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In this paper, a new absolute thermal contrast method is proposed for pulsed infrared thermography. It is based on the computations of reconstructed defect-free images so that no a priori knowledge of a sound area on the sample is necessary. Moreover, a correction is applied to take into account possible delays in the acquisition time. Results are presented both on Plexiglas TM and graphite-epoxy specimens. Comparisons with Pulsed Phase Thermography phase images are also presented along with a discussion on the advantages of the proposed method.

This paper presents a summary of recent research activities carried out at our laboratory in the field of Infrared Thermography for Nondestructive Evaluation (TNDE). First, we explore the latest developments in signal improvement. We describe three approaches: multiple pulse stimulation [1]; the use of Synthetic Data for de-noising of the signal [2]; and a new approach derived from the Fourier diffusion equation called the Differentiated Absolute Contrast method (DAC) [3]. Secondly, we examine the advances carried out in inverse solutions. We describe the use of the Wavelet Transform [4] to manage pulsed thermographic data, and we present a summary on Neural Networks for TNDE [5]. Finally, we look at the problem of complex geometry inspection. In this case, due to surface shape, heat variations might be incorrectly identified as flaws. We describe the Shape-from-Heating approach [6] and we propose some potential research avenues to deal with this problem.

An automation of the Differentiated Absolute Contrast (DAC) method, called Interpolated Differentiated Absolute Contrast Algorithm (IDAC), is proposed for pulsed infrared thermography. Based on the previously known method, the new algorithm simplifies the analysis process of thermographic sequences resolving the decisions that the user should normally take when applying the DAC method. The algorithm has been successfully checked experimentally with results obtained using Plexiglas TM , graphite-epoxy and aluminium specimens.

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