Analytische Chemie
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In the field of optically excited thermography, flash lamps (impulse shaped planar heating) and halogen lamps (modulated planar heating) have become established for the specific regimes of impulse and lock-in thermography. Flying-spot laser thermography is implemented by means of a rasterized focused laser, e. g. for crack detection (continuous wave operation) and photothermal material characterization (high-frequency modulated). The availability of novel technologies, i. e. fast and high-resolution IR cameras, brilliant innovative light sources and high-performance data acquisition and processing technology will enable a paradigm shift from stand-alone photothermal and thermographic techniques to uniform quantitative measurement and testing technology that is faster and more precise. Similar to an LED array, but with irradiance two orders of magnitude higher, a new type of brilliant laser source, i. e. the VCSEL array (vertical-cavity surface-emitting laser), is now available. This novel optical energy source eliminates the strong limitation to the temporal dynamics of established light sources and at the same time is spectrally clearly separated from the detection wavelength. It combines the fast temporal behavior of a diode laser with the high optical irradiance and the wide illumination area of flash lamps. In addition, heating can also be carried out in a structured manner, because individual areas of the VCSEL array can be controlled independently of each other. This new degree of freedom enables the development of completely new thermographic NDT methods.
Thermographic reconstruction of defects that lie in the bulk of a sample is a difficult task because entropy production during heat diffusion leads to information loss. To reconstruct defects one has to solve an inverse heat conduction problem. The quality of the reconstruction is closely related to the information content of the observed data set that is reflected by the decreasing ability to spatially resolve a defect with growing defect depth. In this work we show a 2D reconstruction of rectangular slots with different width-to-depth ratios in a metallic sample. For this purpose, we apply the virtual wave concept and incorporate positivity and sparsity as prior information to overcome the diffusion-based information loss partially. The reconstruction is based on simulated and experimental pulse thermography data. In the first reconstruction step, we compute a virtual wave field from the surface temperature data. This allows us, in the second step, to use ultrasonic backpropagation methods for image reconstruction.
We report on photothermal detection of subsurface defects by coherent superposition of thermal wave fields. This is made possible by structured heating using high-power VCSEL laser arrays whose individual emitter groups can be arbitrarily controlled. In order to locate the defects, we have developed a scanning method based on the continuous wavelet transformation with complex Morlet wavelet using the destructive interference effect of thermal waves. This approach can also be used for thermally very fast and highly reflective materials such as uncoated aluminum. We show that subsurface defects at an aspect ratio of defect width to defect depth down to 1/3 are still detectable in this material.
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.
Crack detection in metallic samples at high surface temperature, hostile and hazardous environments, etc. is challenging situation in any manufacturing industries. Most of the present NDE methods are suitable only for lower surface temperatures, especially room temperature. In this situation, we need a fast and non-contact NDT method which can be applied even in high sample surface temperature. Laser thermography is one of the techniques having a high potential in non-contact inspection. As a preliminary investigation, in this article, we have studied the potentiality of laser line thermography in crack detection at room temperature. In laser line thermography, a continuous wave (CW) laser is used to generate a laser line, which in turn is used to scan the metal surface. The heat distribution over the sample surface is recorded by an infrared thermal (IR) camera. Two different approaches are reported in this work. Firstly, a stationary laser line source and its interaction with cracks; secondly, moving laser line source scanning over a surface with crack. When the distance between crack centre to laser line centre increases, crack detectability will decrease; and when laser power increases, crack detectability will increase. A dedicated image processing algorithm was developed to improve the detectability of the cracks. To understand the heat transfer phenomenon, a simplified 3D model for laser thermography was developed for the heat distribution during laser heating and was validated with experimental results. Defects were incorporated as a thermally thin resistive layer (TTRL) in numerical modeling, and the effect of TTRL in heat conduction is compared with experimental results.
In this work, the limits of using spatially structured heating combined with subsequent photothermal super resolution reconstruction for the thermographic detection and resolution of internal defects are investigated. The approach is based on the application of modern high-power laser projector technology, which is used to repeatedly project varying spatially structured 2D pixel patterns to photothermally heat the object under test. After processing the generated thermographic data using nonlinear convex optimisation in conjunction with exploiting the joint-sparse nature of the defect signals within the individual measurements, a high-resolution 2D-sparse defect/inhomogeneity map is obtained. The main focus of the investigation is set on the influence of the number of individual measurements on the achievable reconstruction quality. Using numerical simulations based on an analytical representation of the forward solution to the underlying inverse problem, the convergence rate over performed measurements of the achievable reconstruction quality is determined. Finally, all findings are experimentally validated by reconstructing a set of internal defects in an additively manufactured sample. In this work, it is shown that for a variety of different defect separation distances, the projection of 50 different pixel patterns allows for a good trade-off between experimental complexity and reconstruction quality.
In this work, we present a novel approach to photothermal super resolution based thermographic resolution of internal defects using two-dimensional pixel pattern-based active photothermal laser heating in conjunction with subsequent numerical reconstruction to achieve a high-resolution reconstruction of internal defect structures. With the proposed adoption of pixelated patterns generated using laser coupled high-power DLP projector technology the complexity for achieving true two-dimensional super resolution can be dramatically reduced taking a crucial step forward towards widespread practical viability. Furthermore, based on the latest developments in high-power DLP projectors, we present their first application for structured pulsed thermographic inspection of macroscopic metal samples. In addition, a forward solution to the underlying inverse problem is proposed along with an appropriate heuristic to find the regularization parameters necessary for the numerical inversion in a laboratory setting. This allows the generation of synthetic measurement data, opening the door for the application of machine learning based methods for future improvements towards full automation of the method. Finally, the proposed method is experimentally validated and shown to outperform several established conventional thermographic testing techniques while conservatively improving the required measurement times by a factor of 8 compared to currently available photothermal super resolution techniques.
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.