8.0 Abteilungsleitung und andere
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Many advanced materials consist of fibers. They are used as nonwovens, fabrics, or in composite materials. Characterization of individual fibers allows us to predict resulting material properties. We present a measurement setup and analysis software to characterize individual, micrometer-sized fibers fast and reliably. The setup is based on the lock-in thermography principle. Thermal diffusivity values of seven reference samples agree very well with previously reported values. We use our setup to investigate critical measurement parameters like excitation frequency, excitation power, pixel size, and fiber orientation. Our results show that fibers with subpixel diameters can be measured even if they are not aligned. However, special care has to be taken to choose an adequate excitation power. Measurements at high intensities can underestimate thermal diffusivity even though the raw data looks reasonable. By automatically measuring at different excitation powers, our setup solves this issue.
Because of its mobility and ability to investigate exposed surfaces, single-sided (SiS) nuclear magnetic resonance (NMR) technology enables new application fields in geosciences. To test and assess its corresponding potential, we compare longitudinal (T1) and transverse (T2) data measured by SiS NMR with those of conventional geoscientific laboratory NMR. We use reference sandstone samples covering a broad range of pore sizes. Our study demonstrates that the lower signal-to-noise ratio of SiS NMR data generally tends to slightly overestimated widths of relaxation time distributions and consequently pore size distributions. While SiS and conventional NMR produce very similar T1 relaxation data, unbiased SiS NMR results for T2 measurements can only be expected for fine material, i.e. clayey or silty sediments and soils with main relaxation times below 0.05s. This limit is given by the diffusion relaxation rate due to the gradient in the primary magnetic field associated with the SiS NMR. Above that limit, i.e. for coarse material, the relaxation data is strongly attenuated. If considering the diffusion relaxation time of 0.2 s in the numerical data inversion process, the information content >0.2s is blurred over a range larger than that of conventional NMR. However, our results show that principle range and magnitudes of the relaxation time distributions are reconstructed to some extent. Regarding these findings, SiS NMR can be helpful to solve geoscientific issues, e.g. to assess the hydro-mechanical properties of the walls of underground facilities or to provide local soil moisture data sets for calibrating indirect remote techniques on the regional scale. The greatest opportunity provided by the SiS NMR technology is the acquisition of profile relaxation data for rocks with significant bedding structures at the µm scale. With this unique feature, SiS NMR can support the understanding and modeling of hydraulic and diffusional anisotropy behavior of sedimentary rocks.
Direct Laser Writing (DLW) and X-ray computed tomography (XCT) both offer unique possibilities in their respective fields. DLW produces full three-dimensional (3D) polymer structures on the microscale with resolutions below 100 nm. The fabricated structures can be analysed by XCT or X-ray microscopy (XRM), which incorporates additional X-ray lenses, in three dimensions down to a minimal basic spatial resolution of about 500 nm or 50 nm, respectively. In this work, two different DLW structures are analysed via XCT. Internal defects are detected and analysed for the purpose of quality control. Defects and structures with sizes down to 1.5 µm are successfully analysed. A 3D reconstruction and internal, hidden features of the fabricated structures are shown and discussed. In a first-of-its-kind study, we demonstrate the detectability of a single-voxel line inside a fabricated structure that would not be detectable with SEM or light microscopy. Furthermore, the direct fabrication on a PET substrate is shown to overcome the high X-ray absorbance of commonly used glass substrates. Attenuation spectra of SZ2080 and glass substrates are compared to a fabrication route direct on a 170 µm PET foil. The practical aspects of XCT measurements for DLW structures on different substrates will be discussed.
In-situ monitoring of the Laser Powder Bed Fusion build process via bi- chromatic optical tomography
(2022)
As metal additive manufacturing (AM) is entering industrial serial production of safety relevant components, the need for reliable process qualification is growing continuously. Especially in strictly regulated industries, such as aviation, the use of AM is strongly dependent on ensuring consistent quality of components. Because of its numerous influencing factors, up to now, the metal AM process is not fully controllable. Today, expensive part qualification processes for each single component are common in industry.
This contribution focusses on bi-chromatic optical tomography as a new approach for AM in-situ quality control. In contrast to classical optical tomography, the emitted process radiation is monitored simultaneously with two temperature calibrated cameras at two separate wavelength bands. This approach allows one to estimate the local maximum temperatures during the manufacturing process, thus increases the comparability of monitoring data of different processes. A new process information level at low investment cost is reachable, compared to, e.g., infrared thermography.
Shape memory alloy structures for actuator and vibration damper applications may be manufactured using wire arc additive manufacturing (W AAM), which is one of the additive manufacturing technologies. Multilayer deposition causes heat accumulation during W AAM, which rises the preheat temperature of the previously created layer. This leads to process instabilities, which result in deviations from the desired dimensions and mechanical properties changes. During W AAM deposition of the wall structure, a systematic research is carried out by adjusting the interlayer delay from 10 to 30 s. When the delay period is increased from 10 to 30 s, the breadth decreases by 45% and the height increases by 33%. Grain refinement occurs when the interlayer delay duration is increased, resulting in better hardness, phase transformation temperature, compressive strength, and shape recovery behavior. This study shows how the interlayer delay affects the behavior of W AAM-built nickel-titanium alloy (NiTi) structures in a variety of applications.
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.
The detection of internal irregularities is crucial for quality assessment in metal-based additive manufacturing (AM) technologies such as laser powder bed fusion (L-PBF). The utilization of in-process thermography as an in situ monitoring tool in combination with post-process X-ray micro computed tomography (XCT) as a reference technique has shown great potential for this aim. Due to the small irregularity dimensions, a precise registration of the datasets is necessary as a
requirement for correlation. In this study, the registration of thermography and XCT reference datasets of a cylindric specimen containing keyhole pores is carried out for the development of a porosity prediction model. The considered datasets show variations in shape, data type and dimensionality, especially due to shrinkage and material elevation effects present in the manufactured part. Since the resulting deformations are challenging for registration, a novel preprocessing methodology is introduced that involves an adaptive volume adjustment algorithm which is based on the porosity distribution in the specimen. Thus, the implementation of a simple three-dimensional image-to-image registration is enabled. The results demonstrate the influence of the part deformation on the resulting porosity location and the importance of registration in terms of irregularity prediction.
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.
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.