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Optical lock-in thermography is a completely contactless and very sensitive NDT technique. As an optical source of energy, incandescent lamps are most commonly used because they are relatively inexpensive and offer high irradiances at the test specimen. However, they are strongly restricted by their low modulation bandwidth with a maximum modulation frequency of only about 1 Hz. The use of high-power kilowattclass laser sources, e.g. diode laser arrays, pushes this constraint beyond 100 Hz. This allows for the exploration of the near-surface region of metals and layer systems with better and more accurate penetration depth and depth resolution. Moreover, these lasers are virtually free of any additional thermal radiation that could interfere with the “true” thermal response emitted from the heated sample. In turn, they can be easily used in a one-sided test configuration. We present current activities with kilowatt-class highpower laser sources for advanced lock-in thermography and focus on the application of laser arrays that offer a very high irradiation strength over a large sample area beyond the mentioned advantages.
Using spatial and temporal shaping of laser-induced diffuse thermal wave fields in thermography
(2020)
The diffuse nature of thermal waves is a fun-damental limitation in thermographic nonde-structive testing. In our studies we investigated different approaches by shaping the thermal wave fields which result from heating. We have used high-power laser sources to heat metallic samples. Using these spatial and temporal shaping techniques leads to a higher detection sensitivity in our measurements with the infra-red camera. In this contribution we show our implementation of shaping laser-induced diffuse thermal wave fields and the effect on the defect reconstruction quality.
Thermographic super resolution techniques allow the spatial resolution of defects/inhomogeneities below the classical limit, which is governed by the diffusion properties of thermal wave propagation. In this work, we report on the extension of this approach towards a full frame 2D super resolution technique. The approach is based on a repeated spatially structured heating using high power lasers. In a second post-processing step, several measurements are coherently combined using mathematical optimization and taking advantage of the (joint) sparsity of the defects in the sample
Thermographic super-resolution techniques allow the resolution of defects/inhomogeneities beyond the classical limit, which is governed by the diffusion properties of thermal wave propagation. Photothermal super-resolution is based on a combination of an experimental scanning strategy and a numerical optimization which has been proven to be superior to standard thermographic methods in the case of 1D linear defects. In this contribution, we report on the extension of this approach towards a full frame 2D photothermal super-resolution technique. The experimental approach is based on a repeated spatially structured heating using high power lasers. In a second post-processing step, several measurements are coherently combined using mathematical optimization and taking advantage of the (joint) sparsity of the defects in the sample. In our work we extend the possibilities of the method to efficiently detect and resolve defect cross sections with a fully 2D-structured blind illumination.
Active thermography as a nondestructive testing modality suffers greatly from the limitations imposed by the diffusive nature of heat conduction in solids. As a rule of thumb, the detection and resolution of internal defects/inhomogeneities is limited to a defect depth to defect size ratio greater than or equal to one. Earlier, we demonstrated that this classical limit can be overcome for 1D and 2D defect geometries by using photothermal laser-scanning super resolution. In this work we report a new experimental approach using 2D spatially structured illumination patterns in conjunction with compressed sensing and computational imaging methods to significantly decrease the experimental complexity and make the method viable for investigating larger regions of interest.
Thermographic photothermal super resolution reconstruction enables the resolution of internal defects/inhomogeneities below the classical limit, which is governed by the diffusion properties of thermal wave propagation. Based on a combination of the application of special sampling strategies and a subsequent numerical optimization step in post-processing, thermographic super resolution has already proven to be superior to standard thermographic methods in the detection of one-dimensional defect/inhomogeneity structures. In our work, we report an extension of the capabilities of the method for efficient detection and resolution of defect cross sections with fully two-dimensional structured laser-based heating. The reconstruction is carried out using one of two different algorithms that are proposed within this work. Both algorithms utilize the combination of several coherent measurements using convex optimization and exploit the sparse nature of defects/inhomogeneities as is typical for most nondestructive testing scenarios. Finally, the performance of each algorithm is rated on reconstruction quality and algorithmic complexity. The presented experimental approach is based on repeated spatially structured heating by a high power laser. As a result, a two-dimensional sparse defect/inhomogeneity map can be obtained. In addition, the obtained results are compared with those of conventional thermographic inspection methods that make use of homogeneous illumination. Due to the sparse nature of the reconstructed defect/inhomogeneity map, this comparison is performed qualitatively.
Classification of Spot-Welded Joints in Laser Thermography Data Using Convolutional Neural Networks
(2021)
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.
Learned block iterative shrinkage thresholding algorithm for photothermal super resolution imaging
(2022)
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.
Using an infrared camera for radiometric imaging allows the contactless temperature measurement of multiple surface pixels simultaneously. From the measured surface data, a sub-surface structure, embedded inside a sample or tissue, can be reconstructed and imaged when heated by an excitation light pulse. The main drawback in radiometric imaging is the degradation of the spatial resolution with increasing depth, which results in blurred images for deeper lying structures. We circumvent this degradation with blind structured illumination, combined with a non-linear joint sparsity reconstruction algorithm. The ground-breaking concept of super-resolution can be transferred from optics to thermographic imaging.
Photothermal radiometry with an infrared camera allows the contactless temperature measurement of multiple surface pixels simultaneously. A short light pulse heats the sample. The heat propagates through the sample by diffusion and the corresponding temperature evolution is measured at the sample’s surface by an infrared camera. The main drawback in radiometric imaging is the loss of the spatial resolution with increasing depth due to heat diffusion, which results in blurred images for deeper lying structures. We circumvent this information loss due to the diffusion process by using blind structured illumination, combined with a non-linear joint sparsity reconstruction algorithm. The structured illumination is realized by parallel laser lines from a vertical-cavity surface-emitting laser (VCSEL) array controlled by a random binary pattern generator. By using 150 different patterns of structured illumination and our iterative joint sparsity algorithm, it was possible to resolve 1 mm thick lines at a distance down to 0.5 mm, which results in a resolution enhancement of approximately a factor of four compared to the resolution of 5.9 mm for homogenous illuminated thermographic reconstruction.