TY - JOUR A1 - Ahmadi, Samim A1 - Burgholzer, P. A1 - Jung, P. A1 - Caire, G. A1 - Ziegler, Mathias T1 - Super resolution laser line scanning thermography N2 - 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. KW - Super resolution KW - Laser thermography KW - Compressed sensing KW - Laser scanning KW - Joint sparsity PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-509796 DO - https://doi.org/10.1016/j.optlaseng.2020.106279 SN - 0143-8166 VL - 134 SP - 106279 PB - Elsevier Ltd. AN - OPUS4-50979 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Thummerer, G. A1 - Mayr, G. A1 - Hirsch, Philipp Daniel A1 - Ziegler, Mathias A1 - Burgholzer, P. T1 - Photothermal Image Reconstruction in Opaque Media with Virtual Wave Backpropagation N2 - 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. KW - Virtual wave concept KW - Thermography KW - Photothermal Technique KW - Image reconstruction PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-506166 DO - https://doi.org/10.1016/j.ndteint.2020.102239 VL - 112 SP - 102239 PB - Elsevier Ltd. CY - Netherlands AN - OPUS4-50616 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Ahmadi, Samim A1 - Burgholzer, P. A1 - Mayr, G. A1 - Jung, P. A1 - Caire, G. A1 - Ziegler, Mathias T1 - Photothermal super resolution imaging: A comparison of different thermographic reconstruction techniques N2 - This paper presents different super resolution reconstruction techniques to overcome the spatial resolution limits in thermography. Pseudo-random blind structured illumination from a onedimensional laser array is used as heat source for super resolution thermography. Pulsed thermography measurements using an infrared camera with a high frame rate sampling lead to a huge amount of data. To handle this large data set, thermographic reconstruction techniques are an essential step of the overall reconstruction process. Four different thermographic reconstruction techniques are analyzed based on the Fourier transform amplitude, principal component analysis, virtual wave reconstruction and the maximum thermogram. The application of those methods results in a sparse basis representation of the measured data and serves as input for a compressed sensing based algorithm called iterative joint sparsity (IJOSP). Since the thermographic reconstruction techniques have a high influence on the result of the IJOSP algorithm, this paper Highlights their Advantages and disadvantages. KW - Super resolution KW - Compressed sensing KW - Laser thermography KW - Virtual wave KW - Defect reconstruction PY - 2020 DO - https://doi.org/10.1016/j.ndteint.2020.102228 VL - 111 SP - 2228 PB - Elsevier Ltd. CY - Netherlands AN - OPUS4-50419 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Jonietz, Florian A1 - Myrach, Philipp A1 - Suwala, H. A1 - Ziegler, Mathias T1 - Examination of Spot Welded Joints with Active Thermography N2 - The method described here allows to determine the size of the thermal contact between two metal sheets joined by spot welding. This size is a measure for the size of the weld nugget, i.e. the zone melted during the welding process, and thus the quality of the welded joint. The method applies active thermography in transmission or reflection setup. Especially the reflection setup offers an attractive possibility for non-destructive testing when components can be accessed from one side only. The spot weld region is optically heated by laser or flash light radiation. The weld nugget provides the mechanical joint, but also constitutes a thermal bridge between the two welded sheets. The latter will be exploited in this method. The better thermal contact at the weld Nugget contrasts with the surrounding material, where the heat transfer between the two sheets is comparatively low. A major advantage of the described method is the applicability on sheets without any surface treatment. This is achieved by a proper normalization of the data, allowing for a correction of the varying surface emissivity. KW - Active thermography KW - Spot weld KW - Automotive industry PY - 2016 UR - http://link.springer.com/article/10.1007/s10921-015-0318-4?wt_mc=internal.event.1.SEM.ArticleAuthorIncrementalIssue DO - https://doi.org/10.1007/s10921-015-0318-4 VL - 35:1 SP - 1 EP - 14 PB - Springer CY - New York AN - OPUS4-35480 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Puthiyaveettil, N. A1 - Thomas, K. R. A1 - Myrach, Philipp A1 - Ziegler, Mathias A1 - Rajagopal, P. A1 - Balasubramaniamam, K. T1 - Defect detection in steel bars up to 600 °C using laser line thermography N2 - 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. KW - Laser thermography KW - High temperature KW - Modeling KW - Surface cracks KW - Non-destructive testing PY - 2020 DO - https://doi.org/10.1016/j.infrared.2020.103565 SN - 1350-4495 VL - 111 SP - 103565 PB - Elsevier B.V. AN - OPUS4-51573 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Ahmadi, Samim A1 - Lecompagnon, Julien A1 - Hirsch, Philipp Daniel A1 - Burgholzer, P. A1 - Jung, P. A1 - Caire, G. A1 - Ziegler, Mathias T1 - Laser excited super resolution thermal imaging for nondestructive inspection of internal defects N2 - A photothermal super resolution technique is proposed for an improved inspection of internal defects. To evaluate the potential of the laser-based thermographic technique, an additively manufactured stainless steel specimen with closely spaced internal cavities is used. Four different experimental configurations in transmission, reflection, stepwise and continuous scanning are investigated. The applied image post-processing method is based on compressed sensing and makes use of the block sparsity from multiple measurement events. This concerted approach of experimental measurement strategy and numerical optimization enables the resolution of internal defects and outperforms conventional thermographic inspection techniques. KW - Super Resolution KW - Laser Thermography KW - Non Destructive Testing KW - Comressed Sensing KW - Inverse Problem KW - Thermography PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-519016 DO - https://doi.org/10.1038/s41598-020-77979-y VL - 10 IS - 1 SP - 22357 PB - Springer Nature AN - OPUS4-51901 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Pech May, Nelson Wilbur A1 - Ziegler, Mathias T1 - Detection of surface breaking cracks using flying line laser thermography: A Canny-based algorithm N2 - In this work, we introduce a new algorithm for effectual crack detection using flying line laser thermography, based on the well-known Canny approach. The algorithm transforms the input thermographic sequence into an edge map. Experimental measurements are performed on a metallic component that contains surface breaking cracks due to industrial use. The specimen is tested using flying line thermography at different scanning speeds and laser input powers. Results obtained with the proposed algorithm are additionally compared with a previously established algorithm for flying spot thermography. The proposed Canny-based algorithm can be used in automated systems for thermographic non-destructive testing. T2 - Advanced Infrared Technology and Applications 2021 CY - Online meeting DA - 26.10.2021 KW - Canny approach KW - Flying line thermography KW - Crack detection PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-539628 DO - https://doi.org/10.3390/engproc2021008022 SN - 2673-4591 VL - 8 IS - 1 SP - 1 EP - 4 PB - MDPI CY - Basel AN - OPUS4-53962 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kästner, L. A1 - Ahmadi, Samim A1 - Jonietz, Florian A1 - Jung, P. A1 - Caire, G. A1 - Ziegler, Mathias A1 - Lambrecht, J. T1 - Classification of Spot-Welded Joints in Laser Thermography Data Using Convolutional Neural Networks N2 - 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. KW - Active thermal imaging KW - Laser thermography KW - Spot-welded joints KW - Convolutional neural network KW - Classification KW - Data processing PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-524216 DO - https://doi.org/10.1109/ACCESS.2021.3063672 VL - 9 SP - 48303 EP - 48312 AN - OPUS4-52421 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Ahmadi, Samim A1 - Thummerer, G. A1 - Breitwieser, S. A1 - Mayr, G. A1 - Lecompagnon, Julien A1 - Burgholzer, P. A1 - Jung, P. A1 - Caire, G. A1 - Ziegler, Mathias T1 - Multi-dimensional reconstruction of internal defects in additively manufactured steel using photothermal super resolution combined with virtual wave based image processing N2 - We combine three different approaches to greatly enhance the defect reconstruction ability of active thermographic testing. As experimental approach, laser-based structured illumination is performed in a step-wise manner. As an intermediate signal processing step, the virtual wave concept is used in order to effectively convert the notoriously difficult to solve diffusion-based inverse problem into a somewhat milder wavebased inverse problem. As a final step, a compressed-sensing based optimization procedure is applied which efficiently solves the inverse problem by making advantage of the joint sparsity of multiple blind measurements. To evaluate our proposed processing technique, we investigate an additively manufactured stainless steel sample with eight internal defects. The concerted super resolution approach is compared to conventional thermographic reconstruction techniques and shows an at least four times better spatial resolution. KW - Active thermography KW - Additive manufacturing KW - Stainless steel KW - ADMM KW - Block regularization KW - Internal defects KW - Joint sparsity KW - Laser excitation KW - Multi-dimensional reconstruction KW - Photothermal super resolution KW - Virtual waves PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-525330 DO - https://doi.org/10.1109/tii.2021.3054411 SN - 1551-3203 SN - 1941-0050 VL - 17 IS - 11 SP - 7368 EP - 7378 PB - IEEE CY - New York, NY AN - OPUS4-52533 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Ahmadi, Samim A1 - Kästner, L. A1 - Hauffen, Jan Christian A1 - Jung, P. A1 - Ziegler, Mathias T1 - Photothermal-SR-Net: A customized deep unfolding neural network for photothermal super resolution imaging N2 - 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. KW - Deep unfolding KW - Defect reconstruction KW - Elastic net KW - Inverse problems KW - Iterative shrinkage thresholding KW - Neural network KW - Nondestructive testing (NDT) KW - Photothermal imaging KW - Super resolution (SR) KW - Thermography PY - 2022 DO - https://doi.org/10.1109/tim.2022.3154803 SN - 1557-9662 VL - 71 SP - 1 EP - 9 PB - IEEE AN - OPUS4-54678 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -