Skip to main content
SpringerLink
Log in

Tool monitoring of end milling based on gap sensor and machine learning

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Tool wear is a detrimental circumstance in end milling and estimating its occurrence in machinery is an onerous process. Indirect tool monitoring has been actively studied to identify instances of wear on the cutting tool based on the signal from a sensor that represents the tool condition. Runout of a machine spindle during machining as a result of a defective tool commonly occurs in the metal cutting process. In this study, gap sensors were installed at the machine spindle to measure the runout. Two types of tool conditions and four cutting depths were considered during end milling to identify the relation between the spindle runout, cutting depth, and tool condition based on the gap sensor signal. Statistical features were extracted from the signals obtained, and a feature selection technique was applied to identify the ideal features as an input for the machine learning (ML) algorithms, specifically support vector machine (SVM) and multi-layer perceptron neural network (MLP NN). The SVM models were evaluated through k-fold cross-validation, while stochastic learning was applied to the MLP NN models to obtain the most compatible algorithm for the binary classification. The performance of SVM and MLP NN algorithms in classifying the signal based on the tool condition was studied and compared. The SVM outperformed the MLP NN in terms of classification accuracy, F1-score, precision, and sensitivity for all datasets despite the minimal parameter assignment in the former.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Akinosho TD, Oyedele LO, Bilal M, Ajayi AO, Delgado MD, Akinade OO, Ahmed AA (2020) Deep learning in the construction industry: a review of present status and future innovations. J Build Eng 32:101827

    Article  Google Scholar 

  • Atli AV, Urhan O, Ertürk S, Sönmez M (2006) A computer vision-based fast approach to drilling tool condition monitoring. Proc Inst Mech Eng Part B 220:1409–1415

    Article  Google Scholar 

  • Bahr B, Motavalli S, Arfi T (1997) Sensor fusion for monitoring machine tool conditions. Int J Comput Integr Manuf 10(5):314–323. https://doi.org/10.1080/095119297131066

    Article  Google Scholar 

  • Cartas-Rosado R, Becerra-Luna B, Martínez-Memijea R et al (2020) Continuous wavelet transform based processing for estimating the power spectrum content of heart rate variability during hemodiafiltration. Biomed Signal Process Control 62:102031

    Article  Google Scholar 

  • 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 45:241–249

    Article  Google Scholar 

  • Cho S, Binsaeid S, Asfour S (2010) Design of multisensor fusion-based tool condition monitoring system in end milling. Int J Adv Manuf Technol 46:681–694

    Article  Google Scholar 

  • Cooper C, Wang P, Zhang J, Gao RX, Roney T, Ragai I, Shaffer D (2019) Convolutional neural network-based tool condition monitoring in vertical milling operations using acoustic signals. In: 8th International Conference on Through-Life Engineering Service-TESConf 2019, Procedia Manufacturing, pp 105–111

  • Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297

    MATH  Google Scholar 

  • Drouillet C, Karandikar J, Nath C, Journeaux A-C, Mansori ME, Kurfess T (2016) Tool life predictions in milling using spindle power with the neural network technique. J Manuf Process 22:161–168

    Article  Google Scholar 

  • García PE, Núñez LP (2017) Surface roughness monitoring by singular spectrum analysis of vibration signals. J Mech Syst Signal Process 84:516–530

    Article  Google Scholar 

  • Gholami R, Fakhari N (2017) Support vector machine: principles, parameters, and applications. In: Handbook of neural computational. Academic Press, London, pp 515–535. https://doi.org/10.1016/B978-0-12-811318-9.00027-2

  • Ghori KM, Imran M, Nawaz A, Abbasi RA, Ullah A, Szathmary L (2019) Performance analysis of machine learning classifers for non-technical loss detection. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-019-01649-9

    Article  Google Scholar 

  • Ghosh N, Ravi YB, Patra A, Mukhopadhyay S, Paul S, Mohanty AR, Chattopadhyay AB (2007) Estimation of tool wear during CNC milling using neural network-based sensor fusion. Mech Syst Signal Process 21(1):466–479

    Article  Google Scholar 

  • Guyon I, Weston J, Barnhill S (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46:389–422

    Article  Google Scholar 

  • Heinemann R, Hinduja S (2012) A new strategy for tool condition monitoring of small diameter twist drills in deep-hole drilling. Int J Mach Tools Manuf 52:69–76

    Article  Google Scholar 

  • Hesser DF, Markert B (2019) Tool wear monitoring of a retrofitted CNC milling machine using artificial neural networks. Manuf Lett 19:1–4

    Article  Google Scholar 

  • Hou Q, Sun J, Huang P (2019) A novel algorithm for tool wear online inspection based on machine vision. Int J Adv Manuf Technol 101(9–12):2415–2423

    Article  Google Scholar 

  • Hsueh YW, Yang CY (2009) Tool breakage diagnosis in face milling by support vector machine. J Mater Process Technol 209:145–152

    Article  Google Scholar 

  • Khaire UM, Dhanalakshmi (2019) Stability of feature selection algorithm: a review. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2019.06.012

    Article  MATH  Google Scholar 

  • Kothuru A, Nooka SP, Liu R (2019) Application of deep visualization in CNN-based tool condition monitoring for end milling. Procedia Manuf 34:995–1004

    Article  Google Scholar 

  • Kovac P, Gostimironic M, Rodic D, Savkovic B (2019) Using the temperature method for the prediction of tool life in sustainable production. Measurement 133:320–327

    Article  Google Scholar 

  • Lee WK, Ratnam MM, Ahmad ZA (2017) Detection of chipping in ceramic cutting inserts from workpiece profile during turning using fast Fourier transform (FFT) and continuous wavelet transform (CWT). Precis Eng 47:406–423

    Article  Google Scholar 

  • Mohanraj T, Shankar S, Rajasekar R, Sakthivel NR, Pramanik A (2020) Tool condition monitoring techniques in milling process—a review. J Mater Res Technol 9(1):1032–1042

    Article  Google Scholar 

  • Niu J, Peng J, Ding Y, Zhu L (2018) Evaluation indicators of the runout effects on milling forces and regenerative stability. Procedia CIRP 77:98–101

    Article  Google Scholar 

  • Niu B, Sun J, Yang B (2020) Multisensory based tool wear monitoring for practical applications in milling of titanium alloy. Mater Today 22:1209–1217

    Google Scholar 

  • Obuchowski J, Zimroz R, Wyłomańska A (2016) Blind equalization using combined skewness–kurtosis criterion for gearbox vibration enhancement. Measurement 88:34–44

    Article  Google Scholar 

  • Ravindra HV, Srinivasa YG, Krishnamurthy R (1997) Acoustic emission for tool condition monitoring in metal cutting. Wear 212:78–84

    Article  Google Scholar 

  • 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 26:693–710

    Article  Google Scholar 

  • Richhariya B, TanveerM RAH (2020) Diagnosis of Alzheimer’s disease using universum support vector machine based recursive feature elimination (USVM-RFE). Biomed Signal Process Control 59:101903

    Article  Google Scholar 

  • Sun S, Hu X, Cai W, Zhong J (2019) Tool breakage detection of milling cutter insert based on SVM. Int Feder Autom Control 52(13):1549–1554

    Google Scholar 

  • Susai MJ, Sai BMA, Krishnakumari A, Nakandhrakumar RS, Dinakaran D (2019) Monitoring of drill runout using least square support vector machine classifier. Measurement 146:24–34

    Article  Google Scholar 

  • Taylor FW (1906) On the art of cutting metals, 3rd edn. The American Society of Mechanical Engineers, New York

    Google Scholar 

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

    Article  Google Scholar 

  • Vaidya S, Ambad P, Bhosle S (2018) Industry 4.0—a glimpse. Procedia Manuf 20:233–238

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • worldbank.org (2018) 4.2 World Development Indicators: Structure of Output. http://wdi.worldbank.org/table/4.2#; 2020. (Accessed 10 Apr 2020)

  • Zhou Y, Sun B, Sun W (2020) A tool condition monitoring method based on two-layer angle kernel extreme learning machine and binary differential evolution for milling. Measurement 166:108186

    Article  Google Scholar 

  • Zhu KP, Wong YS, Hong GS (2009) Wavelet analysis of sensor signals for tool condition monitoring: a review and some new results. Int J Mach Tools Manuf 49:537–553

    Article  Google Scholar 

Download references

Acknowledgements

This research was funded by “Development of ICT-based smart machine tools and flexible automation systems” of the Ministry of Trade Industry and Energy (MOTIE), Korea [Grant No. 10060188J]. The authors would like to acknowledge this funding.

Author information

Authors and Affiliations

  1. Department of Nano Fusion Technology, Pusan National University, 2 Busandaehak-ro, 63 beon-gil, Geumjeonggu, 46241, Republic of Korea

    Siti Nurfadilah Binti Jaini & Yongseung Kwon

  2. Department of Nano Energy Engineering, Pusan National University, 2 Busandaehak-ro, 63 beon-gil, Geumjeonggu, 46241, Republic of Korea

    Deugwoo Lee

  3. Industrial Liaison Innovation Center, Pusan National University, 2 Busandaehak-ro, 63 beon-gil, Geumjeonggu, 46241, Republic of Korea

    Seungjun Lee

  4. Dongnam Regional Division, Korea Institute of Industrial Technology (KITECH), 25, Yeonkkot-ro, 165 beon-gil, Jeongchon-myeon, Jinju-si, Gyeongsangnam-do, 52845, Republic of Korea

    Miru Kim

Authors
  1. Siti Nurfadilah Binti Jaini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Deugwoo Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Seungjun Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Miru Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yongseung Kwon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Deugwoo Lee.

Ethics declarations

Conflict of interest

The authors declare that there was no competing of interest financially or personal relationship that involved in the work mentioned in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Fig. 10 and Table 7.

Fig. 10
figure 10

Feature selection based on RFECV method and linear SVM. Skew and kur are ranked first in all cutting depths data

Full size image
Table 7 Binary classification performance of linear SVM with skew and kurt as input datasets
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jaini, S.N.B., Lee, D., Lee, S. et al. Tool monitoring of end milling based on gap sensor and machine learning. J Ambient Intell Human Comput 12, 10615–10627 (2021). https://doi.org/10.1007/s12652-020-02875-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-020-02875-2

Keywords

Search

Navigation