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Abstract

In the paper a method using active thermography and a neural algorithm for material defect characterization is presented. Experimental investigations are conducted with the stepped heating method, so-called time-resolved infrared radiometry, for the test specimen made of a material with low thermal diffusivity. The results of the experimental investigations were used in simulations of artificial neural networks. Simulations are performed for three datasets representing three stages of the heating process occurring in the investigated sample. In this work, the simulation research aimed to determine the accuracy of defect depth estimation with the use of the mentioned algorithm is descibed
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Abstract

In the paper a method for correction of heating non-homogeneity applied in defect detection with the use of active thermography is presented. In the method an approximation of thermal background with second- and third-order surfaces was used, what made it possible to remove partially the background. In the paper the simulation results obtained with the abovementioned method are presented. An analysis of the influence of correction of heating non-homogeneity on the effectiveness of defect detection is also carried out. The simulations are carried out for thermograms obtained on the basis of experiments on a test sample with simulated defects, made of a material of low thermal diffusivity.
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