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 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-548351 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 - CONF A1 - Krankenhagen, Rainer A1 - Ziegler, Mathias A1 - Maierhofer, Christiane ED - Maldague, X. T1 - Systematic errors in the evaluation of uncorrected data from thermographic lock-in measurements N2 - Lock-in thermography (LT) is based on the correct evaluation of phase differences between the temperature oscillations at different surface regions of the object under test during periodic heating. Since the usual heating procedures contain a DC component, the actual heating pattern achieved is not harmonic. This causes systematic deviations when phase differences are determined by means of harmonic analysis, e.g. with FFT analysis. The resulting errors depend clearly on the ratio between DC and AC amplitude, which is demonstrated at simulated and experimentally recorded temperature transients. Further experimental LT data obtained by different oscillating energy inputs showed a variety of possible shapes of transients with different DC components. T2 - 14th QIRT Conference CY - Berlin, Germany DA - 26.06.2018 KW - NDT KW - Lock-in Thermography KW - FFT PY - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-453768 SP - 539 EP - 547 PB - QIRT Council AN - OPUS4-45376 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Klewe, Tim A1 - Völker, Tobias A1 - Götz, J. A1 - Landmann, M. A1 - Wilsch, Gerd A1 - Kruschwitz, Sabine T1 - Sorting of construction and demolition waste by combining LIBS with NIR spectroscopy N2 - In a joint project of partners from industry and research, the automated recycling of construction and demolition waste (CDW) is investigated and tested by combing laser-induced breakdown spectroscopy (LIBS) and near-infrared (NIR) spectroscopy. Joint processing of information (data fusion) is expected to significantly improve the sorting quality of various materials like concrete, main masonry building materials, organic components, etc., and may enable the detection and separation of impurities such as SO3-cotaining building materials (gypsum, aerated concrete, etc.). The project focuses primarily on the Berlin site to analyze the entire value chain, minimize economic/technological barriers and obstacles at the cluster level, and sustainably increase recovery and recycling rates. First measurements with LIBS and NIR spectroscopy show promising results in distinguishing various material types and indicate the potential for a successful combination. In addition, X-ray fluorescence (XRF) spectroscopy is being performed to obtain more information about the quantitative elemental composition of the different building materials. Future work will apply the developed sorting methodology in a fully automated measurement setup with CDW on a conveyor belt. T2 - NDT-CE 2022 CY - Zurich, Switzerland DA - 16.08.2022 KW - LIBS KW - NDT KW - Circular economy KW - Recycling KW - Material classification PY - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-555531 UR - http://www.ndt.net/?id=27220 VL - 2022/09 SP - 1 EP - 9 PB - NDT.net CY - Bad Breisig AN - OPUS4-55553 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Niederleithinger, Ernst A1 - Maak, Stefan A1 - Mielentz, Frank A1 - Effner, Ute A1 - Strangfeld, Christoph A1 - Timofeev, Juri T1 - Review of recent developments in ultrasonic echo testing of concrete N2 - Ultrasonic methods are used in concrete investigations since decades. While being limited to transmission testing in the laboratory for a while, in-situ echo measurements for structural investigations and condition assessment have made their way into practical application in the past 20 years. However, several challenges remain. On one side, there are technical issues as limitations in depth of penetration, resolution and imaging capabilities. On the other side there are still gaps in validation, standardization and certification, which are limiting the applicability in condition/load capacity assessment. This review reports a couple of developments which will help to overcome these issues. This includes technical developments as new devices which are easier to handle on site or giving a much deeper penetration depth (e.g. the LAUS device at BAM) as well as improvements in imaging by hardware update (e. g. air coupled ultrasound or coded signals) or new software (e. g. RTM imaging). To foster the application in real world projects we are as well working on standardization by developing new reference specimen with international partners which will ensure world-wide comparability of ultrasonic and other methods and quality assurance codes. Further, non-destructive methods are being used to update probabilistic models used for the reassessment of existing structures to support the structural engineer’s decisions. T2 - SMAR 2019 CY - Potsdam, Germany DA - 27.08.2019 KW - Engineered Barrier System (EBS) KW - Ultraschall KW - NDT KW - Re-assessment of exiting bridges PY - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-489686 SP - 1 EP - 6 AN - OPUS4-48968 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Küttenbaum, Stefan A1 - Taffe, A. A1 - Braml, T. A1 - Maack, Stefan ED - Alexander, M. G. ED - Beushausen, H. ED - Dehn, F. ED - Moyo, P. T1 - Reliability assessment of existing bridge constructions based on results of non-destructive testing N2 - The non-destructive testing methods available for civil engineering (NDT-CE) enable the measurements of quantitative parameters, which realistically describe the characteristics of existing buildings. In the past, methods for quality evaluation and concepts for validation expanded into NDT-CE to improve the objectivity of measured data. Thereby, a metrological foundation was developed to collect statistically sound and structurally relevant information about the inner construction of structures without destructive interventions. More recently, the demand for recalculations of structural safety was identified. This paper summarizes a basic research study on structural analyses of bridges in combination with NDT. The aim is to use measurement data of nondestructive testing methods as stochastic quantities in static calculations. Therefore, a methodical interface between the guide to the expression of uncertainty in measurement and probabilistic approximation procedures (e.g. FORM) has been proven to be suitable. The motivation is to relate the scientific approach of the structural analysis with real information coming from existing structures and not with those found in the literature. A case study about the probabilistic bending proof of a reinforced concrete bridge with statistically verified data from ultrasonic measurements shows that the measuring results fulfil the requirements concerning precision, trueness, objectivity and reliability. T2 - International Conference on Concrete Repair, Rehabilitation and Retrofitting (ICCRRR 2018) CY - Cape Town, South Africa DA - 19.11.2018 KW - NDT KW - Concrete KW - Probabilistic reassessment KW - Bridge PY - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-467898 SN - 2261-236X VL - 199 SP - 06001, 1 EP - 9 PB - MATEC Web of Conferences AN - OPUS4-46789 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Puthiyaveettil, N. A1 - Kidangan, R. A1 - Unnikrishnakurup, Sreedhar A1 - Krishnamurthy, C. V. A1 - Ziegler, Mathias A1 - Myrach, Philipp A1 - Balasubramaniam, K. T1 - Numerical study of laser line thermography for crack detection at high temperature N2 - The detection of cracks before the failure is highly significant when it comes to safety-relevant structures. Crack detection in metallic samples at high surface temperature is one of the challenging situation in manufacturing industries. Laser thermography has already proved its detection capability of surface cracks in metallic samples at room temperature. In this work a continuous wave (CW) laser use to generate a laser, which is using to scan the metal surface with notch. The corresponding heat distribution on the surface monitored using infrared thermal (IR) camera. A simplified 3D model for laser thermography is developed and validated with experimental results. A dedicated image processing algorithm developed to improve the detectability of the cracks. To understand the dependency of surface temperature, laser power, laser scanning speed etc. in defect detection, we carried out parametric studies with our validated model. Here we Report the capability of laser thermography in crack detection at elevated temperature. T2 - 14th Quantitative InfraRed Thermography Conference CY - Berlin, Germany DA - 25.06.2018 KW - Thermal contrast KW - Laser Thermography KW - FEM KW - Surface cracks KW - NDT PY - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-499113 SN - 2371-4085 VL - 2018 SP - 685 EP - 686 AN - OPUS4-49911 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 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-570551 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 - Haller, T. A1 - Völker, Christoph A1 - Hartmann, T. T1 - Machine learning based multi-sensor fusion for the nondestructive testing of corrosion in concrete N2 - Half-cell potential mapping (HP) is the most popular non-destructive testing method (NDT) for locating corrosion damage in concrete. It is generally accepted that HP is susceptible to environmental factors caused by salt-related deterioration, such as different moisture and chloride gradients. Additional NDT methods are able to identify distinctive areas but are not yet used to estimate more accurate test results. We present a Supervised Machine Learning (SML) based approach to data fusion of seven different signal features to obtain higher quality information. SMLs are methods that explore (or learn) relationships between different (sensor) data from predefined data labels. To obtain a representative, labelled data set we conducted a comprehensive experiment simulating the deterioration cycle of a chloride exposed device in the laboratory. Our data set consists of 18 measurement campaigns, each containing HP, Ground Penetrating- Radar, Microwave Moisture and Wenner resistivity data. We compare the performance of different ML approaches. Many outperform the best single method, HP. We describe the intrinsic challenges posed by a data-driven approach in NDT and show how future work can help overcome them. T2 - SMAR2019 - 5th International Conference on Smart Monitoring, Assessment and Rehabilitation of Civil Structures CY - Potsdam, Germany DA - 27.08.2019 KW - Machine Learning KW - NDT KW - Half-Cell Potential Mapping KW - Corrosion KW - Reinforced Concrete KW - Data Fusion PY - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-498563 UR - http://www.ndt.net/?id=24890 VL - 25 IS - 1 SP - 24890-1 EP - 24890-8 PB - NDT.net CY - Kirchwald AN - OPUS4-49856 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Klewe, Tim A1 - Völker, Tobias A1 - Landmann, Mirko A1 - Kruschwitz, Sabine T1 - LIBS‐ConSort: Development of a sensor‐based sorting method for construction and demolition waste N2 - AbstractA joint project of partners from industry and research institutions approaches the challenge of construction and demolition waste (CDW) sorting by investigating and testing the combination of laser‐induced breakdown spectroscopy (LIBS) with near‐infrared (NIR) spectroscopy and visual imaging. Joint processing of information (data fusion) is expected to significantly improve the sorting quality of various materials like concrete, main masonry building materials, organic components, etc., and may enable the detection and separation of impurities such as SO3‐cotaining building materials (gypsum, aerated concrete, etc.)Focusing on Berlin as an example, the entire value chain will be analyzed to minimize economic / technological barriers and obstacles at the cluster level and to sustainably increase recovery and recycling rates.The objective of this paper is to present current progress and results of the test stand development combining LIBS with NIR spectroscopy and visual imaging. In the future, this laboratory prototype will serve as a fully automated measurement setup to allow real‐time classification of CDW on a conveyor belt. T2 - 21st Ibausil - International Conference on Building Materials CY - Weimar, Germany DA - 13.09.2023 KW - NDT KW - Material cassification KW - Recycling KW - LIBS KW - Data fusion KW - Construction and demolition waste KW - Circular economy PY - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-590734 SN - 2509-7075 VL - 6 IS - 6 SP - 973 EP - 976 PB - Ernst & Sohn GmbH AN - OPUS4-59073 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - 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 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-554598 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 -