TY - JOUR A1 - Kruschwitz, Sabine A1 - Munsch, Sarah A1 - Telong, Melissa A1 - Schmidt, Wolfram A1 - Bintz, Thilo A1 - Fladt, Matthias A1 - Stelzner, Ludwig T1 - The NMR core analyzing TOMograph: A multi-functional tool for non-destructive testing of building materials N2 - NMR is becoming increasingly popular for the investigation of building materials as it is a non-invasive technology that does not require any sample preparation nor causes damage to the material. Depending on the specific application it can offer insights into properties like porosity and spatial saturation degree as well as pore structure. Moreover it enables the determination of moisture transport properties and the (re-)distribution of internal moisture into different reservoirs or chemical phases upon damage and curing. However, as yet most investigations were carried out using devices originally either designed for geophysical applications or the analysis of rather homogeneous small scale (< 10 mL) samples. This paper describes the capabilities of an NMR tomograph, which has been specifically optimized for the investigation of larger, heterogeneous building material samples (diameters of up to 72 mm, length of up to 700 mm) with a high flexibility due to interchangeable coils allowing for a high SNR and short echo times (50 - 80 m s). KW - Fire spalling KW - Moisture transport KW - Concrete KW - Cement hydration KW - Sensitivity KW - Supplementary cementitous materials KW - Frost salt attack PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-573755 DO - https://doi.org/10.1016/j.mrl.2023.03.004 SN - 2097-0048 VL - 3 IS - 3 SP - 207 EP - 219 PB - Elsevier B.V. AN - OPUS4-57375 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Strangfeld, Christoph A1 - Schönsee, Eric A1 - Jeyifous, Olubunni Anthony A1 - Mezhov, Alexander A1 - Hüsken, Götz T1 - Introduction of a monitoring system for Bingham fluids in additive manufacturing with concrete N2 - Freeform additive manufacturing of concrete structures is a rising technology in civil engineering with several fascinating advantages. Nonetheless, to ensure reliability and structural integrity, standards and quality control are required in the future to bring this technology into the market. As the concrete is manufactured continuously, continuous quality control of the printing process is also required, i.e. comprehensive process monitoring. At BAM, a test rig will be installed, enabling the printing of concrete structures with a maximum size of 2 m x 1 m x 1 m (l x w x h). Here, process monitoring is the focus of the test rig. In this study, we show the results of the first pump tests, including the measurement of several parameters such as temperature and pressure along the supply system, i.e. from the concrete pump to the printer head. T2 - NDT-CE 2022 CY - Zurich, Switzerland DA - 16.08.2022 KW - Additive manufacturing of concrete KW - Process monitoring KW - Non-destructive testing KW - Bingham fluid PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-556367 SP - 1 EP - 12 AN - OPUS4-55636 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Kruschwitz, Sabine A1 - Lorenzoni, Renata A1 - Telong, Melissa A1 - Lauinger, Robert A1 - Munsch, Sarah A1 - Schmidt, Wolfram T1 - Investigation of the hydration of clinker-reduced cementitious binders by 1H NMR N2 - In this paper, we demonstrate the value of 1H NMR relaxometry for studying the hydration of clinker-reduced, more climate-friendly cementitious binders. The results were obtained on typical CEM I cements and sister samples containing two different reactive agricultural ashes as well as non-reactive biochars as supplementary cementitious materials. The findings prove that time-resolved NMR measurements provide valuable additional information when combined with classical heat flow calorimetry. T2 - International Conference on Electromagnetic Wave Interaction with Water and Moist Substances of the International Society for Electromagnetic Aquametry (ISEMA) 2023 CY - Brisbane, Ausralia DA - 25.09.2023 KW - Carbon emission KW - 1H NMR relaxometry KW - Heat flow calorimetry KW - Cement, hydration KW - Clinker reduction PY - 2023 SP - 1 EP - 3 AN - OPUS4-58936 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bintz, Thilo A1 - Munsch, Sarah Mandy A1 - Stelzner, Ludwig A1 - Lauinger, Robert A1 - Schmidt, Wolfram A1 - Kruschwitz, Sabine T1 - An NMR tomograph for building materials - applications, experimental studies and limitations - N2 - A summary of the possibilities, technical limitations and application examples for a unique NMR tomograph at BAM. T2 - 13th International Conference on Electromagnetic Wave Interaction with Water and Moist Substances (ISEMA), 2021 CY - Kiel, Germany DA - 27.06.2021 KW - Relaxation time distribution KW - NMR imaging KW - Capillary suction PY - 2021 SN - 978-1-7281-8738-9 N1 - Geburtsname von Munsch, Sarah Mandy: Nagel, S. M. - Birth name of Munsch, Sarah Mandy: Nagel, S. M. SP - 106 EP - 110 PB - IEEE AN - OPUS4-53116 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schönsee, Eric A1 - Strangfeld, Christoph A1 - Jeyifous, Olubunmi Anthony A1 - Hüsken, Götz A1 - Mezhov, Alexander T1 - Calculating rheological properties of fresh mortar for additive manufacturing based on experimental, multi sensor data N2 - Additive manufacturing of concrete structures is a novel and emerging technology. Free contouring in civil engineering, which allows for entirely new designs, is a significant advantage. In the future, lower construction costs are expected with increased construction speeds and decreasing required materials and workers. However, architects and civil engineers rely on a certain quality of execution to fulfil construction standards. Although several techniques and approaches demonstrate the advantages, quality control during printing is highly challenging and rarely applied. Due to the continuous mixing process commonly used in 3D concrete printing, it is impossible to exclude variations in the dry mixture or water content, and a test sample cannot be taken as a representative sample for the whole structure. Although mortar properties vary only locally, a defect in one layer during printing could affect the entire integrity of the whole structure . Therefore, real-time process monitoring is required to record and document the printing process. At the Bundesanstalt für Materialforschung und -prüfung (BAM) a new test rig for the additive manufacturing of concrete is built. The primary purpose is measuring and monitoring the properties of a mortar during the printing process. The following study investigates an approach for calculating yield stress and plastic viscosity based on experimentally recorded pressure data. The calculations assume that fresh mortar behaves as a Bingham fluid and that the Buckingham-Reiner-equation is applicable. A test setup consisting of rigid pipes with integrated pressure sensors at different positions is utilized. Monitoring the printing process with different sensors is crucial for the quality control of an ongoing process. T2 - Non-Tradijtional Cement and Concrete CY - Brno, Czech Republic DA - 25.06.2023 KW - 3DCP KW - Monitoring KW - Additive Manufacturing KW - Rheology KW - Bingham Fluid PY - 2023 AN - OPUS4-58144 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Strangfeld, Christoph A1 - Schönsee, Eric A1 - Jeyifous, Olubunmi Anthony A1 - Mezhov, Alexander A1 - Hüsken, Götz T1 - Introduction of a monitoring system for Bingham fluids in additive manufacturing with concrete N2 - Freeform additive manufacturing of concrete structures is a rising technology in civil engineering with several fascinating advantages. Nonetheless, to ensure reliability and structural integrity, standards and quality control are required in the future to bring this technology into the market. As the concrete is manufactured continuously, continuous quality control of the printing process is also required, i.e. comprehensive process monitoring. At BAM, a test rig will be installed, enabling the printing of concrete structures with a maximum size of 2 m x 1 m x 1 m (l x w x h). Here, process monitoring is the focus of the test rig. In this study, we show the results of the first pump tests, including the measurement of several parameters such as temperature and pressure along the supply system, i.e. from the concrete pump to the printer head. T2 - NDT-CE 2022 CY - Zurich, Switzerland DA - 16.08.2022 KW - Additive manufacturing of concrete KW - Process monitoring KW - Non-destructive testing KW - Bingham fluid PY - 2022 AN - OPUS4-55637 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bintz, Thilo A1 - Munsch, Sarah Mandy A1 - Stelzner, Ludwig A1 - Lauinger, R. A1 - Schmidt, Wolfram A1 - Kruschwitz, Sabine T1 - An NMR tomograph for building materials - applications , experimental studies and limitations N2 - Introduction of the NMR Tomograph of Department 8.0 at BAM, the applications, technical limitations, and uses in laboratory studies. T2 - International Conference on Electromagnetic Wave Interaction with Water and Moist Substances (ISEMA) 2021 CY - Kiel, Germany DA - 26.07.2021 KW - NMR relaxometry KW - NMR imaging KW - Sensitivity KW - Building materials PY - 2021 N1 - Geburtsname von Munsch, Sarah Mandy: Nagel, S. M. - Birth name of Munsch, Sarah Mandy: Nagel, S. M. AN - OPUS4-53043 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Maierhofer, Christiane A1 - Krankenhagen, Rainer A1 - Altenburg, Simon A1 - Röllig, Mathias A1 - Myrach, Philipp T1 - Passive and active thermography applied to buildings and cultural heritage - Cracks and protection layers N2 - Artificial and natural cracks have been investigated with active and passive thermography. For the determination of the thickness of protection layers on concrete, an active thermography method has been developed. T2 - Short Courses of the QIRT 2018 Conference CY - Berlin, Germany DA - 25.6.2018 KW - Active themrography KW - Passive thermography KW - Cracks KW - Concrete PY - 2018 AN - OPUS4-45456 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Pelkner, Matthias A1 - Casperson, Ralf A1 - Pohl, Rainer A1 - Becker, Ben T1 - Conventional and HF Eddy Current Testing of Composite Pressure Vessels N2 - Composite pressure vessels consist of an internal liner (Steel, aluminum, or plastic) which stores the liquid or gas and CFRP (carbon fiber reinforced plastics) which serves as pressure resistance. These vessels comprise some advantages compared with conventional vessels made of steel or aluminum and their application grew strongly over the last decade. The reason for this trend is the tremendous weight saving. However, the long-time behavior for filling and discharging cycles and the creep strength of composite vessels is not fully understood. Therefore, BAM started an internal project to investigate degradation of these material systems. Normally, high-frequency eddy current testing (HF-ET, f > 10 MHz) is deployed for CFRP due to the low conductivity of the fibers, which is in the order of 0.01 MS/s, and the capacitive coupling between them. Nevertheless, in some cases conventional ET can be applied. Some first results using conventional ET for detecting leakages in the internal liners are already presented. This contribution gives an update of our investigation using both conventional and high-frequency ET. Therefore, we developed an ET equipment based on a vector network analyzer using frequencies between 300 kHz and 2 GHz. The focus concerns the study of the aging effects. T2 - 12th European conference on Non-Destructive Testing (ECNDT) CY - Gothenburg, Sweden DA - 11.06.2018 KW - Eddy current testing KW - Composite KW - Pressure vessel PY - 2018 AN - OPUS4-45302 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Altenburg, Simon A1 - Krankenhagen, Rainer T1 - Continuous and Laplace transformable approximation for the temporal pulse shape of Xe-flash lamps for flash thermography N2 - In flash thermography, the temperature transient is strongly influenced by the temporal shape of the heating pulse for samples with high thermal diffusivity or very thin samples. Here, we present a closed phenomenological approximation of the temporal shape of pulses of Xe-flash lamps. It is a non-stitched solution, has a simple Laplace transform and is suitable for different lamps and energy settings. It is demonstrated that simulated temperature transients, based on this approximation, are well consistent with experimental data. T2 - 14th Quantitative InfraRed Thermography Conference CY - Berlin, Germany DA - 25.06.2018 KW - Finite pulse length KW - Flash thermography KW - Laplace transform KW - NDT KW - Pulse shape KW - Thickness determination PY - 2018 AN - OPUS4-45411 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Heckel, Thomas A1 - Vahlsing, Thorsten A1 - Raum, Hanne A1 - Casperson, Ralf A1 - Pohl, Rainer A1 - Heckel, Thomas A1 - Beilken, D. A1 - Dilz, K. A1 - Rühe, S. T1 - FE-simulation of eddy current signals produced from basic model cracks for running surface rail defects N2 - Non-destructive testing for surface crack detection and head check depth quantification at the gauge corner of railway tracks can be achieved using eddy current methods. With the extension of the tested zone to the running surface, rail defect signal types other than head checks can be measured. Due to their mostly irregular shape, a quantitation based on a calibration against regular test cracks of varying depth may not be linear. Estimates of the expected influence of more complex crack patterns may be obtained by a finite element simulation of sufficiently simple limiting cases, like two displaced or intersecting cracks or a simply branched or flexed crack. As a first step, a 3D finite element model of the HC10 eddy current probe distributed by Prüftechnik Linke und Rühe (PLR), Germany was built and verified against measured results from an (easily fabricated) reference block with isolated long cracks. T2 - Railway Engineering 2019 CY - Edinburgh, UK DA - 03.07.2019 KW - Finite element analysis KW - Eddy current testing KW - Rail inspection PY - 2019 AN - OPUS4-48530 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Pelkner, Matthias A1 - Maierhofer, Christiane A1 - Altenburg, Simon T1 - Process monitoring in metal AM@BAM, the project ProMoAM N2 - In this presentation we give an overview of activities regarding non-destructive testing of AM components during the manufacturing process. For this purpose, we installed different NDT methods inside the AM systems. The results obtained during the manufacturing process are presented and discussed. T2 - 74th IIW Annual Assembly and International Conference CY - Online meeting DA - 07.07.2021 KW - Non-destructive testing KW - Additive manufacturing KW - Online monitoring PY - 2021 AN - OPUS4-53186 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bernegger, Raphael A1 - Altenburg, Simon A1 - Maierhofer, Christiane T1 - Iterative numerical 2D-modelling for quantification of material defects by pulsed thermography N2 - This paper presents a method to quantify the geometry of defects such as flat bottom holes (FBH) and notches in opaque materials by a pulse thermography (PT) experiment and a numerical model. The aim was to precisely describe PT experiments in reflection configuration with a simple and fast numerical model in order to use this model and a fit algorithm to quantify defects within the material. The algorithm minimizes the difference between the time sequence of a line shaped region of interest (ROI) on the surface (above the defect) from the PT experiment and the numerical data. Therefore, the experimental data can be reconstructed with the numerical model. In this way, the defect depth of a notch or FBH and its width or diameter was determined simultaneously. A laser was used for heating which was widened to a top hat spatial profile to ensure homogeneous illumination (rectangular impulse profile in time). The numerical simulation considers heating conditions and takes thermal losses due to convection and radiation into account. We quantified the geometry of FBH and notches in steel and polyvinyl chloride plasticized (PVC-U) materials with an accuracy of < 5 %. KW - Pulsed thermography KW - Numerical modelling KW - Data reconstruction KW - Opaque materials KW - 2D model KW - Flat bottom holes KW - Notches PY - 2019 DO - https://doi.org/10.1063/1.5099719 SN - 0094-243X SP - 020015-1 EP - 11 PB - AIP AN - OPUS4-47974 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Pavasarytė, Lina A1 - Azevedo do Nascimento, Allana A1 - Cysne Barbosa, Ana Paula A1 - Trappe, Volker A1 - Melo, Daniel T1 - Effects of particle size and particle concentration of poly (ethylene-co-methacrylic acid) on properties of epoxy resin N2 - Self-healing polymers have been developed to improve durability and reduce costs associated with maintenance during service. The addition of thermoplastics to thermosets to produce mendable polymers appears as a promising selfhealing technique. In this study, poly (ethylene-co-methacrylic acid) (EMAA) was added to epoxy resin and the effects of EMAA addition on epoxy properties were evaluated. Specimens with two different contents of thermoplastic and particles sizes were manufactured. A two-level full factorial experimental design was used to evaluate the effect of particle size and particle content on properties of epoxy modified with addition of EMAA. Tensile tests and dynamic mechanical analysis (DMA) were used and the evaluated responses were tensile strength, modulus of elasticity, and glass transition temperature (Tg). X-ray computed tomography (XCT) was used to investigate particle size and concentration after manufacturing. It was found that the particle concentration has greater effects on stress–strain behavior of epoxy while Tg was not significantly affected by neither of the analyzed entrance variables. KW - Fracture KW - Self-healing KW - Epoxy KW - Thermoplastic PY - 2024 DO - https://doi.org/10.1002/app.55677 SN - 0021-8995 SP - 1 EP - 14 PB - Wiley online library AN - OPUS4-60205 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Oster, Simon A1 - Scheuschner, Nils A1 - Chand, Keerthana A1 - Breese, Philipp Peter A1 - Becker, Tina A1 - Heinrichsdorff, F. A1 - Altenburg, Simon T1 - From Thermographic In-situ Monitoring to Porosity Detection – A Deep Learning Framework for Quality Control in Laser Powder Bed Fusion N2 - In this study, we present an enhanced deep learning framework for the prediction of porosity based on thermographic in-situ monitoring data of laser powder bed fusion processes. The manufacturing of two cuboid specimens from Haynes 282 (Ni-based alloy) powder was monitored by a short-wave infrared camera. We use thermogram feature data and x-ray computed tomography data to train a convolutional neural network classifier. The classifier is used to perform a multi-class prediction of the spatially resolved porosity level in small sub-volumes of the specimen bulk. T2 - SMSI - Sensor and Measurement Science International 2023 CY - Nürnberg, Germany DA - 08.05.2023 KW - Porosity KW - Laser powder bed fusion KW - In-situ monitoring KW - Thermography KW - Machine Learning PY - 2023 UR - https://www.ama-science.org/proceedings/details/4404 DO - https://doi.org/10.5162/SMSI2023/C5.4 SP - 179 EP - 180 AN - OPUS4-57616 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Oster, Simon A1 - Breese, Philipp Peter A1 - Ulbricht, Alexander A1 - Mohr, Gunther A1 - Altenburg, Simon T1 - A deep learning framework for defect prediction based on thermographic in-situmonitoring in laser powder bed fusion N2 - The prediction of porosity is a crucial task for metal based additive manufacturing techniques such as laser powder bed fusion. Short wave infrared thermography as an in-situ monitoring tool enables the measurement of the surface radiosity during the laser exposure. Based on the thermogram data, the thermal history of the component can be reconstructed which is closely related to the resulting mechanical properties and to the formation of porosity in the part. In this study, we present a novel framework for the local prediction of porosity based on extracted features from thermogram data. The framework consists of a data pre-processing workflow and a supervised deep learning classifier architecture. The data pre-processing workflow generates samples from thermogram feature data by including feature information from multiple subsequent layers. Thereby, the prediction of the occurrence of complex process phenomena such as keyhole pores is enabled. A custom convolutional neural network model is used for classification. Themodel is trained and tested on a dataset from thermographic in-situ monitoring of the manufacturing of an AISI 316L stainless steel test component. The impact of the pre-processing parameters and the local void distribution on the classification performance is studied in detail. The presented model achieves an accuracy of 0.96 and an f1-Score of 0.86 for predicting keyhole porosity in small sub-volumes with a dimension of (700 × 700 × 50) μm3. Furthermore, we show that pre-processing parameters such as the porosity threshold for sample labeling and the number of included subsequent layers are influential for the model performance. Moreover, the model prediction is shown to be sensitive to local porosity changes although it is trained on binary labeled data that disregards the actual sample porosity. KW - Laser Powder Bed Fusion (PBF-LB/M, L-PBF) KW - Selective Laser Melting (SLM) KW - SWIR thermography KW - Online monitoring KW - Flaw detection KW - Machine learning KW - Convolutional neural networks (CNN) PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-575148 DO - https://doi.org/10.1007/s10845-023-02117-0 SN - 0956-5515 SP - 1 EP - 20 PB - Springer AN - OPUS4-57514 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Maierhofer, Christiane A1 - Thiel, Erik A1 - Altenburg, Simon A1 - Mohr, Gunther A1 - Thiede, Tobias A1 - Mishurova, Tatiana A1 - Paul, Andrea A1 - Kranzmann, Axel A1 - Hilgenberg, Kai A1 - Pittner, Andreas A1 - Bruno, Giovanni A1 - Sommer, Konstantin A1 - Gumenyuk, Andrey T1 - Quality control in additive manufacturing via in-situ monitoring and non-destructive testing N2 - More than 80 representatives of SMEs, industrial companies and research institutes met on September 12 at the workshop "Challenges in Additive Manufacturing: Innovative Materials and Quality Control" at BAM in Adlershof to discuss the latest developments in materials and quality control in additive manufacturing. In special lectures, researchers, users and equipment manufacturers reported on the latest and future developments in additive manufacturing. Furthermore, funding opportunities for projects between SMEs and research institutions on a national and European level were presented. T2 - Challenges in Additive Manufacturing: Innovative Materials and Quality Control CY - Berlin, Germany DA - 12.09.2018 KW - Additive manufacturing KW - Quality control KW - Non-destructive testing KW - In-situ monitoring PY - 2018 AN - OPUS4-46072 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Oster, Simon A1 - Scheuschner, Nils A1 - Chand, Keerthana A1 - Altenburg, Simon A1 - Gerlach, G. T1 - Potentials and challenges of deep-learning-assisted porosity prediction based on thermographic in-situ monitoring in laser powder bed fusion N2 - Laser powder bed fusion is one of the most promising additive manufacturing techniques for printing complex-shaped metal components. However, the formation of subsurface porosity poses a significant risk to the service lifetime of the printed parts. In-situ monitoring offers the possibility to detect porosity already during manufacturing. Thereby, process feedback control or a manual process interruption to cut financial losses is enabled. Short-wave infrared thermography can monitor the thermal history of manufactured parts which is closely connected to the probability of porosity formation. Artificial intelligence methods are increasingly used for porosity prediction from the obtained large amounts of complex monitoring data. In this study, we aim to identify the potential and the challenges of deep-learning-assisted porosity prediction based on thermographic in-situ monitoring. Therefore, the porosity prediction task is studied in detail using an exemplary dataset from the manufacturing of two Haynes282 cuboid components. Our trained 1D convolutional neural network model shows high performance (R² score of 0.90) for the prediction of local porosity in discrete sub-volumes with dimensions of (700 x 700 x 40) μm³. It could be demonstrated that the regressor correctly predicts layer-wise porosity changes but presumably has limited capability to predict differences in local porosity. Furthermore, there is a need to study the significance of the used thermogram feature inputs to streamline the model and to adjust the monitoring hardware. Moreover, we identified multiple sources of data uncertainty resulting from the in-situ monitoring setup, the registration with the ground truth X-ray-computed tomography data and the used pre-processing workflow that might influence the model’s performance detrimentally. T2 - XXXVII. Messtechnisches Symposium 2023 CY - Freiburg, Germany DA - 27.09.2023 KW - Porosity prediction KW - Defect detection KW - Laser powder bed fusion (PBF-LB/M, L-PBF) KW - Selective laser melting KW - Thermography KW - Machine learning PY - 2023 DO - https://doi.org/10.1515/teme-2023-0062 SN - 0171-8096 SN - 2196-7113 VL - 90 SP - 85 EP - 96 PB - De Gruyter CY - Berlin AN - OPUS4-58366 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Pelkner, Matthias A1 - Stegemann, Robert A1 - Sonntag, Nadja A1 - Pohl, Rainer A1 - Kreutzbruck, Marc T1 - Benefits of GMR sensors for high spatial resolution NDT applications N2 - Magneto resistance sensors like GMR (giant magneto resistance) or TMR (tunnel magneto resistance) are widely used in industrial applications, examples are position measurement and read heads of hard disk drives. However, in case of non-destructive testing (NDT) applications these sensors, although their properties are outstanding like high spatial resolution, high field sensitivity, low cost and low energy consumption, never reached a technical transfer to an application beyond sci-entific scope. This paper deals with benefits of GMR/TMR sensors in terms of high spatial resolution testing for different NDT applica-tions. The first example demonstrates the preeminent advantages of MR-elements compared with conventional coils used in eddy current testing (ET). The probe comprises one-wire excitation with an array of MR elements. This led to a better spatial resolution in terms of neighboring defects. The second section concentrates on MFL-testing (magnetic flux leakage) with active field excitation during and before test-ing. The latter illustrated the capability of highly resolved crack detection of a crossed notch. This example is best suited to show the ability of tiny magnetic field sensors for magnetic material characterization of a sample surface. Another example is based on characterization of samples after tensile test. Here, no external field is applied. The magnetization is only changed due to external load and magnetostriction leading to a field signature which GMR sensors can resolve. This gives access to internal changes of the magnetization state of the sample under test. T2 - QNDE 2017 CY - Provo, Utah, USA DA - 16.07.2017 KW - GMR KW - Non-destructive testing KW - Sensor arrays KW - Spatial resolution PY - 2018 SN - 978-0-7354-1644-4 DO - https://doi.org/10.1063/1.5031535 SN - 0094-243X VL - 1949 SP - UNSP 040001, 1 EP - 10 AN - OPUS4-45050 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Oster, Simon A1 - Scheuschner, Nils A1 - Chand, Keerthana A1 - Altenburg, Simon A1 - Gerlach, Gerald T1 - Potentials and challenges of deep-learning-assisted porosity prediction based on thermographic in-situ monitoring in PBF-LB/M N2 - Laser powder bed fusion is one of the most promising additive manufacturing techniques for printing complex-shaped metal components. However, the formation of subsurface porosity poses a significant risk to the service lifetime of the printed parts. In-situ monitoring offers the possibility to detect porosity already during manufacturing. Thereby, process feedback control or a manual process interruption to cut financial losses is enabled. Short-wave infrared thermography can monitor the thermal history of manufactured parts which is closely connected to the probability of porosity formation. Artificial intelligence methods are increasingly used for porosity prediction from the obtained large amounts of complex monitoring data. In this study, we aim to identify the potential and the challenges of deep-learning-assisted porosity prediction based on thermographic in-situ monitoring. Therefore, the porosity prediction task is studied in detail using an exemplary dataset from the manufacturing of two Haynes282 cuboid components. Our trained 1D convolutional neural network model shows high performance (R2 score of 0.90) for the prediction of local porosity in discrete sub-volumes with dimensions of (700 x 700 x 40) μm³. It could be demonstrated that the regressor correctly predicts layer-wise porosity changes but presumably has limited capability to predict differences in local porosity. Furthermore, there is a need to study the significance of the used thermogram feature inputs to streamline the model and to adjust the monitoring hardware. Moreover, we identified multiple sources of data uncertainty resulting from the in-situ monitoring setup, the registration with the ground truth X-ray-computed tomography data and the used pre-processing workflow that might influence the model’s performance detrimentally. T2 - XXXVII. Messtechnisches Symposium 2023 CY - Freiburg im Breisgau, Germany DA - 27.09.2023 KW - Porosity prediction KW - Defect detection KW - Laser Powder Bed Fusion (PBF-LB/M, L-PBF) KW - Selective Laser Melting KW - Thermography KW - Machine Learning PY - 2023 AN - OPUS4-59192 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Oster, Simon A1 - Scheuschner, Nils A1 - Chand, Keerthana A1 - Breese, Philipp Peter A1 - Becker, Tina A1 - Heinrichsdorff, F. A1 - Altenburg, Simon T1 - From Thermographic In-situ Monitoring to Porosity Detection – A Deep Learning Framework for Quality Control in Laser Powder Bed Fusion N2 - In this study, we present an enhanced deep learning framework for the prediction of porosity based on thermographic in-situ monitoring data of laser powder bed fusion processes. The manufacturing of two cuboid specimens from Haynes 282 (Ni-based alloy) powder was monitored by a short-wave infrared camera. We use thermogram feature data and x-ray computed tomography data to train a convolutional neural network classifier. The classifier is used to perform a multi-class prediction of the spatially resolved porosity level in small sub-volumes of the specimen bulk. T2 - Sensor and Measurement Science International CY - Nurnberg, Germany DA - 08.05.2023 KW - Laser powder bed fusion KW - In-situ monitoring KW - Thermography KW - Machine Learning KW - Porosity PY - 2023 AN - OPUS4-57614 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Oster, Simon A1 - Becker, Tina A1 - Breese, Philipp Peter A1 - Scheuschner, Nils A1 - Maierhofer, Christiane A1 - Ulbricht, Alexander A1 - Frisch, Tobias A1 - Mohr, Gunther A1 - Altenburg, Simon T1 - Defect prediction in laser powder bed fusion based on thermographic features utilizing convolutional neural networks N2 - The appearance of irregularities such as keyhole porosity is a major challenge for the production of metal parts by laser powder bed fusion (PBF-LB/M). The utilization of thermographic in-situ monitoring is a promising approach to extract the thermal history which is closely related to the formation of irregularities. In this study, we investigate the utilization of convolutional neural networks to predict keyhole porosity based on thermographic features. Here, the porosity information calculated from an x-ray micro computed tomography scan is used as reference. Feature engineering is performed to enable the model to learn the complex physical characteristics of the porosity formation. The model is examined with regard to the choice of hyperparameters, the significance of thermal features and characteristics of the data acquisition. Based on the results, future demands on irregularity prediction in PBF-LB/M are derived. T2 - GIMC SIMAI YOUNG 2022 CY - Pavia, Italy DA - 29.09.2022 KW - Laser Powder Bed Fusion KW - Thermography KW - In-situ Monitoring KW - Convolutional Neural Networks PY - 2022 AN - OPUS4-56331 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Oster, Simon A1 - Becker, Tina A1 - Breese, Philipp Peter A1 - Scheuschner, Nils A1 - Maierhofer, Christiane A1 - Ulbricht, Alexander A1 - Fritsch, Tobias A1 - Mohr, Gunther A1 - Altenburg, Simon T1 - Defect prediction on the Base of Thermographic features in Laser Powder Bed Fusion Utilizing Machine Learning Algorithms N2 - Avoiding the formation of defects such as keyhole pores is a major challenge for the production of metal parts by Laser Powder Bed Fusion (LPBF). The use of in-situ monitoring by thermographic cameras is a promising approach to detect defects, however the data is hard to analyze by conventional algorithms. Therefore, we investigate the use of Machine Learning (ML) in this study, as it is a suitable tool to model complex processes with many influencing factors. A ML model for defect prediction is created based on features extracted from process thermograms. The porosity information calculated from an x-ray Micro Computed Tomography (µCT) scan is used as reference. Physical characteristics of the keyhole pore formation are incorporated into the model to increase the prediction accuracy. Based on the prediction result, the quality of the input data is inferred and future demands on in-situ monitoring of LPBF processes are derived. T2 - Additive Manufacturing Benchmarks 2022 CY - Bethesda, MA, USA DA - 14.08.2022 KW - Laser Powder Bed Fusion KW - Thermography KW - In-situ Monitoring KW - Machine Learning KW - Defect prediction PY - 2022 AN - OPUS4-55591 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Oster, Simon A1 - Becker, Tina A1 - Breese, Philipp Peter A1 - Scheuschner, Nils A1 - Altenburg, Simon T1 - Machine Learning based defect detection in Laser Powder Bed Fusion utilizing thermographic feature data N2 - The formation of irregularities such as keyhole porosity pose a major challenge to the manufacturing of metal parts by laser powder bed fusion (PBF-LB/M). In-situ thermography as a process monitoring technique shows promising potential in this manner since it is able to extract the thermal history of the part which is closely related to the formation of irregularities. In this study, we investigate the utilization of machine learning algorithms to detect keyhole porosity on the base of thermographic features. Here, as a referential technique, x-ray micro computed tomography is utilized to determine the part's porosity. An enhanced preprocessing workflow inspired by the physics of the keyhole irregularity formation is presented in combination with a customized model architecture. Furthermore, experiments were performed to clarify the role of important parameters of the preprocessing workflow for the task of defect detection . Based on the results, future demands on irregularity prediction in PBF-LB/M are derived. T2 - International Conference on NDE 4.0 CY - Berlin, Germany DA - 24.10.2022 KW - Laser Powder Bed Fusion KW - Thermography KW - In-situ Monitoring KW - Convolutional Neural Networks PY - 2022 AN - OPUS4-56332 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -