A TSR-Based Quantitative Processing Procedure to Synthesize Thermal D-Scans of Real-Life Damage in Composite Structures

Abstract

A two-step processing procedure of pulse thermography experimental data is proposed to synthesize thermal D-scans of composite materials and structures. The first step consists in the identification of the damaged areas by applying the thermographic signal reconstruction (TSR) technique and the red-green-blue (RGB) projection of a combination of polynomial coefficients. The second step takes full advantage of the correlation between the colors of the resulting TSR-RGB map and the defect depths: a limited number of thermograms (one for each color shade) is analyzed to estimate the emergence times of the contrasts locally created by the defects, which enables to assess their depths and provide a depth-scale to the thermal image, at low cost. Once validated for the quantitative imaging of calibrated defects of known depths, the procedure is applied to actual real-life damage, whether initially introduced by manufacturing processes or induced by external solicitations such as impact, fire exposition or quasi-static mechanical loading. Simple planar plates and more complex structures (stiffened panel, honeycomb sandwich and riveted assembly) are considered.

IR thermography for lightning-strike damage monitoring in composite materials

“…The helical-like damage patterns induced by lightning-strike ( Fig. 2) are quite similar to those generated by lowvelocity mechanical impacts on unidirectional carbon/epoxy composite materials [5].…”

Analysing the probability of detection of shallow spherical defects by means of Active Thermography

“…The coefficient γ describes the contrast of the thermal effusivities at the interface of the sound material to defect, which is very close to 1 only in the theoretical case of perfect reflection and a large discontinuity [7], [26]- [27]. During the last years in which the pulsed thermography technique has been used for several applications, several post-processing algorithms were considered and adopted to improve the quality of the raw thermal data and then the signal-to-noise ratio such as Pulsed Phase Thermography (PPT) [1], [6]- [7], [15], Thermographic Signal Reconstruction (TSR) [16]- [17], Principal Component Thermography (PCT) [18], Slope and R 2 [24]. The theoretical background of each of these algorithms is well known, as reported in the mentioned works.…”

“…Several thermographic tests have been carried out using the pulsed thermography technique in reflection configuration, with two flash lamps and a cooled sensor. Different post-processing algorithms were considered to improve the quality of the raw thermal data in terms of signal-to-noise ratio (SNR), and, in particular, Pulsed Phase Thermography (PPT) [1], [6]- [7], [15], Thermographic Signal Reconstruction (TSR) [16]- [17], Principal Component Thermography (PCT) [18], Slope and R 2 [24]. In addition, the PoD curves were compared to analyse the performance of the technique and establish, in a quantitative way, the limits of the used setup, parameters, and post-processing algorithms to detect spherical defects.…”

Taking into account heat losses in front-face pulse IR thermography experiment for thermal diffusivity identification

“…The method has been validated experimentally by measuring the diffusivity of a 5.24 mm-thick plate made of carbon-epoxy used in previous NDE studies [13][14][15]. Both rear-and front-face measurements are made with the same IR camera (FLIR X6540sc), working in short waves [1.5 -5] µm.…”