Tool wear monitoring in milling of titanium alloy Ti–6Al–4 V under MQL conditions based on a new tool wear categorization method

  • Meng Hu
  • Weiwei Ming
  • Qinglong An
  • Ming ChenEmail author


Tool wear monitoring is crucial during machining of difficult-to-cut materials to save cost and improve efficiency. In this paper, a tool wear–monitoring strategy was proposed for milling of titanium alloy Ti–6Al–4 V under inner minimum quantity lubrication (MQL) conditions. Unlike the usual categorization method, tool wear was categorized into four states based on tool wear mechanism, tool wear rate, and tool life. Thus, more detailed information of tool could be predicted for tool wear monitoring. Cutting forces and acoustic emission were measured online as raw datasets. Statistical features were extracted from time and frequency domain, and mutual information (MI) was used for feature selection. Then, linear discriminant analysis (LDA) was adopted for dimensionality reduction and finding the optimal datasets for training. At last, ν-Support vector machine (ν-SVM) was applied for training and prediction. The proposed strategy had a prediction accuracy of 98.9%, which could be considered as valid and useful for tool wear monitoring.


Tool wear categorization Cutting forces Acoustic emission MQL Tool wear monitoring 


Funding information

The work is supported by the National Natural Science Foundation of China (No.51875355, 51675204 and 51875356), Shanghai Science and Technology Committee Major Program (17DZ1101202), and State Key Laboratory of Mechanical System and Vibration (MSVZD201801).


  1. 1.
    Davim JP (2014) Machining of titanium alloys. Springer, BerlinGoogle Scholar
  2. 2.
    Ezugwu EO, Bonney J, Yamane Y (2003) An overview of the machinability of aeroengine alloys. J Mater Process Technol 134(2):233–253CrossRefGoogle Scholar
  3. 3.
    Hong SY, Markus I, Jeong W (2001) New cooling approach and tool life improvement in cryogenic machining of titanium alloy Ti-6Al-4 V. Int J Mach Tools Manufac 41(15):2245–2260CrossRefGoogle Scholar
  4. 4.
    Liu Z, An Q, Xu J, Chen M, Han S (2013) Wear performance of (nc-AlTiN)/(a-Si3N4) coating and (nc-AlCrN)/(a-Si3N4) coating in high-speed machining of titanium alloys under dry and minimum quantity lubrication (MQL) conditions. Wear 305(1):249–259CrossRefGoogle Scholar
  5. 5.
    An Q, Fu Y, Xu J (2011) Experimental study on turning of TC9 titanium alloy with cold water mist jet cooling. Int J Mach Tools Manufac 51(6):549–555CrossRefGoogle Scholar
  6. 6.
    Sharma A, Tiwari A, Dixit A (2016) Effects of minimum quantity lubrication (MQL) in machining processes using conventional and nanofluid based cutting fluids: a comprehensive review. J Clean Prod 127:1–18CrossRefGoogle Scholar
  7. 7.
    Weinert K, Inasaki I, Sutherland J, Wakabayashi T (2004) Dry machining and minimum quantity lubrication. CIRP Ann 53(2):511–537CrossRefGoogle Scholar
  8. 8.
    Park K, Suhaimi M, Yang G, Lee D, Lee S, Kwon P (2017) Milling of titanium alloy with cryogenic cooling and minimum quantity lubrication (MQL). Int J Precis Eng Manuf 18(1):5–14CrossRefGoogle Scholar
  9. 9.
    Cai X, Liu Z, Chen M, An Q (2012) An experimental investigation on effects of minimum quantity lubrication oil supply rate in high-speed end milling of Ti–6Al–4 V. Proc Inst Mech Eng Part B-J Eng Manuf 226(11):1784–1792CrossRefGoogle Scholar
  10. 10.
    Liu Z, Chen M, An Q (2015) Investigation of friction in end-milling of Ti-6Al-4 V under different green cutting conditions. Int J Adv Manuf Technol 78(5):1181–1192CrossRefGoogle Scholar
  11. 11.
    Zhou Y., Xue W. (2018a) A multisensor fusion method for tool condition monitoring in milling. Sensors 18(11):3866CrossRefGoogle Scholar
  12. 12.
    Aliustaoglu C, Ertunc H, Ocak H (2009) Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system. Mech Syst Signal Process 23(2):539–546CrossRefGoogle Scholar
  13. 13.
    Teti R, Jemielniak K, O’Donnell G, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Ann 59(2):717–739CrossRefGoogle Scholar
  14. 14.
    Lauro C, Brandão L, Baldo D, Reis R, Davim J (2014) Monitoring and processing signal applied in machining processes – a review. Measurement 58(58):73–86CrossRefGoogle Scholar
  15. 15.
    Zhu K, Wong Y, Hong G (2009) Wavelet analysis of sensor signals for tool condition monitoring: a review and some new results. Int J Mach Tools Manufac 49(7):537–553CrossRefGoogle Scholar
  16. 16.
    Rizal M, Ghani J, Nuawi M, Haron C (2017) Cutting tool wear classification and detection using multi-sensor signals and Mahalanobis-Taguchi System. Wear 376-377:1759–1765CrossRefGoogle Scholar
  17. 17.
    Xie Z, Li J, Lu Y (2018) Feature selection and a method to improve the performance of tool condition monitoring. Int J Adv Manuf Technol 100(9-12):3197–3206CrossRefGoogle Scholar
  18. 18.
    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(C):47–58CrossRefGoogle Scholar
  19. 19.
    Kong D, Chen Y, Li N, Tan S (2017) Tool wear monitoring based on kernel principal component analysis and v -support vector regression. Int J Adv Manuf Technol 89(1-4):1–16CrossRefGoogle Scholar
  20. 20.
    Zhou Y, Xue W (2018b) Review of tool condition monitoring methods in milling processes. Int J Adv Manuf Technol 96(5):2509–2523CrossRefGoogle Scholar
  21. 21.
    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 16(6):795CrossRefGoogle Scholar
  22. 22.
    Binsaeid S, Asfour S, Cho S, Onar A (2009) Machine ensemble approach for simultaneous detection of transient and gradual abnormalities in end milling using multisensor fusion. J Mater Process Technol 209(10):4728–4738CrossRefGoogle Scholar
  23. 23.
    Hsieh W, Lu M, Chiou S (2012) Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling. Int J Adv Manuf Technol 61(1):53–61CrossRefGoogle Scholar
  24. 24.
    García E, Núñez P (2018) Application of the wavelet packet transform to vibration signals for surface roughness monitoring in CNC turning operations. Mech Syst Signal Process 98:902–919CrossRefGoogle Scholar
  25. 25.
    Griffin J, Diaz F, Geerling E, Clasing M, Ponce V, Taylor C, Turner S, Michael E, Patricio F, Bronfman L (2017) Control of deviations and prediction of surface roughness from micro machining of THz waveguides using acoustic emission signals. Mech Syst Signal Process 85:1020–1034CrossRefGoogle Scholar
  26. 26.
    Liu M., Tseng Y., Tran M. (2019) Tool wear monitoring and prediction based on sound signal. Int J Adv Manuf Technol 103(9):3361–3373CrossRefGoogle Scholar
  27. 27.
    Zhu K, Hong G, Wong Y (2008) A comparative study of feature selection for hidden markov model-based micro-milling tool wear monitoring. Mach Sci Technol 12(3):348–369CrossRefGoogle Scholar
  28. 28.
    Qin W., Zha D., Zhang J. (2018) An effective approach for causal variables analysis in diesel engine production by using mutual information and network deconvolution. J Intell Manuf 1–11.
  29. 29.
    Abellan-Nebot J, Romero F (2010) A review of machining monitoring systems based on artificial intelligence process models. Int J Adv Manuf Technol 47(1):237–257CrossRefGoogle Scholar
  30. 30.
    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):39–51CrossRefGoogle Scholar
  31. 31.
    Wang W, Hong G, Wong Y, Zhu K (2007) Sensor fusion for online tool condition monitoring in milling. Int J Prod Res 45(21):5095–5116CrossRefGoogle Scholar
  32. 32.
    Alexandre F, Lopes W, Dotto F, Ferreira F, Aguiar P, Bianchi E, Lopes J (2018) Tool condition monitoring of aluminum oxide grinding wheel using AE and fuzzy model. Int J Adv Manuf Technol 96(1-4):1–13CrossRefGoogle Scholar
  33. 33.
    Kaya B, Oysu C, Ertunc H, Ocak H (2012) A support vector machine-based online tool condition monitoring for milling using sensor fusion and a genetic algorithm. Proc Inst Mech Eng Part B-J Eng Manuf 226(11):1808–1818CrossRefGoogle Scholar
  34. 34.
    Atlas L, Ostendorf M, Bernard G (2000) In Hidden Markov models for monitoring machining tool-wear, In Proceedings of 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Istanbul. pp. 3887–3890Google Scholar
  35. 35.
    Benkedjouh T, Medjaher K, Zerhouni N, Rechak S (2013) Health assessment and life prediction of cutting tools based on support vector regression. J Intell Manuf 26(2):213–223CrossRefGoogle Scholar
  36. 36.
    Proteau A, Tahan A, Thomas M (2019) Specific cutting energy: a physical measurement for representing tool wear. Int J Adv Manuf Technol 103(1):101–110CrossRefGoogle Scholar
  37. 37.
    Kannatey-Asibu E, Yum J, Kim T (2017) Monitoring tool wear using classifier fusion. Mech Syst Signal Process 85:651–661CrossRefGoogle Scholar
  38. 38.
    Ross B (2014) Mutual information between discrete and continuous data sets. PLoS One 9(2):e87357CrossRefGoogle Scholar
  39. 39.
    Jin X, Zhao M, Chow T, Pecht M (2014) Motor bearing fault diagnosis using trace ratio linear discriminant analysis. IEEE Trans Ind Electron 61(5):2441–2451CrossRefGoogle Scholar
  40. 40.
    Scholkopf B, Smola A, Williamson R, Bartlett P (2000) New support vector algorithms. Neural Comput 12(5):1207–1245CrossRefGoogle Scholar
  41. 41.
    Chang C, Lin C (2007) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):27CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Mechanical System and Vibration, School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghaiPeople’s Republic of China

Personalised recommendations