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Infrared thermography testing (IRT) has been widely used in the defect detection of composite materials. However, the identification of defects characteristics is unsatisfying due to the interference of factors such as uneven background and noise in the original thermal image sequence. A novel thermography-based defect detection method with the semantic segmentation network is proposed to enhance the defect contrast and extract perfect features. To Figure out the abnormal distribution of temperature field in thermal images, AG-UNet was used with a spatial self-attention gate module to extract spatial features of thermal images. The 3D-UNet network was obtained by the 3D convolution module and the temporal convolution module to extract thermal temporal and spatial features simultaneously which could help the defect segmentation in thermal videos. Compared with traditional algorithms such as principal component analysis (PCA), thermographic signal reconstruction (TSR), and fast Fourier transform (FFT), defects detection results were significantly enhanced with the proposed method, and defects of smaller diameter-to-depth ratio can be detected by deep learning models. [ABSTRACT FROM AUTHOR]

Active three-dimensional(3-D) thermography combines 3-D shape and active thermography. Thus, it enables the provision of the intuitive thermographic inspection results for composite parts with a complex geometry. However, conventional 3-D thermography acquires thermographic information and 3-D shape through at least two independent sensors and requires complex cross-modal image registration algorithm based on the keypoint detection and matching. In this article, an active 3-D thermography system is proposed using only one thermal camera for moving objects. This system does not require an independent 3-D sensor while the thermal camera acts as the 3-D sensor. Nature behind that is that a mathematical model is proposed to unify the line scanning thermography and laser triangulation in the dynamic scanning process, taking a line laser as both heat excitation for active thermography and spatial coding for 3-D reconstruction. Furthermore, the model enables the feature-free registration of thermogram sequence and 3-D shape, so that the registration is fast and robust without keypoint features. The experiments on standard height specimens, 3-D printing glass fiber composites, and carbon fiber intake tube have shown the error is calibrated within 0.25 mm in the range of 1 to 150 mm and evidenced the capability for subsurface defects detection. The ease and robustness of the proposed active 3-D thermography have a bright future for 3-D measurement, defects detection, and quality control in the production line. [ABSTRACT FROM AUTHOR]

Joint scanning laser thermography (JLST) is well-known for its efficiency to overcome the field of view (FOV) limitation of thermal imagers. However, JSLT requires a data reconstruction to reveal the location of the defective area straightforwardly. Moreover, its detection capacity is limited by the lack of a deconvolution algorithm adaptive to the reconstructed data. In this study, a deconvolutional reconstruction method based on the Lucy–Richardson (LR) algorithm has been developed for JST, which is effective in suppressing random noise and the blur effect caused by the thermal diffusion. A JSLT inspection is carried out on a functional coating material with cylinder-like defects to test the performance of the proposed method. In comparison to the directly processed method on the original data, the proposed method is processed on the reconstructed data and then compared with principal component analysis (PCA), restored pseudo heat flux (RPHF), fast Fourier transform (FFT) methods and non-negative matrix factorization (NMF). The experimental results indicated that our proposed LR method exhibited a higher signal-to-noise ratio. Besides, it can detect the cylinder-mocked debonding defects with a diameter of 1.5 mm and a depth of 2.0 mm buried under the 1.0-mm coating. In addition, the defect detection diameter-to-depth ratio reached 1.5, while the defect detection rate of the test specimens can approach 90%. [ABSTRACT FROM AUTHOR]

In this paper, a new inspection method, joint scanning laser thermography (JSLT) as well as its data reconstruction and processing algorithm, is proposed. The new inspection method is utilized to detect and characterize the flat-bottom holes (FBH) in carbon fiber composites by using joint laser scanning scheme. By analyzing the nature of the thermal image sequences sampled under such a scanning scheme, a quick and simple reconstruction method is developed to characterize the buried depth of defects based on 1D heat conduction model. The processed thermal images are expected to get higher temporal resolution and spatial resolution. It can inspect larger area within shorter acquisition time than the pulse thermography. Thus, the study solves the dilemma between the inspection speed and the inspection capacity. Later, a joint laser scanning thermography test is set up to test the algorithm on a carbon fiber composite panel with defects buried at different depth. The experimental results show that the reconstructed data almost behave as those under pulse excitation. The tendency of temperature to change in the logarithmic domain over time is similar to the curve in the TSR method. But, unlike the pulse thermography data, the defect detection rate of PCA based on reconstructed data is higher than that of fast Fourier transform (FFT) amplitude image, independent component analysis (ICA) and FFT phase image. The JSLT system is used to detect FBHs and the diameter-depth ratio reached 3.33. [ABSTRACT FROM AUTHOR]

A method without requirements on knowledge about thermal properties of coatings or those of substrates will be interested in the industrial application. Supervised machine learning regressions may provide possible solution to the problem. This paper compares the performances of two regression models (artificial neural networks (ANN) and support vector machines for regression (SVM)) with respect to coating thickness estimations made based on surface temperature increments collected via time resolved thermography. We describe SVM roles in coating thickness prediction. Non-dimensional analyses are conducted to illustrate the effects of coating thicknesses and various factors on surface temperature increments. It’s theoretically possible to correlate coating thickness with surface increment. Based on the analyses, the laser power is selected in such a way: during the heating, the temperature increment is high enough to determine the coating thickness variance but low enough to avoid surface melting. Sixty-one pain-coated samples with coating thicknesses varying from 63.5 μm to 571 μm are used to train models. Hyper-parameters of the models are optimized by 10-folder cross validation. Another 28 sets of data are then collected to test the performance of the three methods. The study shows that SVM can provide reliable predictions of unknown data, due to its deterministic characteristics, and it works well when used for a small input data group. The SVM model generates more accurate coating thickness estimates than the ANN model. [ABSTRACT FROM AUTHOR]

The study here demonstrates the capability of thermography to detect and characterize pinhole defects in a visually transparent anti-reflection (AR) film. The diameter of the pin-holes varies from 0.03 mm to 4 mm. Each inspected area was unevenly heated for 65 s. Thermal images were processed by the following steps: de-trend processing, primary edge detection based on Sobel approximation, and further edge separation based on k-means clustering. Then, the diameter of pinholes was directly measured from binary images. The proposed image processing enables thermography to detect 86 of 90 inspected areas correctly in position. [ABSTRACT FROM AUTHOR]

• CFRP plates subjected to impact damage are inspected by THz imaging and VT. • Peak-time shift and independent component analysis are used in data processing. • The detection results of THz imaging and VT are quantitatively compared. In this paper, terahertz (THz) imaging and vibro-thermography (VT) are implemented for non-destructive evaluation (NDE) of CFRP subjected to impact damage. THz inspection is well-known for its outstanding detection depth in dielectric materials but unreliable in conductive materials like carbon fiber reinforced plastics (CFRP): an unwanted electro-magnetic wave will be excited under the irradiation of THz waves in conductive molecules and interrupted the reflective waves leading to a blur in the detection results. However, our experimental results showed THz imaging is comparable when it's used to detect surface defects and shallow ones caused by impact loads. We conducted a set of VT tests as a contrast test, and the VT data are processed by Fourier transform and eigenvector decomposition algorithms, i.e. independent component analysis (ICA). Compared with VT, THz imaging show more details on surface defects and shallow-buried ones due to the absence of thermal diffusion effects. THz imaging could perform non-invasive, non-ionizing and non-contact inspection on the impact damage based on the differences in reflective index. In the end, signal-to-noise (SNR) evaluation is used as an effective means to quantitatively compare the results of these two techniques. [ABSTRACT FROM AUTHOR]

Infrared imaging-based machine vision (IRMV) is the technology used to automatically inspect, detect, and analyse infrared images (or videos) obtained by recording the intensity of infrared light emitted or reflected by observed objects. Depending on whether controllable excitation is used during the imaging of infrared rays, thermal IRMV can be categorised into passive thermography and active thermography. Passive thermography is an important supplement to conventional machine vision based on visible light and is a valid imaging tool for self-heating objects such as the human body and electrical power devices. Active thermography is a non-destructive testing method for the quality evaluation and safety assurance of non-self-heating objects. In active thermography, the trend is to inspect rapidly, reliably, and intelligently by introducing multiple-mode excitation sources and artificial intelligence. The rapid development of deep learning makes IRMV more and more intelligent and highly automated, thus considerably increasing its range of applications. This paper reviews the principle, cameras, and thermal data of IRMV and discusses the applications of deep learning applied to IRMV. Case studies of IRMV and deep learning on various platforms such as unmanned vehicles, mobile phones and embedded systems are also reported. [ABSTRACT FROM AUTHOR]