear based on modal analysis of monitored signals. Appl Sci-Basel 8(5)
Li JM, Yao XF, Wang H, Zhang JF (2019) Periodic impulses extraction based on improved adaptive VMD and sparse code shrinkage denoising and its application in rotating machinery fault diagnosis. Mech Syst Signal Pr 126:568–589
Fan J, Zhencai Z, Wei L (2018) An improved VMD with empirical mode decomposition and its application in incipient fault detection of rolling bearing. IEEE Access 6:44483–44493
Wang YX, Yang L, Xiang JW, He SL, Yang JW (2017) A hybrid approach to fault diagnosis of roller bearings under variable speed conditions. Meas Sci Technol 28(12)
Frei MG (2078) Osorio I (2007) Intrinsic time-scale decomposition: time-frequency-energy analysis and real-time filtering of non-stationary signals. P Roy Soc A-Math Phy 463:321–342
Hu AJ, Xiang L, Gao N (2017) Fault diagnosis for the gearbox of wind turbine combining ensemble intrinsic time-scale decomposition with Wigner bi-spectrum entropy. J Vibroeng 19(3):1759–1770
Xing ZQ, Qu JF, Chai Y, Tang Q, Zhou YM (2017) Gear fault diagnosis under variable conditions with intrinsic time-scale decomposition-singular value decomposition and support vector machine. J Mech Sci Technol 31(2):545–553
Jemielniak K, Urbanski T, Kossakowska J, Bombinski S (2012) Tool condition monitoring based on numerous signal features. Int J Adv Manuf Technol 59(1-4):73–81
Painuli S, Elangovan M, Sugumaran V (2014) Tool condition monitoring using K-star algorithm. Expert Syst Appl 41(6):2638–2643
Wang SH, Xiang JW, Zhong YT, Tang HS (2018) A data indicator-based deep belief networks to detect multiple faults in axial piston pumps. Mech Syst Signal Pr 112:154–170
Ouadine AY, Mjahed M, Ayad H, EI-Kari A (2019) Helicopter gearbox vibration fault classification using order tracking method and genetic algorithm. Automatika 60(1): 68-78
Ren HJ, Yin AJ, Zhou Q, Li J, Hu YH (2019) A wind turbine bearing performance evaluation method based on similarity analysis of fuzzy k-principal curves in manifold space. IEEE Access 7:36154–36163
Baliarsingh SK, Vipsita S, Muhammad K, Dash B, Bakshi S (2019) Analysis of high-dimensional genomic data employing a novel bio-inspired algorithm. Appl Soft Comput 77:520–532
Liu JW, Li Q, Chen WR, Yan Y, Wang XT (2019) A fast fault diagnosis method of the pemfc system based on extreme learning machine and dempster–shafer evidence theory. IEEE T Transp Electr 5(1):271–284
Liu XW, Wang L, Huang GB, Zhang J, Yin JP (2015) Multiple kernel extreme learning machine. Neurocomputing 149:253–264
Koseki S, Inoue K, Sekiya K, Morito S, Usuki H (2017) Wear mechanisms of PVD-coated cutting tools during continuous turning of Ti-6Al-4 V alloy. Precis Eng 47:434–444
Zhu KP, Mei T, Ye DS (2015) Online condition monitoring in micromilling: A force waveform shape analysis approach. IEEE T Ind Electron 62(6):3806–3813
This work was supported by National Natural Science Foundation of China (Grant No. 51405346 and 71471139), the Zhejiang Provincial Natural Science Foundation of China (No. LY17E050005), and the Wenzhou City Public Industrial Science and technology project of China (No. G20180026). We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.
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Lei, Z., Zhou, Y., Sun, B. et al. An intrinsic timescale decomposition-based kernel extreme learning machine method to detect tool wear conditions in the milling process. Int J Adv Manuf Technol 106, 1203–1212 (2020). https://doi.org/10.1007/s00170-019-04689-9
- Milling process
- Tool condition
- Intrinsic timescale decomposition
- Kernel extreme learning machine