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This paper presents a non-destructive laser thermography (LT) procedure for estimating Usibor® 1500 ultimate tensile strength (UTS) based on thermal diffusivity measurements. The key innovation lies in the revealed inverse relationship between thermal diffusivity (α) and UTS, highlighting its potential for estimating mechanical properties in a non-destructive way. The experimental phase involved analyzing fifteen specimens using a 960 nm CW laser source and a thermal camera to measure thermal diffusivity. The results demonstrate a clear correlation between α and UTS, providing valuable material characterization insights and demonstrating promising applications in mechanical design. [ABSTRACT FROM AUTHOR]

This paper presents a non-destructive laser thermography (LT) procedure for estimating Usibor® 1500 ultimate tensile strength (UTS) based on thermal diffusivity measurements. The key innovation lies in the revealed inverse relationship between thermal diffusivity (α) and UTS, highlighting its potential for estimating mechanical properties in a non-destructive way. The experimental phase involved analyzing fifteen specimens using a 960 nm CW laser source and a thermal camera to measure thermal diffusivity. The results demonstrate a clear correlation between α and UTS, providing valuable material characterization insights and demonstrating promising applications in mechanical design.

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]

Block-sparse regularization is already well known in active thermal imaging and is used for multiple-measurement-based inverse problems. The main bottleneck of this method is the choice of regularization parameters which differs for each experiment. We show the benefits of using a learned block iterative shrinkage thresholding algorithm (LBISTA) that is able to learn the choice of regularization parameters, without the need to manually select them. In addition, LBISTA enables the determination of a suitable weight matrix to solve the underlying inverse problem. Therefore, in this paper we present LBISTA and compare it with state-of-the-art block iterative shrinkage thresholding using synthetically generated and experimental test data from active thermography for defect reconstruction. Our results show that the use of the learned block-sparse optimization approach provides smaller normalized mean square errors for a small fixed number of iterations. Thus, this allows us to improve the convergence speed and only needs a few iterations to generate accurate defect reconstruction in photothermal super-resolution imaging. [ABSTRACT FROM AUTHOR]

• Detection of microcracks on uncoated aluminum alloy surface by joint laser scanning thermography. • Dynamic minimum value feature sampling method within a large region of interest, peak-valley bipolar feature establishment for microcracks. • Overcoming the noise interference of uncoated aluminum alloy surface. • Detection and localization of crack waveform features based on wavelet transform analysis. Laser thermography is a cutting-edge non-destructive testing method with remarkable advantages such as contactless operation, swift detection, and exceptional sensitivity. In this study, an innovative thermal signal sampling method is introduced for the detection of microcracks on uncoated aluminum alloy surface through joint scanning laser thermography. Initially, numerical simulation was conducted to analyze the thermal response characteristics of the uncoated surface during inspection. Subsequently, the dynamic minimum value features of the surface temperature distribution were extracted within a large region of interest, and the optimal sampling region was determined by evaluating the signal-to-noise ratio. The temperature curves of the sampling results showed a clear peak-valley bipolar feature at the microcrack locations. The correlation coefficient of one-dimensional continuous wavelet transform was employed to extract the singularity of crack features, and the singularity points were clearly defined to locate the microcracks spatial location. The experimental results showed that our proposed detection method could quickly and automatically locate and characterize microcracks on the surface of uncoated aluminum alloy with good stability and robustness. This method provides a feasible solution for the application of laser thermography in industry, thus improving the quality and safety performance of products. [ABSTRACT FROM AUTHOR]

The study investigated the anti-correlation between thermal diffusivity and Ultimate Tensile Strength (UTS) in Usibor® 1500 steel. The non-destructive pulsed laser spot thermography technique was used to analyze fifteen boron steel specimens with varying bainite/martensite phase percentages, while the UTS was measured through uniaxial tensile tests. A 23 % thermal diffusivity difference was found between fully martensitic and fully bainitic structures, with UTS varying by around 90 %. The strong anti-correlation was confirmed (Spearman coefficient −0.98) and an empirical power-law equation was derived to estimate UTS based on thermal diffusivity variations. The approach showed an R-squared value over 0.84, providing a non-destructive thermographic procedure for UTS estimation in Usibor® 1500 steel, offering valuable material property insights. • New non-destructive method for estimating the ultimate tensile strength in steel. • An anti-correlation between thermal diffusivity and ultimate tensile strength. • Measurement of thermal diffusivity to estimate mechanical resistance in Usibor 1500. • Evaluation of the Al–Si coating on thermal diffusivity measurements in steel. • Empirical relations between thermal diffusivity and UTS in Usibor® 1500. [ABSTRACT FROM AUTHOR]

Block-sparse regularization is already well known in active thermal imaging and is used for multiple-measurement-based inverse problems. The main bottleneck of this method is the choice of regularization parameters which differs for each experiment. We show the benefits of using a learned block iterative shrinkage thresholding algorithm (LBISTA) that is able to learn the choice of regularization parameters, without the need to manually select them. In addition, LBISTA enables the determination of a suitable weight matrix to solve the underlying inverse problem. Therefore, in this paper we present LBISTA and compare it with state-of-the-art block iterative shrinkage thresholding using synthetically generated and experimental test data from active thermography for defect reconstruction. Our results show that the use of the learned block-sparse optimization approach provides smaller normalized mean square errors for a small fixed number of iterations. Thus, this allows us to improve the convergence speed and only needs a few iterations to generate accurate defect reconstruction in photothermal super-resolution imaging.

• Laser bidirectional scanning thermography was introduced to detect the surface cracks of the abradable seal coating. • Distinguish between crack defects and non-defects by the attribute of directional difference of thermal signals. • The thermal image sequence normalization algorithm was introduced. • The effect of the difference in optical properties between coating surface phases is suppressed. The paper presents a detection method of laser bidirectional scanning thermography. This method can detect surface crack defects in the abradable seal coating with non-homogeneous and multi-phase structures by normalizing thermal image sequences with opposite scanning directions. Firstly, we analyzed the thermal distribution of the abradable seal coating under laser scanning excitation in two opposite directions. Subsequently, we analyzed the thermal distribution characteristics of normalized thermal images obtained by normalized subtraction processing of the images based on overlapping laser heating areas. We thus proposed a feature extraction algorithm of crack defects by thermal image sequence normalization to detect surface crack defects of abradable sealant coatings. The experimental results demonstrate that the processing method of thermal image sequence normalization has a significant suppression effect on the pseudo-defect noise signal caused by the difference in optical properties of the object surface. The thermal contrast characteristic signal at the crack will reach the extreme value when the crack defect lies near the centerline of the laser heating area of the normalized image. The results show that the proposed detection method can improve the signal-to-noise ratio for detecting crack defects on the surface of objects with significant differences in optical properties. [ABSTRACT FROM AUTHOR]

Steel members are susceptible to cracking, and a number of non-destructive testing (NDT) techniques are used for crack detection. However, these NDT techniques are not only labor intensive but also time consuming. In particular, inspection of welded areas requires surface treatment, and data must be interpreted by experienced engineers to differentiate cracks from weld patterns. In this study, an automated weld crack detection and quantification system was developed by integrating laser thermography, Mask R-CNN, and CycleGAN. The developed system comprises a laser heat source, an IR camera, and a control unit. The laser applied heating to the target surface, and the resulting thermal radiation emitted from the surface was measured using an IR camera. Subsequently, the thermal images were processed for crack detection using Mask R-CNN, and for crack quantification using medial axis transform. The detection and quantification performance of the developed system were validated through laboratory and field tests. • Non-contact and non-destructive inspection using laser thermography. • Automatic weld crack detection based on deep learning. • Minimization of false-alarms by differentiating real fatigue cracks from weld patterns. • Data augmentation to address lack of thermal image data from real fatigue cracks. [ABSTRACT FROM AUTHOR]

Precise and efficient inspection of disband defect in sub-mm layers of composite structures is a big challenge for the non-destructive and testing industry. In this work, 16 artificial disbands (flat bottom holes), with different sizes and depths, in carbon fiber reinforced polymer (CFRP) composites were contactless detected using combined laser thermography and laser ultrasonics with optical microphone. The presented laser ultrasonic technique employed a membrane free optical microphone is a contactless and nondestructive technique and shows high detectability for flat bottom holes with deep depth in this preliminary investigation. Sizes and depths of the flat-bottom holes were determined by the presented laser ultrasonics technique. This investigation combined the high detection efficiency of laser thermography and high detection sensitivity of laser ultrasonics with the optical microphone together for the contactless inspection of artificial disbonds. [ABSTRACT FROM AUTHOR]

• The temperature profiles of five specimens with large differences in thermal conductivity excited by long-pulsed were intrinsically self-similar. An indicator to describe the "temperature retention" performance was constructed. • Curve clustering analysis was applied to the temperature profiles under laser spot long-pulsed excitation, demonstrating that the characteristic temperature, Euclidean norm, and maximum temperature change rate parameters can characterize the thermal conductivity of the material. • The apparent thermal conductivity of a material can be characterized by the "characteristic temperature." • The defined "temperature maintenance" indicator can partially explained the photothermal phenomenon of cracks in laser scanning thermography. • Crack defects on 3Cr13 coatings on 45 steel can be detected using laser long-pulsed thermography. Laser thermography is a novel non-destructive testing method enabling the detections of defects on vertical surfaces (e.g., cracks) and those on parallel surfaces (e.g., delaminations). The thermal response of defects near the surface of the specimen in the temperature field is the basis for defect detection work; however, the thermal response characteristics of defects under direct laser irradiation have not been systematically investigated. In this paper, we investigated the thermal response characteristics of five materials under long-pulsed laser excitations: copper, 45 steel, aluminum alloy, carbon fiber reinforced polymer plate, and ceramic resin composite coating. These specimens exhibited significantly different thermal conductivities. An important characterization parameter, characteristic temperature, was found, which can characterize the difference in thermal conductivity of different specimens. The surface of the 45 steel was coated with 3Cr13 coating, and cracks on the coating surfaces generated significantly different thermal response characteristics. The findings of this study illustrated the feasibility of long-pulsed laser thermography for demonstrating the heat entrapment effect due to cracks and provide a method for defect detection. [ABSTRACT FROM AUTHOR]

In this paper, an improved laser thermography method and system with laser beam homogenizing is developed to remote inspect delamination defect in carbon fiber reinforced polymer (CFRP) laminates. A fiber laser beam shaper is designed to transform the high-power laser beam with uneven intensity into large area square beam with uniform distribution. A numerical model of the optical system is developed to calculate and optimize the light intensity distribution of the output laser from the beam shaper. To investigate the performance of the laser thermography system with beam shaping, the temperature field in CFRP with a delamination defect, excited by different laser illumination, is simulated. Finally, an experimental comparison between conventional laser thermography without beam shaping and the proposed one is reported with two CFRP samples with different artificial defects. Both the simulation and experiment results show that the defect detection capability in the CFRP is significantly improved by the proposed laser thermography method with laser beam shaping. [ABSTRACT FROM AUTHOR]