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Force-based tool condition monitoring for turning process using v-support vector regression

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Abstract

In this paper, comprehensive analysis has been carried out to seek out the effective features which can reveal the tool conditions when turning 50# normalized steel. Tool failure mechanism arising in the cutting processes shows that flank wear is the most common failure mode which is taken as the object in this study. Fourteen time-domain features sensitive to tool wear are picked out by utilizing correlation analysis. There are two kinds of tool wear condition, coded as 0 and 1, which is distinguished by the blunt standard. The predictive v-support vector regression (v-SVR)-based model is constructed to monitor the tool wear conditions. Experimental results show that the prediction accuracy of the v-SVR model reaches up to 96.76%. Besides, the v-SVR model has better prediction effect and stability than the GRNN- and BPNN-based models.

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References

  1. Li D, Mathew J (1990) Tool wear and failure monitoring techniques for turning—a review. Int J Mach Tools Manuf 30(4):579–598

    Article  Google Scholar 

  2. 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

    Article  Google Scholar 

  3. 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 271:2619–2624

    Article  Google Scholar 

  4. Oraby SE (1995) Monitoring of turning operation via force signals part 1: recognition of different tool failure forms by spectral analysis. Wear 184:133–143

    Article  Google Scholar 

  5. Lin SC, Ting CJ (1995) Tool wear monitoring in drilling using force signals. Wear 180:53–60

    Article  Google Scholar 

  6. Lin SC, Lin RJ (1996) Tool wear monitoring in face milling using force signals. Wear 198:136–142

    Article  Google Scholar 

  7. Axinte DA, Gindy N (2003) Tool condition monitoring in broaching. Wear 254:370–382

    Article  Google Scholar 

  8. Shi DF, Gindy NN (2007) Tool wear predictive model based on least squares support vector machines. Mech Syst Signal Process 21(4):1799–1814

    Article  Google Scholar 

  9. Fang N, Pai P, Mosquea S (2011) Effect of tool edge wear on the cutting forces and vibrations in high-speed finish machining of inconel 718: an experimental study and wavelet transform analysis. Int J Adv Manuf Technol 52:65–77

    Article  Google Scholar 

  10. Kong DD, Chen YJ, Li N, Tan SL (2016) Tool wear monitoring based on kernel principal component analysis and v-support vector regression. Int J Adv Manuf Technol. doi:10.1007/s00170-016-9070-x

    Google Scholar 

  11. Dimla SDE (2002) The correlation of vibration signal features to cutting tool wear in a metal turning operation. Int J Adv Manuf Technol 19:705–713

    Article  Google Scholar 

  12. Chelladurai H, Jain V, Vyas N (2008) Development of a cutting tool condition monitoring system for high speed turning operation by vibration and strain analysis. Int J Adv Manuf Technol 37:471–485

    Article  Google Scholar 

  13. Prasad B, Sarcar M, Ben B (2010) Development of a system for monitoring tool condition using acousto-optic emission signal in face turning—an experimental approach. Int J Adv Manuf Technol 51:57–67

    Article  Google Scholar 

  14. Ding F, He Z (2011) Cutting tool wear monitoring for reliability analysis using proportional hazards model. Int J Adv Manuf Technol 57:565–574

    Article  Google Scholar 

  15. Ku XC, Zhou YM, Gao PL, Duan MD (2014) Recognition of tool wear state based on wavelet packet and BP neural network. Modern Manufacturing Engineering 12:68–72

    Google Scholar 

  16. Choudhury SK, Bartarya G (2003) Role of temperature and surface finish in predicting tool wear using neural network and design of experiments. Int J Mach Tools Manuf 43(7):747–753

    Article  Google Scholar 

  17. Singh D, Rao PV (2010) Flank wear prediction of ceramic tools in hard turning. Int J Adv Manuf Technol 50:479–493

    Article  Google Scholar 

  18. Ravindra HV, Srinivasa YG, Krishnamurthy R (1997) Acoustic emission for tool condition monitoring in metal cutting. Wear 212(1):78–84

    Article  Google Scholar 

  19. Li XL, Yuan ZJ (1998) Tool wear monitoring with wavelet packet transform—fuzzy clustering method. Wear 219(2):145–154

    Article  Google Scholar 

  20. Chen XZ, Li BZ (2007) Acoustic emission method for tool condition monitoring based on wavelet analysis. Int J Adv Manuf Technol 33:968–976

    Article  Google Scholar 

  21. Bhuiyan M, Choudhury I, Yusoff N (2012) A new approach to investigate tool condition using dummy tool holder and sensor setup. Int J Adv Manuf Technol 61:465–479

    Article  Google Scholar 

  22. Jemielniak K, Urbański T, Kossakowska J, Bombiński S (2012) Tool condition monitoring based on numerous signal features. Int J Adv Manuf Technol 59:73–81

    Article  Google Scholar 

  23. Li XL, Tso SK (1999) Drill wear monitoring based on current signals. Wear 231(2):172–178

    Article  Google Scholar 

  24. Qiu Y, Xie FY (2014) Tool wear monitoring based on wavelet packet coefficient and hidden Markov model. Hydromechatronics Engineering 42(12):40–44

    Google Scholar 

  25. Li GS, Lau WS, Zhang YZ (1992) In-process drill wear and breakage monitoring for a machining center based on cutting force parameters. Int J Mach Tools Manuf 32(6):855–867

    Article  Google Scholar 

  26. 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 65:371–393

    Article  Google Scholar 

  27. Qian YQ, Tian J, Liu LB, Zhang Y, Chen YS (2010) A tool wear predictive model based on SVM. In: 2010 Chinese control and decision conference, pp 1213–1217

  28. Wang LW, Wang J (2012) Cloud-SVM model and its application in the prediction of CNC machine tool wear state. Modular Machine Tool & Automatic Manufacturing Technique 09:25–31

    Google Scholar 

  29. Nie P, Dong H, Li ZQ, Gao H, Li B (2013) State recognition of tool wear based on improved empirical mode decomposition and least squares support vector machine. Journal of Beijing University of technology 39(12):1784–1790

    Google Scholar 

  30. Peng MW, Chen HT, Zhong CM (2014) Decision fusion techniques of tool wear state based on SVM. Modular Machine Tool & Automatic Manufacturing Technique 04:89–93

    Google Scholar 

  31. Li SC, Li W, Wu MM (2015) Study on the application of feature fusion and GA-SVM in cutting tool wear monitoring. Manufacturing Technology & Machine Tool 04:145–148

    Google Scholar 

  32. Li WL, Fu P, Cao WQ (2015) Study on the technology of tool wear monitoring by modifying least square support vector machine via Kalman filter. Mechanical Science and Technology for Aerospace Engineering 34(1):81–85

    Google Scholar 

  33. Schӧlkopf B, Smola AJ, Williamson RC, Bartlett PL (2000) New support vector algorithms. Neural Comput 12(5):1207–1245

    Article  Google Scholar 

  34. Specht DF (1991) A general regression neural network. IEEE Transactions on Neural Networks 2(6):568–576

    Article  Google Scholar 

  35. 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

    Article  Google Scholar 

  36. Chen RY (2002) Metal cutting principles. China Machine Press, Beijing

    Google Scholar 

  37. Xiong LS, Yan XG, Zhang FR (2006) Fundamentals of mechanical manufacturing technology. Huazhong University of Science and Technology Press, Wuhan

    Google Scholar 

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Authors and Affiliations

  1. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, China

    Ning Li, Yongjie Chen, Dongdong Kong & Shenglin Tan

Authors
  1. Ning Li
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  2. Yongjie Chen
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  3. Dongdong Kong
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  4. Shenglin Tan
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Corresponding author

Correspondence to Dongdong Kong.

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Li, N., Chen, Y., Kong, D. et al. Force-based tool condition monitoring for turning process using v-support vector regression. Int J Adv Manuf Technol 91, 351–361 (2017). https://doi.org/10.1007/s00170-016-9735-5

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  • DOI: https://doi.org/10.1007/s00170-016-9735-5

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