Laser spot thermography and Pulse thermography – comparison of performance for non-destructive testing of composite structures
DOI:
https://doi.org/10.21152/1750-9548.17.1.91Abstract
Laser spot thermography (LST) is a sub-domain method in active thermography that uses a laser as the heat source. The method allows for precise control of the beam shape, pulse duration, and delivered energy. Therefore, this technique can be applied, especially for delicate structures where testing conditions must be maintained precisely. The following work includes the introduction of a laser spot thermography technology demonstrator that was designed and constructed at AGH UST. The paper presents laser thermography laboratory experiments along with the developed procedure for automatic defect identification. The measurements were used to establish an automatic procedure and algorithm for automatic defect identification based on regression methods.
References
K-J. Lee, M-S. Jeon, J-R. Lee, ”Evaluation of manufacturing defects in 3D printed carbon fiber reinforced cylindrical composite structure based on laser ultrasonic testing”, NDT & E International, Volume 135, 2023, https://doi.org/10.1016/j.ndteint.2023.102802
Z.Smoqi, L.D. Sotelo, A. Gaikwad, J. A. Turner, P. Rao, “Ultrasonic nondestructive evaluation of additively manufactured wear coatings”, NDT & E International, Volume 133, 2023, https://doi.org/10.1016/j.ndteint.2022.102754
Q. Tang, J. Hu, T. Yu, ”Electromagnetic evaluation of brick specimens using synthetic aperture radar imaging”, NDT & E International, Volume 104, 2019, pp. 98-107, https://doi.org/10.1016/j.ndteint.2019.04.006
R. Prochazka, J. Dzugan, P. Konopik, “Fatigue limit evaluation of structure materials based on thermographic analysis”, Procedia Structural Integrity, Volume 7, 2017, pp. 315-320, https://doi.org/10.1016/j.prostr.2017.11.094
V.A. Golodov, A.A. Maltseva, “Approach to weld segmentation and defect classification in radiographic images of pipe welds”, NDT & E International, Volume 127, 2022, https://doi.org/10.1016/j.ndteint.2021.102597
J. Galos, B. Ghaffari, E.T. Hetrick, M.H. Jones, M.J. Benoit, T. Wood, P.G. Sanders, M.A. Easton, A.P. Mouritz, ”Novel non-destructive technique for detecting the weld fusion zone using a filler wire of high x-ray contrast”, NDT & E International, Volume 124, 2021, https://doi.org/10.1016/j.ndteint.2021.102537
Q. Wu, X. Qin, K. Dong, A. Shi, Z. Hu, “A learning-based crack defect detection and 3D localization framework for automated fluorescent magnetic particle inspection”, Expert Systems with Applications, Volume 214, 2023, https://doi.org/10.1016/j.eswa.2022.118966
H. Nakata, M. Hirata, T. Tada, ”Fully automatic magnetic-particle inspection system for square billets”, IFAC Proceedings Volumes, Volume 26, Issue 2, Part 5, 1993, pp. 1-4, https://doi.org/10.1016/S1474-6670(17)48210-3
S. Kumar, D. Mahto, “Recent Trends in Industrial and Other Engineering Applications of Non Destructive Testing: A Review” International Journal of Scientific & Engineering Research, Volume 4 (Issue 9), 2013,
R. Spring, R.Huff, M. Schwoegler, “Infrared Thermography: A Versatile non-destructive Testing Technique”, Materials Evaluation, Volume 69, Issue 8, pp. 934 – 942, 2011
S. Bagavathiappan, B.B. Lahiri, T. Saravanan, J. Philip, T. Jayakumar, “Infrared thermography for condition monitoring – A review”, Infrared Physics & Technology, Volume 60, 2013, pp. 35-55, ISSN 1350-4495, https://doi.org/10.1016/j.infrared.2013.03.006.
J. Yang, L. Dong, H. Wang, Z. Xing, Y. Di, C. Gao, R. Li, ”The curve cluster analyses for the characterizations of material defects by long-pulsed laser thermography”, Infrared Physics & Technology, Volume 120, 2022, https://doi.org/10.1016/j.infrared.2021.103956
M. Rodríguez-Martín, J.G. Fueyo, J. Pisonero, J. López-Rebollo, D. Gonzalez-Aguilera, R. García-Martín, F. Madruga, “Step heating thermography supported by machine learning and simulation for internal defect size measurement in additive manufacturing”, Measurement, Volume 205, 2022, https://doi.org/10.1016/j.measurement.2022.112140
H. Khawaja, T. Bertelsen, R. Andreassen, M. Moatamedi, “Study of CRFP Shell Structures under Dynamic Loading in Shock Tube Setup.” Journal of Structures 2014, doi: http://dx.doi.org/10.1155/2014/487809
A. Salazar, M. Colom, A. Mendioroz, “Laser-spot step-heating thermography to measure the thermal diffusivity of solids”, International Journal of Thermal Sciences, Volume 170, 2021, https://doi.org/10.1016/j.ijthermalsci.2021.107124.
V. Vavilov, D.D. Burleigh, Review of pulsed thermal NDT: Physical principles, theory and data processing, NDT&E International Volume 73, 2015, pp. 28-52, https://doi.org/10.1016/j.ndteint.2015.03.003
D. Palumbo, P. Cavallo, U. Galietti ”An investigation of the stepped thermography technique for defects evaluation in GFRP materials”, NDT&E International Volume 102, 2019, pp.254-263, https://doi.org/10.1016/j.ndteint.2018.12.011
S. M. Shepard “Thermal Nondestructive Evaluation of Composite Materials and Structures” Comprehensive Composite Materials II, 2018, pp. 250-269, https://doi.org/10.1016/B978-0-12-803581-8.10038-4
D. L. Balageas, J.M. Roche, F.H. Leroy, W.M. Liu, A. M. Gorbach, ”The thermographic signal reconstruction method: A powerful tool for the enhancement of transient thermographic images”, Biocybernetics and Biomedical Engineering,
Volume 35, Issue 1,2015, pp. 1-9, ISSN 0208-5216, https://doi.org/10.1016/j.bbe.2014.07.002.
W. Shi, Z. Ren, W. He, J. Hou, H. Xie, S. Liu, “A technique combining laser spot thermography and neural network for surface crack detection in laser engineered net shaping”, Optics and Lasers in Engineering, Volume 138, 2021,106431, ISSN 0143-8166, https://doi.org/10.1016/j.optlaseng.2020.106431.
G. Ferrarini, P. Bison, A. Bortolin, G. Cadelano, L. Finesso, "Evaluation of clustering algorithms for the analysis of thermal NDT inspections," Proc. SPIE 11409, “Thermosense: Thermal Infrared Applications XLII”, 2020; https://doi.org/10.1117/12.2559591
N. H. M. M. Shrifan, G. N. Jawad, N. A. M. Isa, and M. F. Akbar, "Microwave Nondestructive Testing for Defect Detection in Composites Based on K-Means Clustering Algorithm," in IEEE Access, vol. 9, pp. 4820-4828, 2021, doi: https://10.1109/ACCESS.2020.3048147
X. Cheng, G. Ma, Z. Wu, H. Zu, X. Hu, “Automatic defect depth estimation for ultrasonic testing in carbon fiber reinforced composites using deep learning”, NDT & E International, 2023, https://doi.org/10.1016/j.ndteint.2023.102804
A.K. Jain, M.N. Murty, P.J. Flynn “Data clustering: a review”, ACM Comput. Surv., Volume 31 (Issue 3), 1999, pp. 264-323, https://doi.org/10.1145/331499.331504
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