Journal of Mechanical Science and Technology

, Volume 33, Issue 11, pp 5589–5602 | Cite as

Design study of impact performance of a DTH hammer using PQRSM and numerical simulation

  • Dae-Ji Kim
  • Joo-Young Oh
  • Jung-Woo Cho
  • Jaewon Kim
  • Jintai ChungEmail author
  • Changheon SongEmail author


This paper presents a simulation model to predict the percussive drilling performance of a down-the-hole (DTH) hammer. First, the pneumatic dynamic model of the DTH hammer is developed considering mass flow rate relations representing orifice opening areas of the air tube, the piston, and the bit flushing channels. Next, the performance of the DTH hammer is numerically simulated and evaluated by considering fluctuations of the upper and lower chamber pressure, impact frequency, and force. The simulation model is validated through a series of laboratory tests. Finally, design factors influencing the hammers impact performance are selected via screening design, and the progressive quadratic response surface method then optimizes the set of design factors to propose a design modification to increasing the impact performance of the DTH hammer.


Down-the-hole drilling Down-the-hole hammer Percussive rock drilling Impact performance; Screening design method Progressive quadratic response surface method (PQRSM) 



Projected area (i = 1, 2, 3,…, n) [m2]

Au, Al

Piston area of upper and lower side [m2]


Inlet area [m2]


Opening area (into the lower chamber) [m2]


Opening area (discharged from the upper chamber to the outside) [m2]


Opening area (discharged from the lower chamber to the outside) [m2]

Cd, Cmi

Mass flow coefficient


Coefficient of approximate function


Young’s modulus of piston [N/m2]


Rebound force [N]


Poppet spring constant [N/m]

mc, mp i

Poppet and piston mass [kg]


Mass flow rate (i = 1, 2, 3,…, n) [kg/s]


Number of design variable


Atmosphere pressure [Pa]

Pu, Pl

Pressure of upper and lower chamber [Pa]


Supply pressure [Pa]

Pup, Pd

Upstream and downstream pressures [Pa]


Supplied mass flow rate [kg/s]


Ideal gas constant [m2/s2K]


Initial trust region


New trust region


Operating temperature [K]


Control volume (i = 1, 2, 3,…, n) [m3]

Vu, Vl

Upper and lower chambers of DTH hammer [m3]

Vuo, Vlo

Initial control volume of front and rear chambers [m3]


Rebound velocity of piston [m/s]


Design variable vector


Displacement of poppet [m]


elocity of poppet [m/s]


Acceleration of poppet [m/s2]


Displacement of piston [m]


Velocity of piston [m/s]


Acceleration of piston [m/s2]


Objective function


Approximation of Yi


Poppet head cone angle [rad]


Hammer inclination angle [rad]


Specific heat ratio


Density of piston [kg/m3]


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



This work was supported by Korea Institute of Industrial Technology (KITECH), South Korea.


  1. [1]
    C. Song, K. B. Kwon, J. Y. Oh, S. Lee, D. Y. Shin and J. W. Cho, Optimum design of the internal flushing channel of a drill bit using RSM and CFD simulation, Int. J. Precis Eng. Manuf., 15 (6) (2014) 1041–1050.CrossRefGoogle Scholar
  2. [2]
    X. Li, G. Rupert, D. A. Summers, P. Santi and D. Liu, Analysis of impact hammer rebound to estimate rock drillability, Rock Mech Rock Eng., 33 (1) (2000) 1–13.CrossRefGoogle Scholar
  3. [3]
    X. Li, G. Rupert, D. A. Summers, P. Santi and D. Liu, Energy transmission of down-hole hammer tool and its conditionality, Trans Nonferrous Met. Soc. China, 10 (1) (2000) 109–113.Google Scholar
  4. [4]
    B. Lundberg and M. Okrouhlik, Influence of 3D effects on the efficiency of percussive rock drilling, Int. J. Impact Engng., 25 (4) (2001) 345–360.CrossRefGoogle Scholar
  5. [5]
    L. E. Chiang and D. A. Elias, A 3D FEM methodology for simulating the impact in rock-drilling hammers, Int. J. Rock Mech. Min Sci., 45 (5) (2008) 701–711.CrossRefGoogle Scholar
  6. [6]
    Y. Kang, I. B. Chung and D. H. Choi, Simulation-based turbofan shape optimization for reducing power consumption and noise of a bladeless circular ceiling air conditioner, Int. J. Prec. Eng. Manuf., 18 (8) (2017) 11545–1163.Google Scholar
  7. [7]
    J. Fang, G. Sun, N. Qiu, N. H. Kim and Q. Li, On design optimization for structural crashworthiness and its state of the art, Struct. Multidisc Optimi., 55 (3) (2017) 1091–1119.MathSciNetCrossRefGoogle Scholar
  8. [8]
    Y. Wang, B. Xu, G. Sun and S. Yang, A two-phase differential evolution for uniform designs in constrained experimental domains, IEEE Transactions on Evolutionary Computation, 21 (5) (2017) 665–680.CrossRefGoogle Scholar
  9. [9]
    X. Song, G. Sun, G. Li, W. Gao and Q. Li, Crashworthiness optimization of foam-filled tapered thin-walled structure using multiple surrogate models, Struct. Multidisc Optimi., 47 (2) (2013) 221–231.MathSciNetCrossRefGoogle Scholar
  10. [10]
    J. Fang, Y. Gao, G. Sun, C. Xu and Q. Li, Multiobjective robust design optimization of fatigue life for a truck cab, Reliab. Eng. Syst. Safe, 135 (2015) 1–8.CrossRefGoogle Scholar
  11. [11]
    D. Y. Shin and C. Song, Performance optimization of Down the-hole hammer using Taguchi method, Transactions of the Korean Society of Mechanical Engineers A, 36 (1) (2012) 109–116.CrossRefGoogle Scholar
  12. [12]
    C. Song, J. Chung, J. H. Kim and J. Y. Oh, Design optimization of a drifter using the Taguchi method for efficient percussion drilling, Journal of Mechanical Science and Technology, 31 (4) (2017) 1797–1803.CrossRefGoogle Scholar
  13. [13]
    J. Y. Oh, G. H. Lee, H. S. Kang and C. S. Song, Modeling and performance analysis of rock drill drifters for rock stiffness, Int. J. Prec. Eng. Manuf., 13 (12) (2012) 2187–2193.CrossRefGoogle Scholar
  14. [14]
    P. Subra and P. Jestin, Screening design of experiment (DOE) applied to supercritical antisolvent process, Industrial and Engineering Chemistry Research, 39 (11) (2000) 4178–4184.CrossRefGoogle Scholar
  15. [15]
    J. S. Kwak and M. K. Ha, Optimization of grinding conditions and prediction of surface roughness using Taguchi experimental design, Journal of the Korean Society for Precision Engineering, 21 (7) (2004) 37–45.Google Scholar
  16. [16]
    H. I. Choi, Y. Lee, D. H. Choi and J. S. Maeng, Design optimization of a viscous micropump with two rotating cylinders for maximizing efficiency, Struct. Multidisc Optimi., 40 (2010) 537–548.CrossRefGoogle Scholar
  17. [17]
    K. J. Hong, M. S. Kim and D. H. Choi, Efficient approximation method for constructing quadratic response surface model, Journal of Mechanical Science and Technology, 15 (7) (2001) 876–888.Google Scholar
  18. [18]
    C. Park, S. Kim, D. Choi and B. Pyo, Design optimization for minimizing warpage in injection molding parts with numerical noise, Transactions of the Korean Society of Mechanical Engineers A, 29 (11) (2005) 1445–1454.CrossRefGoogle Scholar
  19. [19]
    D. Kim, D. Park, J. Lee, S. Shin, J. H. Choi, B. L. Choi and D. H. Choi, Optimal vehicle rear suspension through integration of analysis and design process, Transactions of the Korean Society of Mechanical Engineers, 22 (4) (2014) 72–81.Google Scholar
  20. [20]
    K. Park and D. H. Choi, Optimal design of a heat sink using the sequential approximate optimization algorism, The Society of Air-conditioning and Refrigerating Engineers, 16 (12) (2004) 1156–1166.Google Scholar
  21. [21]
    K. Park and S. Moon, Optimal design of heat exchangers using the progressive quadratic response surface model, Int. J. Heat Mass Transfer, 48 (2005) 2126–2139.CrossRefGoogle Scholar
  22. [22]
    PIDOTECH Inc., PIAnO User’s Manual (2015).Google Scholar
  23. [23]
    D. Kim, D. Park, J. Lee, J. H. Choi, B. L. Choi and D. H. Choi, Optimal vehicle rear suspension through integration of analysis and design process, Transactions of the Korean Society of Automotive Engineers, 22 (4) (2014) 72–81.CrossRefGoogle Scholar

Copyright information

© KSME & Springer 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringHanyang UniversityGyeonggi-doKorea
  2. 2.Construction Equipment R&D GroupKorea Institute of Industrial TechnologyDaeguKorea

Personalised recommendations