Numerical simulation of subsurface defect identification by pulsed thermography and improvement of this technique for noisy data

Authors

  • Oleg Plekhov Institute of Continuous Media Mechanics of the Ural Branch of Russian Academy of Sciences, Perm, Russia
  • Anastasiya Kostina Research and Education Center "Materials", Don State Technical University, Rostov-on-Don, Russia https://orcid.org/0000-0002-5721-3301
  • Sergey Aizikovich Research and Education Center "Materials", Don State Technical University, Rostov-on-Don, Russia

DOI:

https://doi.org/10.3221/IGF-ESIS.50.57

Keywords:

Signal processing, Finite-element analysis, Pulsed thermography, Non-destructive technique

Abstract

Pulsed thermography is an active non-destructive technique which uses optical excitation source to stimulate heating of the object under investigation. This work is devoted to the simulation of the pulsed thermography method in a steel plate with the ceramic coating containing artificial defects of various depths and sizes. The simulation has been carried out on the base of the model which takes into account complex heat exchange of the sample with the surrounding by convection, conduction and radiation. Comparison of the temperature contrast with the experimental data has shown that the results are in a good qualitative and quantitative agreement in all stages of the cooling process. Due to the fact that the temperature contrast is often susceptible to the surface noise of various nature the Kalman-based signal processing technique was developed. The comparative analysis has shown that the proposed filtration technique provides better value of signal-to-noise ratio in comparison to the considered well-known techniques of signal reconstruction when proper calibration of the filtration parameters is carried out

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Published

20-09-2019

How to Cite

Numerical simulation of subsurface defect identification by pulsed thermography and improvement of this technique for noisy data. (2019). Frattura Ed Integrità Strutturale, 13(50), 667-683. https://doi.org/10.3221/IGF-ESIS.50.57

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