Pulsed thermography is commonly used as a non-destructive technique for evaluating defects within materials and components. In the last few years, many algorithms have been developed with the aim to detect defects and different methods have been used for detecting their size and depth. However, only few works in the literature reported a comparison among the different algorithms in terms of the number of detected defects, the time spent in testing and analysis, and the quantitative evaluation of size and depth. In this work, starting from a pulsed thermographic test carried out on an aluminum specimen with twenty flat bottom holes of known nominal size and depth, different algorithms have been used with the aim to obtain a comparison among them in terms of signal to background contrast (SBC) and number of detected defects by analyzing different time intervals. Moreover, the correlation between SBC and the aspect ratio of the defects has been investigated. The algorithms used have been: Pulsed Phase Thermography (PPT), Slope, Correlation Coefficient (R²), Thermal Signal Reconstruction (TSR) and Principal Component Thermography (PCT). The results showed the advantages, disadvantages, and sensitivity of the various thermographic algorithms.

The main goal of non-destructive testing is the detection of defects early enough to avoid catastrophic failure with particular interest for the inspection of aerospace structures; under this aspect, all methods for fast and reliable inspection deserve special attention. In this sense, active thermography for non-destructive testing enables contactless, fast, remote, and not expensive control of materials and structures. Furthermore, different works have confirmed the potentials of lock-in thermography as a flexible technique for its peculiarity to be performed by means of a low-cost set-up. In this work, a new approach called the multi-frequency via software approach (MFS), based on the superimposition via software of two square waves with two different main excitation frequencies, has been used to inspect a sample in carbon fiber reinforced polymers (CFRP) material with imposed defects of different materials, sizes and depths, by means of lock-in thermography. The advantages and disadvantages of the multi-frequency approach have been highlighted by comparing quantitatively the MFS with the traditional excitation methods (sine and square waves).

Additive manufacturing (AM) technologies, generally called 3D printing, are widely used because their use provides a high added value in manufacturing complex-shaped components and objects. Defects may occur within the components at different time of manufacturing, and in this regard, non-destructive techniques (NDT) represent a key tool for the quality control of AM components in many industrial fields, such as aerospace, oil and gas, and power industries. In this work, the capability of active thermography and eddy current techniques to detect real imposed defects that are representative of the laser powder bed fusion process has been investigated. A 3D complex shape of defects was revealed by a µCT investigation used as reference results for the other NDT methods. The study was focused on two different types of defects: porosities generated in keyhole mode as well as in lack of fusion mode. Different thermographic and eddy current measurements were carried out on AM samples, providing the capability to detect volumetric irregularly shaped defects using non-destructive methods.

This article proposes an automatic approach for segmenting inclusion defects in composite materials inspected by lock-in thermography (LT) in a heat source invariant way. In the proposed pipeline, temperature maps are preliminarily processed to enhance the contrast between sound and defective areas, reduce the acquisition noise, and mitigate the effect of nonuniform heat excitation. Then, the enhanced sequences are grouped in highly local sets, the input of a convolution residual neural network, which segments the defects focusing on the temporal evolution of the temperature. Experiments have been run on a dataset acquired from a CFRP specimen tested under five different heat source configurations. Here, a brief study of signal variations associated with the heat source configuration is presented. Then, an ablation study has been presented to compare different architectures of increasing complexity. The best results are achieved by using the proposed preliminary processing, the residual layers, and an input set of temperature signals of size $3 \times 3$ . This network can reach a global intersection over union (IOU) of 0.78, while defects are segmented with an IOU value of 0.58. In addition, the balanced segmentation accuracy of the test reaches 94.78%, with a maximum recall of the defective class of 91.41%. Further cross validation changing the source configurations of the training and test sets produces comparable results of IOU and balanced accuracy, which are on average equal to 0.71 and 84.02%, respectively. A final evaluation of the trained network is performed on a new dataset augmented through a vertical stretch. Results are still comparable with an average IOU and balanced accuracy of 0.74 and 87.15%, respectively. These results prove the capability of the method to segment inclusion defects of any shape without being affected by the configuration of the heating sources used in the LT inspections.

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.

Active thermography is a fast, contactless and non-destructive technique that can be used to detect internal defects in different types of material. Volumetric irregularities such as the presence of pores in materials produced by the Additive Manufacturing processes can strongly affect the thermophysical and the mechanical properties of the final component. In this work, an experimental investigation aimed at detecting different pores in a sample made of stainless AISI 316L produced by Laser Powder Bed Fusion (L-PBF) was carried out using pulsed thermography in reflection mode. The capability of the technique and the adopted setups in terms of geometrical and thermal resolution, acquisition frequency and energy density of the heating source were assessed to discern two contiguous pores as well as to detect a single pore. Moreover, a quantitative indication about the minimum resolvable pore size among the available and analysed defects was provided. A powerful tool to assess the limits and the opportunities of the pulsed technique in terms of detectability and localizability was provided by comparing active thermography results to Computed Tomography as well as a related Finite Element Analysis (FEA) to simulate the pulsed heating transfer with Comsol.