Analytische Chemie
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A composite material is a combination of two or more materials with very different mechanical, thermal and electrical properties. The various forms of composite materials, due to their high material properties, are widely used as structural materials in the aviation, space, marine, automobile, and sports industries. However, some defects like voids, delamination, or inhomogeneous fiber distribution that form during the fabricating processes of composites can seriously affect the mechanical properties of the composite material. In this study, several imaging NDE techniques such as: thermography, high frequency eddy current, ultrasonic, x-ray radiography, x-ray laminography, and high resolution x-ray CT were conducted to characterize the microstructure of carbon fiber composites. Then, a 3D analysis was implemented by the destructive technique of serial sectioning for the same sample tested by the NDE methods. To better analyze the results of this work and extract a clear volume image for all features and defects contained in the composite material, an intensive comparison was conducted among hundreds of 3D-NDE and multi serial sections’ scan images showing the microstructure variation.
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.
Due to the diffusive nature of heat propagation in solids, the detection and resolution of internal defects with active thermography based non-destructive testing is commonly limited to a defect-depth-to-defect-size ratio greater than or equal to one. In the more recent past, we have already demonstrated that this limitation can be overcome by using a spatially modulated illumination source and photothermal super resolution-based reconstruction. Furthermore, by relying on compressed sensing and computational imaging methods we were able to significantly reduce the experimental complexity to make the method viable for investigating larger regions of interest. In this work we share our progress on improving the defect/inhomogeneity characterization using fully 2D spatially structured illumination patterns instead of scanning with a single laser spot. The experimental approach is based on the repeated blind pseudo-random illumination using modern projector technology and a high-power laser. In the subsequent post-processing, several measurements are then combined by taking advantage of the joint sparsity of the defects within the sample applying 2D-photothermal super resolution reconstruction. Here, enhanced nonlinear convex optimization techniques are utilized for solving the underlying ill-determined inverse problem for typical simple defect geometries. As a result, a higher resolution defect/inhomogeneity map can be obtained at a fraction of the measurement time previously needed.
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.
Among the photothermal methods, full-field thermal imaging is used to characterize materials, to determine thicknesses of layers, or to find inhomogeneities such as voids or cracks. The use of classical light sources such as flash lamps (impulse heating) or halogen lamps (modulated heating) led to a variety of nondestructive testing methods, in particular, lock-in and flash-thermography. In vertical-cavity surface-emitting lasers (VCSELs), laser light is emitted perpendicularly to the surface with a symmetrical beam profile. Due to the vertical structure, they can be arranged in large arrays of many thousands of individual lasers, which allows power scaling into the kilowatt range. Recently, a high-power yet very compact version of such a VCSEL-array became available that offers both the fast timing behavior of a laser as well as the large illumination area of a lamp. Moreover, it allows a spatial and temporal control of the heating because individual parts of the array can be controlled arbitrarily in frequency, amplitude, and phase. In conjunction with a fast infrared camera, such structured heating opens up a field of novel thermal imaging and testing methods. As a first demonstration of this approach, we chose a testing problem very challenging to conventional thermal infrared testing: The detection of very thin subsurface defects perpendicularly oriented to the surface of metallic samples. First, we generate destructively interfering thermal wave fields, which are then affected by the presence of defects within their reach. It turned out that this technique allows highly sensitive detection of subsurface defects down to depths in excess of the usual thermographic rule of thumb, with no need for a reference or surface preparation.
Laser powder bed fusion is one of the most promising additive manufacturing techniques for printing complex-shaped metal components. However, the formation of subsurface porosity poses a significant risk to the service lifetime of the printed parts. In-situ monitoring offers the possibility to detect porosity already during manufacturing. Thereby, process feedback control or a manual process interruption to cut financial losses is enabled.
Short-wave infrared thermography can monitor the thermal history of manufactured parts which is closely connected to the probability of porosity formation. Artificial intelligence methods are increasingly used for porosity prediction from the obtained large amounts of complex monitoring data. In this study, we aim to identify the potential and the challenges of deep-learning-assisted porosity prediction based on thermographic in-situ monitoring.
Therefore, the porosity prediction task is studied in detail using an exemplary dataset from the manufacturing of two Haynes282 cuboid components. Our trained 1D convolutional neural network model shows high performance (R² score of 0.90) for the prediction of local porosity in discrete sub-volumes with dimensions of (700 x 700 x 40) μm³.
It could be demonstrated that the regressor correctly predicts layer-wise porosity changes but presumably has limited capability to predict differences in local porosity. Furthermore, there is a need to study the significance of the used thermogram feature inputs to streamline the model and to adjust the monitoring hardware. Moreover, we identified multiple sources of data uncertainty resulting from the in-situ monitoring setup, the registration with the ground truth X-ray-computed tomography data and the used pre-processing workflow that might influence the model’s performance detrimentally.
This article presents deep unfolding neural networks to handle inverse problems in photothermal radiometry enabling super-resolution (SR) imaging. The photothermal SR approach is a well-known technique to overcome the spatial resolution limitation in photothermal imaging by extracting high-frequency spatial components based on the deconvolution with the thermal point spread function (PSF). However, stable deconvolution can only be achieved by using the sparse structure of defect patterns, which often requires tedious, handcrafted tuning of hyperparameters and results in computationally intensive algorithms. On this account, this article proposes Photothermal-SR-Net, which performs deconvolution by deep unfolding considering the underlying physics. Since defects appear sparsely in materials, our approach includes trained block-sparsity thresholding in each convolutional layer. This enables to super-resolve 2-D thermal images for nondestructive testing (NDT) with a substantially improved convergence rate compared to classic approaches. The performance of the proposed approach is evaluated on various deep unfolding and thresholding approaches. Furthermore, we explored how to increase the reconstruction quality and the computational performance. Thereby, it was found that the computing time for creating high-resolution images could be significantly reduced without decreasing the reconstruction quality by using pixel binning as a preprocessing step.
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.
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.
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.