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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]

Spot welding is a crucial process step in various industries. However, classification of spot welding quality is still a tedious process due to the complexity and sensitivity of the test material, which drain conventional approaches to its limits. In this article, we propose an approach for quality inspection of spot weldings using images from laser thermography data. We propose data preparation approaches based on the underlying physics of spot-welded joints, heated with pulsed laser thermography by analyzing the intensity over time and derive dedicated data filters to generate training datasets. Subsequently, we utilize convolutional neural networks to classify weld quality and compare the performance of different models against each other. We achieve competitive results in terms of classifying the different welding quality classes compared to traditional approaches, reaching an accuracy of more than 95 percent. Finally, we explore the effect of different augmentation methods.

The characterization of micro cracks on metal surface plays an important role in the process of manufacturing and using, which has attracted a lot of attention. This is mainly due to the fact that the size of defects is smaller and the depth of defects is difficult to predict compared with metal materials with larger plane, which is still challenging. In order to solve this problem, this paper proposes a characterization method of surface defects based on reflective laser thermography, and designs a laser heating nondestructive testing system based on reflection. The system includes a semiconductor laser to heat the surface of metal cracks, and an infrared imager to record changes in the temperature field of the metal surface. In the process of data analysis, an Otsu adaptive threshold segmentation method is selected to quantify the defect size, which can control the quantification accuracy of defect size within 25%. A derivative analysis method is proposed to quantify the depth of defects, which can control the depth quantification accuracy of tiny defects within 7%.

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.

Additive manufacturing has been recently employed in industrial sectors with the fundamental requirement for zero defect parts. Technological developments in additive manufacturing notwithstanding, there continues to be a scarcity of non-destructive inspection techniques to be exploited during the manufacturing process itself, thus limiting industrial advancements and extensive applications. Therefore, being able to integrate the defect inspection phase within the additive manufacturing process would open the way to enabling corrective actions on the component in itinere, that is, before reaching the final product. For this reason, new methods of in-process monitoring are gaining more and more attention nowadays. In this work, a remote laser thermographic methodology is employed as a mean to detect micrometric defects in additive manufactured samples. Beforehand, a preliminary Finite Element Analysis was carried out in order to optimize the sensitivity of the technique to the micrometric defects. Our results indicate that the technique is proved to be quite successful in detecting flaws, with the added plus of being suitable for integration in the additive manufacturing equipment, thus allowing a continuous in-line inspection.

Additive manufacturing has been recently employed in industrial sectors with the fundamental requirement for zero defect parts. Technological developments in additive manufacturing notwithstanding, there continues to be a scarcity of non-destructive inspection techniques to be exploited during the manufacturing process itself, thus limiting industrial advancements and extensive applications. Therefore, being able to integrate the defect inspection phase within the additive manufacturing process would open the way to enabling corrective actions on the component in itinere, that is, before reaching the final product. For this reason, new methods of in-process monitoring are gaining more and more attention nowadays. In this work, a remote laser thermographic methodology is employed as a mean to detect micrometric defects in additive manufactured samples. Beforehand, a preliminary Finite Element Analysis was carried out in order to optimize the sensitivity of the technique to the micrometric defects. Our results indicate that the technique is proved to be quite successful in detecting flaws, with the added plus of being suitable for integration in the additive manufacturing equipment, thus allowing a continuous in-line inspection. [ABSTRACT FROM AUTHOR]

In this work a novel data collection and processing is proposed for the Infrared Non-Destructive Testing (IR-NDT) of interlaminar disbonds on Fibre Metal Laminate (FML) hybrid composites. The adopted active IR-NDT scheme uses a pointwise laser heat source that is moved along a raster scanning trajectory over the object surface. A Focal Plane Array IR camera is employed to acquire the thermal field generated by the moving heat source. Disbonds defect signatures are then searched by analysing the perturbations of the temperature distribution over a reference area following the heat source. The proposed methodology has been implemented on a GLARE sample, since this class of FMLs has gained extensive use in aerospace structures. In particular, a sample of GLARE 1 3/2–0.3 was manufactured in-house, containing triangular shaped artificial disbonds at different interlayers. The novel inspection approach was able to detect the position, size and to some extent the shape of interlaminar defects by recording the changes in standard deviation of the temperature over the monitored area. The sensitivity found in detecting disbonds proposes the presented methodology as a potential alternative to more conventional inspection routes for FMLs. [ABSTRACT FROM AUTHOR]

• 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]

Infrared thermography uses the temperature-imaging capability of modern thermal cameras to characterize materials and detect flaws. An energy source-whether a pulse of light from a laser or flash lamp, an induction coil, or some other source-induces heat flow in a material, and the resulting temperature patterns are imaged with the thermal camera. In flash thermography, the most widely used form of quantitative thermography, a pulse of light is used as the energy source, and then the surface cooldown is imaged with the thermal camera. Calculations based on an elementary theory of 1D heat conduction can determine thickness (or, equivalently, thermal diffusivity), and nonuniformity in the cooldown will identify defects. This article reviews the methods, approaches, and models of thermography. It focuses on illustrating and identifying the materials, thicknesses, and flaw conditions under which thermography is an effective material characterization technique. [ABSTRACT FROM AUTHOR]

Composites, such as glass fiber reinforced polymer (GFRP) and carbon fiber reinforced polymer (CFRP), and adhesive bonding are being increasingly used in fields of aerospace, renewable energy, civil and architecture, and other industries. Flaws and damages are inevitable during either fabrication or lifetime of composites structures or components. Thus, nondestructive testing (NDT) are extremely required to prevent failures and to increase reliability of composite structures or components in both manufacture and in-service inspection. Infrared thermography techniques including pulsed thermography, pulsed phase thermography, and lock-in thermography have shown the great potential and advantages. Besides conventional optical thermography, other sources such as laser, eddy current, microwave, and ultrasound excited thermography are drawing increasingly attentions for composites. In this work, a fully, in-depth and comprehensive review of thermography NDT techniques for composites inspection was conducted based on an orderly and concise literature survey and detailed analysis. Firstly, basic concepts for thermography NDT were defined and introduced, such as volume heating thermography. Next, the developments of conventional optic, laser, eddy current, microwave, and ultrasound thermography for composite inspection were reviewed. Then, some case studies for scanning thermography were also reviewed. After that, the strengths and limitations of thermography techniques were concluded through comparison studies. At last, some research trends were predicted. This work containing critical overview, detailed comparison and extensive list of references will disseminates knowledge between users, manufacturers, designers and researchers involved in composite structures or components inspection by means of thermography NDT techniques. [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]

Laser thermography (LT) is one of the novel NDT methods for detecting surface-breaking cracks in metals. LT technique is well known for its non-contact nature and relatively fast-detection of surface defects for objects that are at room temperature. In LT, a continuous-wave (CW) laser is used to make a laser spot/line/area, which excites the sample under inspection to create local heating. Using optical mirrors, the spot is then rapidly made to move over the surface of the test sample. The temperature distribution over the sample due to the laser excitation is then monitored using an infrared (IR) thermal camera. This paper investigates the influence of the absorptivity of the material surface during defect detection using LT and its influence in detecting surface-breaking cracks. Six different materials were used under this study, i.e., Aluminum, Brass, Copper, mild Steel, Stainless Steel, and Titanium and with two surface conditions i.e., with and without black paint. 3D FEM numerical models were developed and validated with experiments for the different case studies for surface crack detection. The term Crack Thermal Contrast (CTC) is introduced to indicate the detectability of the surface crack and found to increase linearly with absorptivity of the material. • Laser spot thermography is carried out in different metals surface, ie. With and without black paint coating. • The influence of absorptivity of the material surface in crack detection using laser spot thermography was studied. • 3D FE numerical model developed for laser spot thermography. • A dedicated algorithm developed for defect detection on the metal surface. • The term Crack Thermal Contrast (CTC) is found to increase linearly with absorptivity of the material. [ABSTRACT FROM AUTHOR]

• Detection in steel bars at higher surface temperatures (up to 600 °C). • Investigation of laser line scanning thermography for fast crack detection at higher temperatures. • 3D numerical models for high temperature crack detection. • Sample without an oxide layer and with an oxide layer was used for the studied. • Algorithm for crack detection on moving sample at the higher temperature. Crack detection in steel bars at high surface temperatures is a critical problem in any manufacturing industry. Surface breaking cracks are the major problems during the billet casting. Many NDT techniques are proven its capability in crack detection at room temperature. Here, we are demonstrating the possibility of exposure of cracks using laser line thermography at higher surface temperatures (up to 600 °C). A continuous-wave (CW) laser is used to excite the sample kept at higher surface temperatures. The temperature distribution over the sample due to the laser line scanning is captured using a temperature calibrated infrared (IR) thermal camera. The response of the sample temperature in crack detection is investigated using a validated FE model. The impact of the oxide layer in crack detection is investigated by using two types of samples; one without any oxide layer and the second is with the oxide layer. The influence of laser power in the detection of defects at high temperatures is studied. 3D numerical models were developed for the cases; when the sample is with oxide layer and without any oxide layer for a better understanding of physics. The surface temperature rise due to laser heating is higher for the scaled sample compared to the no-scale sample. The presence of the oxide layer above the parent metal will reduce the reflectivity of the surface. Lower reflectivity will lead to increased absorption of incident energy so that the surface temperature rise will be higher than the surface with no scale. Thermal contrast linearly depends on laser power, which means higher laser power will increase the defect detectability even at a higher surface temperature. [ABSTRACT FROM AUTHOR]

• Novel super resolution techniques for laser line scanning thermography are proposed. • Defect detection in steel could be significantly improved in terms of speed, simplicity and accuracy making this approach highly attractive for industrial application. • Super resolution laser step scanning and continuous scanning are compared using different laser line widths and pulse lengths. • A detailed mathematical description for super resolution laser line scanning thermography is given. • The compressed sensing based iterative joint sparsity approach is discussed and optimization algorithms like Block-FISTA and Block-Elastic-Net are studied. In this paper we propose super resolution measurement and post-processing strategies that can be applied in thermography using laser line scanning. The implementation of these techniques facilitates the separation of two closely spaced defects and avoids the expected deterioration of spatial resolution due to heat diffusion. The experimental studies were performed using a high-power laser as heat source in combination with pulsed thermography measurements (step scanning) or with continuous heating measurements (continuous scanning). Our work shows that laser line step scanning as well as continuous scanning both can be used within our developed super resolution (SR) techniques. Our SR techniques make use of a compressed sensing based algorithm in post-processing, the so-called iterative joint sparsity (IJOSP) approach. The IJOSP method benefits from both - the sparse nature of defects in space as well as from the similarity of each measurement. In addition, we show further methods to improve the reconstruction quality e.g. by simple manipulations in thermal image processing such as by considering the effect of the scanning motion or by using different optimization algorithms within the IJOSP approach. These super resolution image processing methods are discussed so that the advantages and disadvantages of each method can be extracted. Our contribution thus provides new approaches for the implementation of super resolution techniques in laser line scanning thermography and informs about which experimental and post-processing parameters should be chosen to better separate two closely spaced defects. [ABSTRACT FROM AUTHOR]

The surface of Fiber Reinforced Polymer (FRP) concrete is heated with a line laser beam, and the corresponding heat responses are measured by an infrared (IR) camera. Local heat distribution anomalies caused by interfacial debonding between FRP and concrete are automatically traced and detected as defect based on image processing of IR images. Through numerical simulations and lab-scale experiments, the performance of the laser active thermography for the FRP retrofitted concrete inspection was compared with the performance of a halogen lamp based thermography. This comparison shows that the laser active thermography offers a longer inspection range (over 5 m), better damage sensitivity (detecting damage larger than 10 mm diameter), lower power consumption (15W laser excitation), and reduced false alarms due to external noises. [ABSTRACT FROM AUTHOR]