Skip to main content
SpringerLink
Log in

Modeling and drilling parameters optimization on burr height using harmony search algorithm in low-frequency vibration-assisted drilling

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Increasing demands call for the burr-free workpiece in precision manufacturing. Low-frequency vibration-assisted drilling (LFVAD) has been applied to improve the fabrication process. Prediction and minimization of burr size are one of the major research topics in precision machining. In this paper, an LFVAD parameters optimization model was proposed including harmony search (HS) algorithm and a modified LFVAD burr height analytical model. The burr height model of LFVAD was developed using existing analytical burr height model for conventional drilling (CD) and vibration-assisted drilling (VAD). The developed burr height models were then employed with HS algorithm, which is a new meta-heuristic optimization method based on the imitation of music improvisation process, to determine the optimal machining parameters for a given twist drill that results in minimum exit burr height. Experimental results show that the burr height of the optimized LFVAD decreased by 52.75% compared with the CD, and decreased by 17.59% compared with the un-optimized LFVAD. The simulation and experimental results demonstrate that under suitable LFVAD parameters, the burr height could be reduced.

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. Garg A, Tai K, Vijayaraghavan V, Singru PM (2014) Mathematical modelling of burr height of the drilling process using a statistical-based multi-gene genetic programming approach. Int J Adv Manuf Technol 73(1–4):113–126

    Article  Google Scholar 

  2. Azarrang S, Baseri H (2015) Selection of dry drilling parameters for minimal burr size and desired drilling quality. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 231(3):480–489

    Article  Google Scholar 

  3. Gaitonde VN, Karnik SR, Achyutha BT, Siddeswarappa B (2008) Genetic algorithm-based burr size minimization in drilling of AISI 316L stainless steel. J Mater Process Technol 197(1–3):225–236

    Article  Google Scholar 

  4. Rajmohan T, Palanikumar K (2012) Optimization of machining parameters for surface roughness and Burr height in drilling hybrid composites. Mater Manuf Process 27(3):320–328

    Article  Google Scholar 

  5. Gillespie LK (1979) Deburring precision miniature parts. Precis Eng 1(4):189–198

    Article  Google Scholar 

  6. Chang SSF, Bone GM (2005) Burr size reduction in drilling by ultrasonic assistance. Robot Comput Integr Manuf 21(4–5):442–450

    Article  Google Scholar 

  7. Debnath K, Singh I (2017) Low-frequency modulation-assisted drilling of carbon-epoxy composite laminates. J Manuf Process 25:262–273

    Article  Google Scholar 

  8. Wang C, Yaoxiong H, Congxin L (2005) A manufacturing model of helical groove on rotary burr and a universal post processing method. Int J Adv Manuf Technol 29(1–2):9–16

    Google Scholar 

  9. Niknam SA, Songmene V (2014) Analytical modelling of slot milling exit burr size. Int J Adv Manuf Technol 73(1–4):421–432

    Article  Google Scholar 

  10. Zhang T, Liu Z, Xu C (2013) Influence of size effect on burr formation in micro cutting. Int J Adv Manuf Technol 68(9–12):1911–1917

    Article  Google Scholar 

  11. Min S, Dornfeld DA, Nakao Y (2003) Influence of exit surface angle on drilling Burr formation. J Manuf Sci Eng 125(4):637

    Article  Google Scholar 

  12. Kundu S, Das S, Saha PP (2014) Optimization of drilling parameters to minimize burr by providing back-up support on aluminium alloy. Procedia Engineering 97:230–240

    Article  Google Scholar 

  13. Mondal N, Sardar BS, Halder RN, Das S (2014) Observation of drilling burr and finding out the condition for minimum burr formation. Int J Manuf Eng 2014(1):1–12

    Google Scholar 

  14. Takeyama H, Kato S (1991) Burrless drilling by means of ultrasonic vibration. CIRP Ann Manuf Technol 40(1):83–86

    Article  Google Scholar 

  15. Zhang L-B, Wang L-J, Liu X-Y, Zhao H-W, Wang X, Luo H-Y (2001) Mechanical model for predicting thrust and torque in vibration drilling fibre-reinforced composite materials. Int J Mach Tools Manuf 41(5):641–657

    Article  Google Scholar 

  16. Patra K, Jha AK, Szalay T, Ranjan J, Monostori L (2017) Artificial neural network based tool condition monitoring in micro mechanical peck drilling using thrust force signals. Precis Eng 48:279–291

    Article  Google Scholar 

  17. Chatterjee S, Mahapatra SS, Abhishek K (2016) Simulation and optimization of machining parameters in drilling of titanium alloys. Simul Model Pract Theory 62:31–48

    Article  Google Scholar 

  18. Rajmohan T, Palanikumar K, Prakash S (2013) Grey-fuzzy algorithm to optimise machining parameters in drilling of hybrid metal matrix composites. Compos Part B 50:297–308

    Article  Google Scholar 

  19. Nam JS, Kim DH, Chung H, Lee SW (2015) Optimization of environmentally benign micro-drilling process with nanofluid minimum quantity lubrication using response surface methodology and genetic algorithm. J Clean Prod 102:428–436

    Article  Google Scholar 

  20. Zong WG, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation Transactions of the Society for Modeling & Simulation International 76(2):60–68

    Article  Google Scholar 

  21. Shabani M, Abolghasem Mirroshandel S, Asheri H (2017) Selective refining harmony search: a new optimization algorithm. Expert Syst Appl 81:423–443

    Article  Google Scholar 

  22. Cheng M-Y, Prayogo D, Wu Y-W, Lukito MM (2016) A hybrid harmony search algorithm for discrete sizing optimization of truss structure. Autom Constr 69:21–33

    Article  Google Scholar 

  23. He Z, Pan B, Liu Z, Tang X (2017) The mechanical arm control based on harmony search genetic algorithm. Clust Comput 20(4):3251–3261

    Article  Google Scholar 

  24. Abhishek K, Datta S, Mahapatra SS (2016) Multi-objective optimization in drilling of CFRP (polyester) composites: application of a fuzzy embedded harmony search (HS) algorithm. Measurement 77:222–239

    Article  Google Scholar 

  25. Pan Q-K, Suganthan PN, Tasgetiren MF, Liang JJ (2010) A self-adaptive global best harmony search algorithm for continuous optimization problems. Appl Math Comput 216(3):830–848

    MathSciNet  MATH  Google Scholar 

  26. Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188(2):1567–1579

    MathSciNet  MATH  Google Scholar 

  27. Keshtegar B, Hao P, Wang Y, Hu Q (2018) An adaptive response surface method and Gaussian global-best harmony search algorithm for optimization of aircraft stiffened panels. Appl Soft Comput 66:196–207

    Article  Google Scholar 

  28. Park IW, Dornfeld DA (2000) A study of Burr formation processes using the finite element method: part I. J Eng Mater Technol 122(2):305–311

    Google Scholar 

  29. Kim J, Dornfeld DA (2002) Development of an analytical model for drilling burr formation in ductile materials. J Eng Mater Technol 124(2):192

    Article  Google Scholar 

  30. Brehl DE, Dow TA (2008) Review of vibration-assisted machining. Precis Eng 32(3):153–172

    Article  Google Scholar 

  31. Chang SSF, Bone GM (2010) Burr height model for vibration assisted drilling of aluminum 6061-T6. Precis Eng 34(3):369–375

    Article  Google Scholar 

  32. Chandrasekharan V, Kapoor S, DeVor R (1995) A mechanistic approach to predicting the cutting forces in drilling: with application to fiber-reinforced composite materials. J Eng Ind 117(4):559–570

    Article  Google Scholar 

  33. Paul A, Kapoor SG, DeVor RE (2005) A chisel edge model for arbitrary drill point geometry. J Manuf Sci Eng 127(1):23

    Article  Google Scholar 

  34. Guibert N, Paris H, Rech J (2008) A numerical simulator to predict the dynamical behavior of the self-vibratory drilling head. Int J Mach Tools Manuf 48(6):644–655

    Article  Google Scholar 

  35. Audy J (2008) A study of computer-assisted analysis of effects of drill geometry and surface coating on forces and power in drilling. J Mater Process Technol 204(1–3):130–138

    Article  Google Scholar 

  36. Williams R (1974) A study of the drilling process. J Eng Ind 96(4):1207–1215

    Article  Google Scholar 

  37. Ucun İ (2016) 3D finite element modelling of drilling process of Al7075-T6 alloy and experimental validation. J Mech Sci Technol 30(4):1843–1850

    Article  Google Scholar 

  38. Gaitonde VN, Karnik SR (2010) Minimizing burr size in drilling using artificial neural network (ANN)-particle swarm optimization (PSO) approach. J Intell Manuf 23(5):1783–1793

    Article  Google Scholar 

  39. Abdelhafeez AM, Soo SL, Aspinwall DK, Dowson A, Arnold D (2015) Burr formation and hole quality when drilling titanium and aluminium alloys. Procedia CIRP 37:230–235

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China

    Li Shaomin, Zhang Deyuan, Geng Daxi, Shao Zhenyu & Tang Hui

Authors
  1. Li Shaomin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhang Deyuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Geng Daxi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shao Zhenyu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tang Hui
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhang Deyuan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shaomin, L., Deyuan, Z., Daxi, G. et al. Modeling and drilling parameters optimization on burr height using harmony search algorithm in low-frequency vibration-assisted drilling. Int J Adv Manuf Technol 101, 2313–2325 (2019). https://doi.org/10.1007/s00170-018-2997-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-018-2997-3

Keywords

Search

Navigation