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 - 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 -