2022
DOI: 10.46300/9106.2022.16.115
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Post-processing Improvement of Lock-in Thermography Study of MCM-L for Better Hidden Defect Localization

Abstract: This paper examines the impact of post-processing of lock-in thermographic measurement data on the ability to detect and characterize in terms of geometric dimensions and location of specific types of defects in MCM-L. A thermal 3D model of a test specimen with hidden artificial defects is used and simulated lock-in thermography measurement is performed. Qualitative and quantitative assessment was performed of the correct detection and geometric dimensions characterization of the defects. The Shape Difference … Show more

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Cited by 2 publications
(2 citation statements)
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“…Different fiber orientations, layouts, and resign percentages also change its mechanical properties [ 40 , 41 ]. The simulations of the CFRP sample were the same as used previously, with thermal conductivities for the X and Y directions of 6.3 and 0.6, respectively [ 14 ]. It should be considered that the following results and analyses might be different for different properties such as different resin percentages or fiber alignment [ 42 ].…”
Section: Analyses Of the Ltt Simulationsmentioning
confidence: 99%
See 1 more Smart Citation
“…Different fiber orientations, layouts, and resign percentages also change its mechanical properties [ 40 , 41 ]. The simulations of the CFRP sample were the same as used previously, with thermal conductivities for the X and Y directions of 6.3 and 0.6, respectively [ 14 ]. It should be considered that the following results and analyses might be different for different properties such as different resin percentages or fiber alignment [ 42 ].…”
Section: Analyses Of the Ltt Simulationsmentioning
confidence: 99%
“…Stoynova et al performed numerical simulations of a thermal 3D model with artificial defects under a lock-in test. This is one of several works that used LTT to perform subsurface defect detection and characterization [ 14 ]. Other similar works were performed by Shrestha [ 15 ], Chulkov [ 16 ], Pitarresi [ 17 ], An, Y.K, [ 18 ], Zoecke [ 19 ], and Peng [ 20 ].…”
Section: Introductionmentioning
confidence: 99%