Abstract

Subsurface defects can be detected by the pulsed thermography (PT) technique analysing the raw thermal data with the application of different post-processing algorithms. In this regard, different methods, based on one-dimensional models, are used in the literature to estimate the depth and size of defects. Two of the most established methods are the thermal signal reconstruction (TSR) and the pulsed phase thermography (PPT) algorithms. These latter require a careful set up of the testing parameters such as the frame rate, the truncation window size, and the energy density to obtain an accurate estimation of both depths and sizes of defects. Even if some works have already investigated the issue of defect characterization, there are few works in which the correct procedures to obtain both the size and depth were deeply explained, above all for real components with real defects. The aim of this work is to propose a new empirical procedure to obtain depth and size estimation of the defects using the pulsed thermography technique and in particular the principal component thermography (PCT) algorithm. The proposed procedure is based on the experimental observation that exists a linear correlation between the defect contrasts and the relative aspect ratios. In this way, by means of a master specimen, a calibration curve can be obtained considering a suitable truncation window of the analysed data. Then, the size and depth of defects have been retrieved imposing threshold criteria. The procedure is quite general, and it can be also tested with other algorithms. Different experimental tests have been carried out on two materials, aluminium and glass fiber reinforced polymer (GFRP) and then the procedure has been applied and validated both on simulated (flat bottom holes) and real defects.

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