Vol. 20 No. 1 (2023): Journal of Non Destructive Testing and Evaluation (JNDE), March 2023
Research Papers

Infrared Machine Vision for detection and characterization of defects present in a mild steel material

PDF
  • Pranav Yogesh Mahajan,
  • Aneesh Bajwa,
  • Keshav,
  • Nandini Bansal,
  • Geetika Dua,
  • Amanpreet Kaur,
  • Vanita Arora
Geetika Dua
Thapar Institute of Engineering and Technology
Vanita Arora
Indian Institute of Information Technology Una, Vill. Saloh, Teh. Haroli, Distt. Una Himachal Pradesh, India-177209

Published 12-03-2023

Keywords

  • Machine Vision,
  • Non-destructive testing,
  • Scale-invariant feature transform,
  • Watershed transform

How to Cite

Mahajan, P. Y., Bajwa, A. ., Keshav, Bansal, N. ., Dua, G., Kaur, A., & Arora, V. (2023). Infrared Machine Vision for detection and characterization of defects present in a mild steel material . Journal of Non-Destructive Testing and Evaluation (JNDE), 20(1), 38–43. Retrieved from https://jnde.isnt.in/index.php/JNDE/article/view/10

Copyright (c) 2023 Journal of Non-Destructive Testing and Evaluation (JNDE)

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

Significant advancements in machine vision and thermal imaging have provided an advantage in non-destructive testing and evaluating different materials. This work presents an approach to combining thermal wave imaging with machine vision for a fast, accurate way to detect and characterize the sub-surface defects and their features present in a solid material. Machine vision-based defect detection approaches attract the research community due to their reliable performance in employing the stimulated thermal response in active thermal wave imaging. A thermal source stimulates the material surface while the infrared camera captures its thermal response, extracts features from the thermal pattern, and feeds them into a machine vision-based algorithm for characterization. This proposed method improves the detectability reliability regarding qualified characteristics of defects.

PDF

References

  1. X. P. V. Maldague (2001), “Theory and Practice of Infrared Thermography for Non- Destructive Testing,” John Wiley & Sons Inc.
  2. D. P. Almond and S. K. Lau (1994), “Defect sizing by transient thermography I: An analytical treatment,” Journal of Physics D: Applied Physics, 27 (5), pp. 1063-1069.
  3. D. L. Balageas, J.C. Krapez, P. Cielo (1986), “Pulsed photothermal modeling of layered materials,” Journal of Applied Physics, 59 (2), pp. 348-357.
  4. X. Maldague and S. Marinetti (2003), “Pulse Phase Infrared Thermography,” Review of Scientific Instruments, 74 (1 II), pp. 417-419.
  5. X. Maldague and S. Marinetti (1996), “Pulse phase infrared thermography,” Journal of applied physics, vol. 79, no. 5, pp. 2694-2698.
  6. G. Busse (1979), “Optoacoustic phase angle measurement for probing a metal,” Applied Physics Letters, 35 (10), pp. 759-760.
  7. G. Busse, D. Wu, W. Karpen, “Thermal wave imaging with phase-sensitive modulated thermography,” Journal of Applied Physics, 71 (8), pp. 3962-3965, 1992.
  8. R. Mulaveesala and S. Tuli (2006), “Theory of frequency modulated thermal wave imaging for nondestructive subsurface defect detection,” Applied Physics Letters, 89 (19), art.no. 91913.
  9. S. Tuli and R. Mulaveesala, "Defect detection by pulse compression in frequency modulated thermal wave imaging," Quantitative InfraRed Thermography Journal, vol. 2(1), pp. 41-54, 2005.
  10. Ghali V S and Mulaveesala R 2010 Frequency modulated thermal wave imaging techniques for non-destructive testing, Insight: Non-Destructive Testing and Condition Monitoring52, 475-480
  11. Arora V, Mulaveesala R and Bison P 2016 Effect of Spectral Reshaping on Frequency Modulated Thermal Wave Imaging for Non-destructive Testing and Evaluation of Steel Material, Journal of Nondestructive Evaluation35, 1-7.
  12. Kaur K and Mulaveesala R (2019), “An efficient data processing approach for frequency modulated thermal wave imaging for inspection of steel material”, Infrared Physics and Technology, 103, 103083.
  13. Dua G, Arora V and Mulaveesala R (2021), “Defect Detection Capabilities of Pulse Compression Based Infrared Non-Destructive Testing and Evaluation”, IEEE Sensors Journal21, 7940-7947.
  14. Arora V, Mulaveesala R, Rani A, Kumar S, Kher V, Mishra P, Kaur J, Dua G and Jha R (2021),“Infrared Image Correlation for Non-destructive Testing and Evaluation of Materials”, Journal of Nondestructive Evaluation, 40, 1-7.
  15. Mulaveesala R, Arora V and Dua G (2021),“Pulse Compression Favorable Thermal Wave Imaging Techniques for Non-Destructive Testing and Evaluation of Materials”, IEEE Sensors Journal, 21, 12789-12797.
  16. Kaur K and Mulaveesala R (2019), “Experimental investigation on noise rejection capabilities of pulse compression favourable frequency-modulated thermal wave imaging”, Electronics Letters, 55, 352-353.
  17. Duan Y, Liu S, Hu C, Hu J, Zhang H, Yan Y, Tao N, Zhang C, Maldague X and Fang Q (2019), “Automated defect classification in infrared thermography based on a neural network”, NDT & E International 107, 102147.
  18. Hossein-Nejad Z, Agahi H, ad Mahmoodzadeh A (2020), “Detailed review of the scale invariant feature transform (sift) algorithm; concepts, indices and applications”, Journal of Machine Vision and Image Processing, 7(1), 165-190.
  19. Dudek G, Dudzik. S 2018 Classification Tree for Material Defect Detection Using Active Thermography, Advances in Intelligent Systems and Computing, 655, 118-127
  20. Fang Q, Nguyen B D, Castanedo C I, Duan Y and Maldague II X (2020), “Automatic defect detection in infrared thermography by deep learning algorithm”, Thermosense: thermal infrared applications XLII SPIE) pp 180-195.
  21. Mulaveesala R and Dua G (2016), “Non-invasive and non-ionizing depth resolved infra-red imaging for detection and evaluation of breast cancer: A numerical study”, Biomedical Physics and Engineering Express, 2, 055004.
  22. Arora V, Mulaveesala R, Dua G and Sharma A (2020), “Thermal non-destructive testing and evaluation for subsurface slag detection: Numerical modeling”, Insight: Non-Destructive Testing and Condition Monitoring, 62, 264-268.
  23. Cardone, Daniela, EdoardoSpadolini, David Perpetuini, Chiara Filippini, Antonio Maria Chiarelli, and Arcangelo Merla (2021), “Automated warping procedure for facial thermal imaging based on features identification in the visible domain”, Infrared Physics & Technology, 112, 103595.
  24. Gamarra, Margarita, Eduardo Zurek, Hugo Jair Escalante, Leidy Hurtado, and Homero San-Juan-Vergara (2019), “Split and merge watershed: A two-step method for cell segmentation in fluorescence microscopy images, Biomedical signal processing and control, 53, 101575.
  25. Mery D, da Silva R R, Calôba L P and Rebello J M (2003),“Pattern recognition in the automatic inspection of aluminium castings”, Insight-Non-Destructive Testing and Condition Monitoring, 45, 475-483.
  26. Li, Henan, Shigang Wang, Yan Zhao, Jian Wei, and Meilan Piao (2021), “Large-scale elemental image array generation in integral imaging based on scale invariant feature transform and discrete viewpoint acquisition”, Displays, 69, 102025.
  27. Jubair, Aliaa S., Aliaa Jaber Mahna, and H. I. Wahhab (2019), “Scale invariant feature transform based method for objects matching”, International Russian Automation Conference (RusAutoCon), pp. 1-5.