Skip to main content
SpringerLink
Log in

Investigating the fluctuations in tool vibration during GFRP drilling through recurrence quantification analysis

  • Published:
Journal of Mechanical Science and Technology Aims and scope Submit manuscript

Abstract

Glass fiber-reinforced polymers (GFRPs) are a unique class of materials in machining because unlike metals, they are not homogeneous. Given the inhomogeneous and anisotropic nature of composite materials, their machining behavior differs in many respects from that of metal machining. With metallic materials, the cutting force is uniform during drilling. Given GFRPs, drill bits are subject to variable cutting forces, whose responses to machining vary significantly. During GFRP drilling, heavy vibration is undesirable and results in rapid tool wear. The cutting condition can be controlled effectively to avoid heavy vibration by applying the appropriate cutting parameters. Hence, the most suitable cutting condition must be selected for proper process control. This study investigates the influence of vibration as a result of changes in cutting condition, drill diameter, and the number of holes. This influence is continuously monitored by the vibration signals generated during GFRP drilling with a TiN/TiALN-coated carbide drill bit. Experiments were conducted on a computer numerical control milling machine. The input parameters used for various cutting conditions were spindle speed and feed. The vibration signal was measured with an accelerometer, and its fluctuations were examined by a non-linear technique called recurrence quantification analysis (RQA). The influence of these fluctuations on vibration and their effect on tool life were also determined using RQA parameters. These parameters include percent recurrence, percent determinism, and percent laminarity. They have been used to study the effect of continuous fluctuations in vibration signals during drilling. This study confirmed that the RQA technique has potential for use in the investigation of a dynamical system.

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.

Institutional subscriptions

Similar content being viewed by others

References

  1. B. Isik and E. Ekici, Experimental investigations of damage analysis in drilling of woven glass fiber-reinforced plastic composites, Int. J. Adv. Manuf. Technol. (2009).

    Google Scholar 

  2. S. Jain and D. C. H. Yang, Delamination free drilling of composite laminates, ASME, 116 (475–81) (1995) 398–405.

    Google Scholar 

  3. G. Lubin, Hand Book of composites, Van No strand Reinhold, USA (1982).

    Book  Google Scholar 

  4. A. Velayudham, R. Krsishnamurthy and T. Soundarapandian, Evaluation of drilling characteristics of high volume fraction fibre glass reinforced polymeric composite, Int’l J. of Machine Tools and Manufacture, 45 (2005) 399–406.

    Article  Google Scholar 

  5. S. Karuda and C. Bradley, A review of machine vision sensors for tool condition monitoring, Computers in Industry, 34 (1997) 55–72.

    Article  Google Scholar 

  6. K. Uehara, New attempts for short time tool life testing, Ann. CIRP, 22 (1973) 23–24.

    Google Scholar 

  7. M. R. Sewailem, Experimental analysis of the correlation between cutting force variation and nose wear in cutting operation, Wear, 64 (1980) 281–289.

    Article  Google Scholar 

  8. T. Moriwaki, Detection for cutting tool fracture by acoustic emission measurement, Ann. CIRP, 29 (1980) 35–39.

    Article  Google Scholar 

  9. S. B. Rao, Tool wear monitoring through the dynamics of stable turning, ASME Trans., J Engrg. Ind., 112 (3) (1986) 183–190.

    Article  Google Scholar 

  10. G. H. Lim, Tool wear monitoring in machine turning, Journal of Materials Processing Technology, 51 (1995) 25–36.

    Article  Google Scholar 

  11. M. Y. Lee, C. E. Thomas and G. Wildes, Review-prospects for in-process diagnosis of metal cutting by monitoring vibration signals, J. Mat. Sci., 22 (1987) 3821–3890.

    Article  Google Scholar 

  12. J. Gradisek, E. Govekar and I. Grabec, Qualitative and quantitative analysis of stochastic processes based on measured data, II: applications to experimental data, J. Sound Vibrat, 252 (3) (2002) 563–572.

    Article  Google Scholar 

  13. R. G. Landers and A. G. Ulsoy, Supervisory machining control: design and experiments, Ann. CIRP, 47 (1) (1998) 301–306.

    Article  Google Scholar 

  14. T. Choi and Y. S. Shin, Online chatter detection using wavelet based parameter estimation, Trans ASME, J. Manuf. Sci. Eng., 125 (1) (2003) 21–28.

    Article  MathSciNet  Google Scholar 

  15. E. Soliman and F. Ismail, Chatter detection by monitoring spindle drive current, Int. J. Adv. Manuf. Technol., 13 (1) (1997) 27–34.

    Article  Google Scholar 

  16. T. L. Schmitz, K. Medicus and B. Dutterer, Exploring once per revolution audio signal variance as a chatter indicator, Min. Sci. Technol., 6 (2) (2002) 215–233.

    Google Scholar 

  17. B. Karpuschewski, M. Wehmeier and I. Inasaki, Grinding monitoring system based on power and acoustic emission sensors, Ann. CIRP, 49 (1) (2000) 235–240.

    Article  Google Scholar 

  18. J. P. Eckmann, S. O. Kamphorst and D. Ruelle, Recurrence plots of dynamical systems, Europhys Lett., 4 (1987) 973–977.

    Article  Google Scholar 

  19. L. L. Trulla, A. Giuliant, A. Giuliant, J. P. Zbilut and C. L. Webber, Recurrence quantification analysis of the logistic equation with transients, Phys. Lett. A, 223 (1996) 255–260.

    Article  MATH  MathSciNet  Google Scholar 

  20. J. B. Gao and H. Q. Cai, On the structures and quantification of recurrence plots, Phys. Lett. A, 270 (2000) 75–87.

    Article  Google Scholar 

  21. H. Kanz, Quantifying the closeness of fractal measures, Phys. Rev. E. Stat. Phys. Plasmas Fluids Relat Interdiscip Topics, 49 (1994) 5091–5097.

    Google Scholar 

  22. T. Schreiber, Detecting and analyzing non stationarity in a time series using nonlinear cross predictions, Phys. Rev. Lett., 78 (1997) 843–846.

    Article  Google Scholar 

  23. S. D. Mhalsekar, S. S. Rao and K. V. Gangadharan, Investigation on feasibility of recurrence quantification analysis for detecting flank wear in face milling, International Journal of Engineering, Science and Technology, 2 (5) (2010) 23–38.

    Article  Google Scholar 

  24. K. S. Umashankar, K. V. Gangadharan, V. Desai and B. Shivamurthy, Machining characteristics of nanocomposites, Adv. Mat. Lett., 2 (3) (2011) 222–226.

    Article  Google Scholar 

  25. A. K. Sen, R. Longwic, G. Litak and K. Górski, Analysis of cycle-to-cycle pressure oscillations in a diesel engine, Mech. Systems and Signal Processing, 22 (2008) 362–373.

    Article  Google Scholar 

  26. U. A. Khashaba, Delamination in drilling GFR-thermoset composites, Journal of Composite Structures, 63 (2004) 313–327.

    Article  Google Scholar 

  27. C. K. Saghizadeh and A. Dharan, Delamination fracture toughness of graphite and aramid epoxy composites, ASME Journal of Engineering Material Technology, 108 (1986) 290–295.

    Article  Google Scholar 

  28. K. Jessy, S. S. Kumar, D. Dinakaran and R. Seshagiri, Influence of different coolant methods on drill temperature in drilling GFRP composites, International Journal of Advanced Manufacturing Technology, Aug 14th (2014) (online).

    Google Scholar 

  29. D. C. H. Jain and A. Yang, Effects of feed rate and chisel edge on delamination in composite drilling, ASME Journal, 115 (1993) 398–405.

    Article  Google Scholar 

  30. Eugene Kononov’s VRA software, http://Visual-recurrence-analysis. Software informer.com.

  31. N. Marwan, Encounters with neighbours: current development of concepts based on recurrence plots and their applications, Ph.D. Thesis, Universityät Potsdam, Potsdam (2003).

    Google Scholar 

  32. B. Babaei, R. Zarghami, H. Sedighikamal, R. Sotudeh-Gharebagh and N. Mostoufi, Selection of minimal length of line in recurrence quantification analysis, Physica A: Statistical Mechanics and its Applications, 395 (2014) 112–120.

    Article  Google Scholar 

  33. M. I. Coco and R. Dale, Package ‘crqa’ cross-recurrence quantification analysis for categorical and continuous time series CRQA IN R (2013).

    Google Scholar 

  34. L. Mesin, A. Monaco and R. Cattaneo, Investigation of Nonlinear Pupil Dynamics by Recurrence Quantification Analysis, BioMed Research International, Sep 26 (2013) (online).

    Google Scholar 

  35. C. L. Webber and Jr. J. P. Zbilut, Recurrence quantification analysis of nonlinear dynamical systems (2006).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, KCG College of Technology, Chennai, India

    K. Jessy

  2. Centre for Automation and Robotics, Hindustan University, Chennai, India

    D. Dinakaran

  3. Department of Production Engineering, Velammal Engineering College, Chennai, India

    S. Satish Kumar

Authors
  1. K. Jessy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D. Dinakaran
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. Satish Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. Jessy.

Additional information

Recommended by Associate Editor Jihong Hwang

K. Jessy graduated with a Bachelor’s degree in Production Engineering in 2004 from Adhiyamann College of Engineering Hosur, which is affiliated with Periyar University, India. She received her Masters Degree in CAD/CAM from RajaLakshmi Engineering College, Chennai, India in 2007. In 2014, she completed her Ph.D in Mechanical Engineering at Hindustan University. She is currently working as an Assistant Professor in the Department of Mechanical Engineering at the K.C.G. College of Technology, Chennai. Her area of interest includes machining, composite materials, and process parameter optimization.

S. Satishkumar graduated with a Bachelor’s degree in Mechanical Engineering in 1996 from the RVS College of Engineering and Technology, Dindigul, which is affiliated with Madurai Kamaraj University, India. He received his Masters Degree in Manufacturing Technology from the Regional Engineering College (presently known as the National Institute of Technology) Tiruchirappalli, India in 1997. In 2007, he completed his Ph.D. at the National Institute of Technology. He is currently working as a Professor in the Department of Production Engineering at Velammal Engineering College. His area of interest includes nano finishing, the application of soft computing techniques in manufacturing processes, manufacturing process selection, and process parameter optimization.

D. Dinakaran graduated with a Bachelor’s degree in Mechanical Engineering in 2001 from the Pallavan College of Engineering, Chennai, which is affiliated with Madras University, India. He received his Masters Degree in Manufacturing Engineering from Anna University, Chennai, India in 2004. In 2010, he completed his Ph.D in Mechatronics at Anna University. He is currently working as a Professor and a Group Leader at the Centre for Automation and Robotics at Hindustan University, India. His area of interest includes condition monitoring, machining, and robotics.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jessy, K., Dinakaran, D. & Kumar, S.S. Investigating the fluctuations in tool vibration during GFRP drilling through recurrence quantification analysis. J Mech Sci Technol 29, 1265–1272 (2015). https://doi.org/10.1007/s12206-015-0241-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12206-015-0241-8

Keywords

Search

Navigation