TY - JOUR A1 - Hauffen, J. C. A1 - Kästner, L. A1 - Ahmadi, Samim A1 - Jung, P. A1 - Caire, G. A1 - Ziegler, Mathias T1 - Learned block iterative shrinkage thresholding algorithm for photothermal super resolution imaging N2 - 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. KW - Thermography KW - Laser KW - Machine learning KW - Optimization KW - Non-destructive testing KW - NDT KW - Neural network KW - Defect reconstruction KW - Block-sparsity KW - Active thermal imaging KW - Regularization KW - Laser thermography PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-554598 DO - https://doi.org/10.3390/s22155533 SN - 1424-8220 VL - 22 IS - 15 SP - 1 EP - 15 PB - MDPI CY - Basel AN - OPUS4-55459 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Lecompagnon, Julien A1 - Ahmadi, Samim A1 - Hirsch, Philipp Daniel A1 - Rupprecht, C. A1 - Ziegler, Mathias T1 - Thermographic detection of internal defects using 2D photothermal super resolution reconstruction with sequential laser heating N2 - 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. KW - Thermography KW - Super resolution KW - NDT KW - Inspection KW - Internal defects PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-548351 DO - https://doi.org/10.1063/5.0088102 SN - 1089-7550 VL - 131 IS - 18 SP - 1 EP - 12 PB - AIP Publishing AN - OPUS4-54835 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Lecompagnon, Julien A1 - Hirsch, Philipp Daniel A1 - Rupprecht, C. A1 - Ziegler, Mathias T1 - Nondestructive thermographic detection of internal defects using pixel-pattern based laser excitation and photothermal super resolution reconstruction N2 - 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. KW - Thermography KW - Super resolution KW - NDT KW - Inspection KW - Internal defects KW - DMD KW - DLP PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-570551 DO - https://doi.org/10.1038/s41598-023-30494-2 SN - 2045-2322 VL - 13 SP - 1 EP - 13 PB - Nature Research AN - OPUS4-57055 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Yagdjian, H. A1 - Lecompagnon, Julien A1 - Hirsch, Philipp Daniel A1 - Ziegler, Mathias A1 - Gurka, M. T1 - Application of the thermal shock response spectrum (TSRS) methodology to various forms of heat sources by pulse thermography and comparison by using a rotating line scan contour search algorithm N2 - In this paper, we propose a novel contour search algorithm (CSA) for image processing. Its efficacy is evaluated through a comparative analysis with established techniques such as Canny Edge and Snakes: Active contour models, utilizing infrared thermography (IRT) images. Based on the new CSA, we investigate the influence of different pulse shapes on the IRT post-processing methodology, particularly focusing on the thermal shock response spectrum (TSRS), using two different heat sources: xenon flash lamps and a laser. Moreover, this allows for a more precise quantitative comparison of the TSRS with existing IRT post-processing techniques, including pulse phase thermography and thermal signal reconstruction, concerning the detection of defects in composite materials, particularly in carbon fiber-reinforced polymer. A quantitative comparison was performed using the Tanimoto criterion and signal-to-noise ratio. A more detailed analysis is conducted to identify inherent limitations and potential benefits of the new TSRS methodology. We further investigate and experimentally confirm our previous finding on the qualitative correlation between the one-dimensional thermal N-layer model and test data from the TSRS optimization process for defect determination. This correlation can eliminate the time-consuming optimization step, making TSRS a more attractive alternative to common IRT methods and enhancing the quantitative description of defects. KW - Thermography KW - Non-destructive testing KW - NDT KW - Defect identification KW - Laser KW - Contour search PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-615056 DO - https://doi.org/10.1063/5.0232015 SN - 1089-7550 VL - 136 IS - 175101 SP - 1 EP - 20 PB - AIP Publishing AN - OPUS4-61505 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Stamm, Michael T1 - AI-Assisted thermographic and visual classification of leading-edge erosion of wind turbine blades N2 - The wind industry is crucial for carbon neutrality, with turbines featuring blades over 100 meters long. Regular inspections, often manual and visual, struggle to capture subsurface damage or airflow dynamics. Leading-edge erosion, caused by rain and hail, significantly reduces turbine efficiency. The Federal Institute for Materials Research and Testing (BAM) in Berlin is working with industry partners to classify leading-edge damage and estimate yield loss using ground-based thermographic images. These images visualize airflow disruptions caused by erosion. AI models, trained on 1500 thermographic images, can detect and classify this damage. BAM aims to create a reference dataset by 2024, using data from 30 wind turbines. This dataset will include simultaneous thermographic and high-resolution visual images. The project also explores predicting stall and calculating performance loss due to erosion. A secure data platform facilitates data exchange and federated learning, enhancing AI systems with diverse data. KW - NDT KW - Thermography KW - Wind Turbine Blades KW - AI KW - KI-VISIR PY - 2024 UR - https://source.asnt.org/226h005/ SN - 0025-5327 VL - 82 IS - 6 SP - 14 EP - 15 AN - OPUS4-62452 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kunji Purayil, Sruthi Krishna A1 - Balasubramaniam, K. T1 - Data-driven AI for the automated classification of the isothermal heat-treated thermal barrier coatings using pulsed infrared thermography N2 - Development of reliable age prediction models are crucial in monitoring the formation of oxide layer and degradation of TBC at regular intervals. This study proposes an automated classification of isothermal heat-treated TBC samples using temperature data, which helps in predicting the TBC life and monitoring the TBC degradation. TBC-coated samples are isothermal heat-treated at 1000 °C, and the initial growth of thermally grown oxide is monitored using a non-destructive thermal imaging technique. The proposed study integrates data-driven AI (DAI) models and feature extraction techniques to interpret complex thermal patterns measured from the TBC coating surface. The performance of the proposed classification framework is tested using deep learning and classical machine learning models with different types and window sizes of input data. Input data used for validation are raw experiment data, logarithmic of experiment data, polynomial fit data, and thermal signal reconstruction fit coefficients. The maximum c KW - Infrared thermography KW - NDT KW - Artificial intelligence KW - Deep learning KW - Predictive maintenance PY - 2024 DO - https://doi.org/10.1088/1361-6463/ad8ce7 SN - 1361-6463 VL - 58 IS - 4 SP - 1 EP - 10 PB - IOP Publishing CY - Bristol AN - OPUS4-61641 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Scheuschner, Nils A1 - Oster, Simon A1 - Mohr, Gunther A1 - Hilgenberg, Kai T1 - Comparison of NIR and SWIR thermography for defect detection in Laser Powder Bed Fusion N2 - Since laser powder bed fusion (PBF-LB/M) is prone to the formation of defects during the building process, a fundamental requirement for widespread application is to find ways to assure safety and reliability of the additively manufactured parts. A possible solution for this problem lies in the usage of in-situ thermographic monitoring for defect detection. In this contribution we investigate possibilities and limitations of the VIS/NIR wavelength range for defect detection. A VIS/NIR camera can be based on conventional silicon-based sensors which typically have much higher spatial and temporal resolution in the same price range but are more limited in the detectable temperature range than infrared sensors designed for longer wavelengths. To investigate the influence, we compared the thermographic signatures during the creation of artificially provoked defects by local parameter variations in test specimens made of a nickel alloy (UNS N07208) for two different wavelength ranges (~980 nm and ~1600 nm). KW - Laser powder bed fusion KW - PBF-LB/M KW - Thermography KW - Additive manufacturing KW - NDT PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-610380 DO - https://doi.org/10.1016/j.procir.2024.08.122 VL - 124 SP - 301 EP - 304 PB - Elsevier B.V. AN - OPUS4-61038 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Sobczak, M. A1 - Lecompagnon, Julien A1 - Hirsch, Philipp Daniel A1 - Pieczonka, Ł. A1 - Ziegler, Mathias T1 - Impact damage characterization on CFRP parts using laser line scanning active thermography N2 - This study presents a dual-path data processing framework for the detection and characterization of barely visible impact damage (BVID) in carbon-fiber-reinforced polymer (CFRP) structures using laser line thermography (LLT). A robotic LLT system was used to scan impacted CFRP specimens, and the resulting thermal sequences were analyzed using two complementary methods: full thermogram reconstruction followed by Pulse Phase Thermography (PPT) to detect subsurface delaminations, and Time-Summed Gradient Filtering (TSGF) to enhance surface-breaking cracks. Both processing paths produced interpretable results that were fused into a unified combined image and overlay mask, enabling simultaneous visualization of different defect types from a single scan. Quantitative analysis was performed on the binary masks to extract defect dimensions and Signal-to-noise ratio (SNR) values. The results demonstrated that delaminations and multiple cracks could be accurately detected and spatially distinguished, with good agreement to reference methods such as flash thermography and vibrothermography. This work highlights the potential of LLT as a versatile and scalable inspection technique, where multimodal defect detection and segmentation can be achieved through targeted processing and data fusion strategies. KW - Active thermography KW - CFRP KW - Laser line scanning KW - Delamination KW - Cracks KW - BVID KW - NDT PY - 2026 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-654097 DO - https://doi.org/10.1016/j.compositesb.2026.113425 SN - 1359-8368 VL - 313 SP - 1 EP - 9 PB - Elsevier Ltd. AN - OPUS4-65409 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -