An intrinsic timescale decomposition-based kernel extreme learning machine method to detect tool wear conditions in the milling process

Abstract

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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References

  1. 1.

    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

    Google Scholar 

  2. 2.

    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

    Google Scholar 

  3. 3.

    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

    Google Scholar 

  4. 4.

    Jain AK, Lad BK (2017) A novel integrated tool condition monitoring system. J Intell Manuf 3:1–14

    Google Scholar 

  5. 5.

    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

    Google Scholar 

  6. 6.

    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

    Google Scholar 

  7. 7.

    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

    Google Scholar 

  8. 8.

    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

    Google Scholar 

  9. 9.

    Klaic M, Murat Z, Staroveski T, Brezak D (2018) Tool wear monitoring in rock drilling applications using vibration signals. Wear 408:222–227

    Google Scholar 

  10. 10.

    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

    Google Scholar 

  11. 11.

    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

    Google Scholar 

  12. 12.

    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

    Google Scholar 

  13. 13.

    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

    Google Scholar 

  14. 14.

    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

    Google Scholar 

  15. 15.

    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

    Google Scholar 

  16. 16.

    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

    Google Scholar 

  17. 17.

    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

    Google Scholar 

  18. 18.

    Zhou YQ, Xue W (2018) Review of tool condition monitoring methods in milling processes. Int J Adv Manuf Technol 96(5-8):2509–2523

    Google Scholar 

  19. 19.

    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

  20. 20.

    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

    Google Scholar 

  21. 21.

    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

    Google Scholar 

  22. 22.

    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

    Google Scholar 

  23. 23.

    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

    Google Scholar 

  24. 24.

    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

    Google Scholar 

  25. 25.

    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

    Google Scholar 

  26. 26.

    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

    Google Scholar 

  27. 27.

    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

  28. 28.

    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

    Google Scholar 

  29. 29.

    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

    Google Scholar 

  30. 30.

    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)

    Google Scholar 

  31. 31.

    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

    Google Scholar 

  32. 32.

    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

    Google Scholar 

  33. 33.

    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)

    Google Scholar 

  34. 34.

    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

    Google Scholar 

  35. 35.

    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

    Google Scholar 

  36. 36.

    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

    Google Scholar 

  37. 37.

    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

    Google Scholar 

  38. 38.

    Painuli S, Elangovan M, Sugumaran V (2014) Tool condition monitoring using K-star algorithm. Expert Syst Appl 41(6):2638–2643

    Google Scholar 

  39. 39.

    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

    Google Scholar 

  40. 40.

    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

    Google Scholar 

  41. 41.

    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

    Google Scholar 

  42. 42.

    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

    Google Scholar 

  43. 43.

    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

    Google Scholar 

  44. 44.

    Liu XW, Wang L, Huang GB, Zhang J, Yin JP (2015) Multiple kernel extreme learning machine. Neurocomputing 149:253–264

    Google Scholar 

  45. 45.

    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

    Google Scholar 

  46. 46.

    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

    Google Scholar 

Download references

Funding

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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Correspondence to Yuqing Zhou.

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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

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Keywords

  • Milling process
  • Tool condition
  • Intrinsic timescale decomposition
  • Kernel extreme learning machine