Journal of Visualization

, Volume 22, Issue 6, pp 1081–1092 | Cite as

A study on guided wave tomographic imaging for defects on a curved structure

  • Junpil Park
  • Younho ChoEmail author
Regular Paper


Guided wave tomography is a very attractive technique for online nondestructive evaluation and structural health monitoring. The reconstruction algorithm for probabilistic inspection of damage (RAPID) is an effective tomography algorithm for detecting, locating, and imaging defects. Conventional tomography imaging techniques are difficult to quantify defects in structures with curved surfaces like aircraft. It is not a complete tool for evaluation of a damaged area on a curved surface, because of insufficient guidelines for shape factor (Bata) and curvature. Probabilistic inspection of damage is used to construct tomographic images of hole defects in a 30 mm diameter and thickness 5 mm carbon steel plate and curved surface specimen. Imaging is completed using an array of 16 transducers. It is shown that defect location can be accurately determined on plate and curved surfaces. The work presented here introduces a calculation for the shape factor for evaluation of the damaged area, as well as a variable \(\beta\) parameter technique to correct a damaged shape. An experiment is performed using the guided wave pitch-catch method to find the length of the damage on a ray path. Also, we perform research in modeling simulation and an experiment for comparison with a suggested inspection method and verify its validity. In the plate and curved surface images resulting from the advanced RAPID algorithm, the defect area and shape showed good agreement. Quantitative imaging techniques on the surface can be applied to real-time defects imaging of aircraft component monitoring.

Graphic abstract


Structural health monitoring (SHM) Guided wave Curved surface structure Plate Defect detection Defect imaging 



This research was supported by a national Research Foundation of Korea (NRF) Grant funded by the Korean Government (MSIP) (No. 2016M2A2A9A03913295).


  1. Albiruni F et al (2012) Non-contact guided waves tomographic imaging of plate-like structures using a probabilistic algorithm. Mater Trans 53(2):330–336CrossRefGoogle Scholar
  2. Belanger P, Cawley P, Simonetti F (2010) Guided wave diffraction tomography within the born approximation. IEEE Trans Ultrason Ferroelectr Freq Control 57(6):1405–1418CrossRefGoogle Scholar
  3. Cawley P, Alleyne D (1996) The use of Lamb waves for the ling range inspection of large structures. Ultrasonics 34(2–5):287–290CrossRefGoogle Scholar
  4. Elsersy M et al (2016) Performance evaluation of experimental damage detection in structure health monitoring using acceleration. In: Wireless communications and mobile computing conference (IWCMC), 2016 international, pp 529–534Google Scholar
  5. Hay TR et al (2006) A comparison of embedded sensor Lamb wave ultrasonic tomography approaches for material loss detection. Smart Mater Struct 15(4):946–951CrossRefGoogle Scholar
  6. Hutchins DA, Jansen DP, Edwards C (1993) Lamb-wave tomography using non-contact transduction. Ultrasonics 31(2):97–103CrossRefGoogle Scholar
  7. Huthwaite P, Simonetti F (2013) High-resolution guided wave tomography. Wave Motion 50(5):979–993MathSciNetCrossRefGoogle Scholar
  8. Jansen DP, Da Hutchins, Yong RP (1993) Ultrasonic tomography using scanned contact transducers. J Acoust Soc Am 93(6):3242–3249CrossRefGoogle Scholar
  9. Lee J, Sheen B, Cho Y (2015) Quantitative tomographic visualization for irregular shape defects by guided wave long range inspection. Int J Precis Eng Manuf 16(9):1949–1954CrossRefGoogle Scholar
  10. Lee J, Sheen B, Cho Y (2016) Multi-defect tomographic imaging with a variable shape factor for the RAPID algorithm. J Vis 19(3):393–402CrossRefGoogle Scholar
  11. Leonard KR, Hinders MK (2005) Lamb wave tomography of pipe-like structures. Ultrasonics 43(7):574–583CrossRefGoogle Scholar
  12. Li W, Cho Y (2016) Combination of nonlinear ultrasonic and guided wave tomography for imaging the micro-defects. Ultrasonics 65:87–95CrossRefGoogle Scholar
  13. Malyarenko EV, Hinders MK (2001) Ultrasonic Lamb wave diffraction tomography. Ultrasonics 39(4):269–281CrossRefGoogle Scholar
  14. Pei J et al (1996) Lamb wave tomography and its application in pipe erosion/corrosion monitoring. Res Nondestr Eval 8(4):189–197CrossRefGoogle Scholar
  15. Rose JL (2002) A baseline and vision of ultrasonic guided wave inspection potential. J Press Vessel Technol 124(3):273–282CrossRefGoogle Scholar
  16. Rose JL (2004) Ultrasonic guided waves in structural health monitoring. Eng Mater 270–293:14–21Google Scholar
  17. Rose JL (2014) Ultrasonic guided waves in solid media. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  18. Sheen B, Cho Y (2012) A study on quantitative lamb wave tomogram via modified RAPID algorithm with shape factor optimization. Int J Precis Eng Manuf 13(5):671–677CrossRefGoogle Scholar
  19. Tiberiu AS et al (2014) Lamb waves tuning on aluminium plates for structure health monitoring. INCAS Bull 6(1):73–81CrossRefGoogle Scholar
  20. Van Velsor J, Gao H, Rose JL (2007) Guided-wave tomographic imaging of defects in pipe using a probabilistic reconstruction algorithm. Insight Nondestruct Test Cond Monit 49(9):532–537CrossRefGoogle Scholar
  21. Wessling V, Reuter M (2014) Structure health monitoring of steel with CI-based methods. World Autom Congr (WAC) 2014:202–206Google Scholar
  22. Willey CL et al (2014) Guided wave tomography of pipes with high-order helical modes. NDT&E Int 65:8–21CrossRefGoogle Scholar
  23. Wright W et al (1997) Air-coupled Lamb wave tomography. IEEE Trans Ultrason Ferroelectr Freq Control 44(1):53–59CrossRefGoogle Scholar

Copyright information

© The Visualization Society of Japan 2019

Authors and Affiliations

  1. 1.Graduate School of Mechanical EngineeringPusan National UniversityBusanSouth Korea
  2. 2.School of Mechanical EngineeringPusan National UniversityBusanSouth Korea

Personalised recommendations