TY - CONF A1 - Lecompagnon, Julien T1 - New options for finding defects on and below the surface using structured laser thermography N2 - In infrared thermography, the interaction of the heat flow with the internal geometry or inhomogeneities in a sample and their effect on the transient temperature distribution is used, e.g., to detect defects non-destructively. An equivalent way of describing this is the propagation of thermal waves inside the sample. Although thermography is suitable for a wide range of inhomogeneities and materials, the fundamental limitation is the diffuse nature of thermal waves and the need to measure their effect radiometrically at the sample surface only. The crucial difference between diffuse thermal waves and propagating waves, as they occur, e.g., in ultrasound, is the rapid degradation of spatial resolution with increasing defect depth. This degradation usually limits the applicability of thermography for finding small defects on and below the surface. A promising approach to improve the spatial resolution and thus the detection sensitivity and reconstruction quality of the thermographic technique lies in the shaping of these diffuse thermal wave fields using structured laser thermography. Some examples are: • Narrow crack-like defects below the surface can be detected with high sensitivity by superimposing several interfering thermal wave fields, • Defects very close to each other can be separated by multiple measurements with varying heating structures, • Defects at different depths can be distinguished by an optimized temporal shaping of the thermal excitation function, • Narrow cracks on the surface can be found by robotic scanning with focused laser spots. We present the latest results of this technology obtained with high-power laser systems and modern numerical methods. T2 - 20th World Conference on Non-Destructive Testing (WCNDT) CY - Incheon, Korea DA - 27.05.2024 KW - Thermography KW - Laser KW - NDT KW - Super-Resolution KW - DLP KW - DMD PY - 2024 AN - OPUS4-60172 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Chaudhuri, Somsubhro T1 - Passive infrared thermography as an inspection tool for operational wind turbine rotor blades N2 - The growing wind energy infrastructure presents a significant challenge in the maintenance and operation of wind turbines (WT) and their intricate components. An important aspect of WT maintenance is the inspection of wind turbine rotor blades (WTB) to ensure the overall health and safety of the turbine. This inspection process involves both visual and mechanical examinations of the blades to identify any indicators of damage or wear that could compromise their performance and, consequently, the structural integrity of the entire WT system. The complexity of WTBs is compounded by their ever-expanding dimensions, exceeding 100 meters in length for 16 MW WT systems, and their multi-material composition. Within this context, passive infrared thermography emerges as a potential alternative to conventional contact- or proximity-based inspection methods. Unlike active thermography, passive thermography uses solar radiation and ambient temperature variation for thermal contrast, eliminating the need for traditional heat lamps, flash, or laser-based techniques. A novel inspection method has been developed to semi-autonomously assess wind turbine blades (WTBs) while the wind turbine (WT) is operational, from ground level. This approach leverages optimal thermal contrast, which depends on prevailing weather conditions during field measurements, enabling the visualization of both external and internal features of the WTBs through post-processing techniques. In this study, thermal data obtained through passive thermography is compared with contemporaneous visual imagery to definitively classify observed features in thermal images as either surface or sub-surface features. This analysis, coupled with corresponding weather conditions, provides valuable insights into the capabilities and limitations of the inspection technique. Additionally, finite-element-based (FE) thermal simulations of a WTB section are employed to parametrically assess the influence of weather conditions, beyond those observed during field measurements, based on a validated model. In addition, the thermal images also consist of thermal signatures of leading-edge turbulence due to possible leading-edge erosion in WTBs. These are primarily vortices, and their shape and size depend on the morphology of the damage as well as the rotational speed of the WTBs. The inspections are accompanied by automatic data evaluation of the thermal signatures. To improve the precision of erosion damage identification, a fully convolutional network (FCN) is employed, trained, and tested using over 1000 annotated thermographic blade images. Additionally, the study introduces strategies for grouping smaller damage indications and simplification rules based on realistic thermal imaging resolutions. As leading-edge erosion could potentially lead to annual energy production (AEP) losses, this technique could prove to be a powerful tool in establishing the presence of damage and the resulting AEP loss. T2 - 20th World Conference on Non-Destructive Testing CY - Incheon, South Korea DA - 27.05.2024 KW - Non-destructive testing KW - Thermografie KW - Wind turbine rotor blades KW - Windenergie Anlage Rotorblätter KW - Thermography PY - 2024 AN - OPUS4-60695 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Chaudhuri, Somsubhro A1 - Junker, Birgit T1 - Infrared thermography as an inspection tool for wind turbine rotor blades N2 - With the rapid expansion of wind turbine construction, rotor blades have increasingly been identified as a primary cause of turbine downtime and failure. This has led to a growing demand for inspection technologies through which energy production losses can be minimised—particularly in comparison to conventional methods such as rope-access inspections—and through which sub-surface blade conditions can be assessed to detect damage or failure in advance. Infrared thermography has been considered as a promising non-contact, full-field inspection method that can be applied to rotor blades in both operational and idle states. In this presentation, delivered at BladesEurope 2025, the physical mechanisms that give rise to thermal contrast during thermographic inspection were explained, as this contrast enables the detection and visualisation of structural features. Results obtained from a collaborative project between BAM and Statkraft Norway were presented, followed by the validation of a finite element simulation through experiments conducted in a climate chamber.” T2 - BladesEurope Forum 2025 CY - Edinburgh, Scotland DA - 29.04.2025 KW - Thermography KW - Wind energy KW - Inspection KW - Windenergie anlage rotorblätte KW - Damage detection PY - 2025 AN - OPUS4-63060 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Sridaran Venkat, Ramanan A1 - Stamm, Michael A1 - Wittmann, Jost A1 - Lauterbach, Helge A1 - Bleier, Michael T1 - Harnessing the power of thermal imagery and visual inspection- a mean for reliable damage detection of wind turbine rotor blades N2 - Generation of green electricity as part of the energy transition is leading to a growing market in the wind energy sector all over the world. Maintenance and inspection are key-factor for the reliable, safe and efficient operation of wind turbines. Regular maintenance of rotor blades focuses on damage such as erosion on the leading edge of the profile, delamination and thermal cracks due to lightning strikes. To date, visual inspection by technicians (climbers) has been the state of the art. Recently, drone-based inspections using visual cameras have become more common, enabling fast, reliable and cost-effective inspections. However, no internal damage to the rotor blades are detectable during such an inspection. This deficiency is often compensated by thermography, a recognized method for detecting damages beneath the surface of an object. Thermography has been promoted and continuously developed at BAM for years. In the first part of this paper, the authors present the concept for a drone-based multisensory system that combines a thermal camera, a high-resolution visual camera and a 3D Laser scanner.The system provides a comprehensive view of the rotor blade samples internal and external conditions. In the second part of the paper, we present a lab-based demonstrator comprising an infrared camera and an RGB camera. We elaborate on the process for fusing the images comprising the calibration, image preprocessing and image registration. T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Wind rotor blade inspection KW - Thermography KW - Data fusion KW - Drone inspection KW - Multi-sensors PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-625107 DO - https://doi.org/10.58286/29868 SN - 1435-4934 VL - 29 SP - 1 EP - 8 AN - OPUS4-62510 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Stamm, Michael A1 - Sridaran Venkat, Ramanan T1 - Harnessing the power of thermal imagery and visual inspection- a mean for reliable damage detection of wind turbine rotor blades N2 - Generation of green electricity as part of the energy transition is leading to a growing market in the wind energy sector all over the world. Maintenance and inspection are key to the reliability, safety and efficiency of wind turbines, the regular maintenance of rotor blades focuses on damage such as erosion on the leading edge of the profile, delamination and thermal cracks due to lightning strikes. To date, visual inspection by technicians (climbers) has been the state of the art and it is time consuming besides posing safety risk for themselves. Recently, drone-based inspections using visual cameras have become more common, enabling fast, reliable and cost-effective inspections. However, no internal damage to the rotor blades can be detected during such an inspection. Thermography is a recognised method for detecting damage beneath the surface of an object, which has been promoted and further developed at BAM for years. To enhance the accuracy and reliability of wind turbine blade inspection, the fusion o T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Wind rotor blade inspection KW - Thermography KW - Data fusion KW - Drone inspection KW - Multi-sensors PY - 2024 AN - OPUS4-62515 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 - CONF A1 - Junker, Birgit A1 - Stamm, Michael T1 - No downtime thermographic rotor blade inspection N2 - In this presentation, the KI-VISIR Reference Dataset, created by the Bundesanstalt für Materialforschung und -prüfung (BAM) will be discussed. It includes thermographic and visual inspection data of 30 operational wind turbines. This dataset aims to support the maintenance and inspection of wind turbines, which are increasingly being built across Germany and Europe. The data is publicly available and helps in the development of digital methods for damage classification and inspection. T2 - AMI Wind Turbine Blades CY - Düsseldorf, Germany DA - 10.12.2024 KW - Thermography KW - Wind Turbine Blades KW - AI KW - KI-VISIR KW - NDT PY - 2024 AN - OPUS4-62498 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Stamm, Michael T1 - Damage detection of wind turbine blades with thermographic inspection and AI-based classification N2 - In this presentation, the possibilities, limitations and challenges of thermographic rotor blade inspection will be discussed. Among other things, the data from the KI-VISIR reference data set will be discussed. This data was also used to train an AI-based image recognition system. T2 - WindEurope Technology Workshop 2024 CY - Dublin, Ireland DA - 10.06.2024 KW - Thermography KW - Wind Turbine Blades KW - AI KW - KI-VISIR KW - NDT PY - 2024 AN - OPUS4-62499 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Lapšanská, Ivana T1 - Passive Thermografie als Inspektionsinstrument für Rotorblätter von Windkraftanlagen im Betrieb N2 - Der „European Green Deal“ und der globale Kampf gegen den Klimawandel erfordern mehr und größere Windkraftanlagen. Um dem steigenden Bedarf an Wartung und Inspektion zu bedienen, werden neben der „klassischen“ Inspektion durch Industriekletterer zunehmend auch halb autonome Methoden der Ferninspektion entwickelt. Die BAM arbeitet in mehreren Projekten an der Detektion sowohl inneren als auch oberflächlichen Schäden mittels bodenbasierter passiver Thermografie. Passive Thermografie hat den Vorteil, dass die Sonne als Wärmequelle genutzt wird und so sehr große Objekte ohne direkten Zugriff untersucht werden können. Dies erlaubt eine Inspektion von Rotorblättern von Windenergieanlagen im laufenden Betrieb. Das verhindert Ausfallzeiten und Umsatzeinbußen im Vergleich zu herkömmlichen Blattinspektionen. Neben der Automatisierung der Aufnahmetechnik stehen zwei wissenschaftliche Fragestellungen im Fokus der aktuellen Arbeiten. Auf der einen Seite sind Inspektionen mit passiver Thermografie und deren Ergebnisse stark wetterabhängig. Deswegen bedarf es der einem guten Verständnis dieser Abhängigkeit und einer Kopplung von Simulationen und experimentellen Daten. So kann gezeigt werden, welche Defekte in Rotorblätter passive Thermografie bei welchen Wetterbedingungen sichtbar macht. Auf der anderen Seite sind die Auswertung und Interpretation der schnell erfassten Inspektionsergebnisse zeitaufwendig und verlangen gute Kenntnisse der Thermografie. Um hier praxistauglicher zu werden, arbeitet die BAM an der Erstellung von Algorithmen, die eine Vorauswahl und -interpretation der Inspektionsdaten mit künstlicher Intelligenz ermöglichen. In dem Beitrag wird der messtechnische Aufbau zur bodenbasierten passiven Thermografie an Rotorblättern, der Vergleich von Simulationsdaten und Feldmessungen sowie die Arbeiten bezüglich der KI-basierten Auswertung von thermografischen Aufnahmen gezeigt. T2 - DGZfP Jahrestagung 2024 CY - Osnabrück, Germany DA - 06.05.2024 KW - Wind KW - Windrad KW - Rotorblatt KW - Thermography KW - Thermografie KW - Rotorblade PY - 2024 AN - OPUS4-62500 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Lapšanská, Ivana T1 - Thermographic detection and AI evaluation of leading-edge erosion N2 - The European Green Deal and the global fight against climate change call for more and larger wind turbines. To meet the increasing demand for maintenance and inspection, semi-autonomous methods of remote inspection are increasingly being developed in addition to industrial climbers performing the inspection [1, 2]. Rotor blades are exposed to extreme weather conditions throughout their lifetime causing leading edge erosion which changed the aerodynamic features of blades and can cause structural damages. The estimated annual energy production (AEP) losses caused by erosion damages are between 0.5% and 2% per year. The classification of the severity of the damage and the quantification of the resulting AEP losses are subject of scientific research. With such information, cost efficient repairs and maintenance efforts can improve the power production of wind turbines. Passive thermography presents a viable method for inspecting rotor blades, capable of identifying internal damage [3, 4] and surface erosion [5, 6]. An advantage of this method is that it not only detects rain erosion damage but also reveals temperature variations caused by resulting turbulence on the blade's surface. These turbulent effects reduce rotor blade efficiency, leading to losses in AEP. The inspection process takes approximately 10 minutes per turbine and is carried out while the turbine is fully operational, eliminating downtime and revenue losses typically associated with traditional blade inspections. This efficient inspection procedure is further enhanced by an automatic data analysis system, resulting in a substantial number of wind turbines being inspected within a specified timeframe. The automated evaluation of thermal images is executed using a fully convolutional network (FCN). The initial phase involved training an FCN using over 1500 thermal images. The primary objective was to accurately identify the thermal patterns associated with erosion damage at the leading edge. In the ongoing second phase, the emphasis is on integrating thermographic and visual images to assess the extent of the damage using an FCN. Additionally, this phase involves leveraging human factors in the FCN training process and providing a precise estimation of Annual Energy Production losses attributed to the identified rain erosion damages. T2 - EERA Deep Wind 2024 CY - Trondheim , Norway DA - 17.01.2024 KW - Wind KW - Turbulent flow KW - Thermography KW - Wind rotor blade KW - Inspection PY - 2024 UR - https://www.sintef.no/globalassets/project/eera-deepwind-2024/posters/operation-and-maintenance_ivana_lapsanska_thermographic-detection.pdf AN - OPUS4-62501 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Oster, Simon T1 - In-situ monitoring by thermography: Influence of the scan angle on the melt pool N2 - Online quality control of security relevant parts manufactured by Laser Powder Bed Fusion (LPBF) remains to be a challenge due to the highly complex process conditions. Furthermore, the influence of characteristic scan strategy parameters is not sufficiently clarified yet due to the commonly used method of single-track investigations. In this contribution, this topic is addressed by observing large 316L volume sections using in-situ melt pool monitoring by thermography in high temporal and spatial resolution. In detail, the influence of the scan angle on the melt pool geometry is investigated on. Characteristic melt pool features are extracted from the image data and analyzed using statistical methods data for altering scan angles. The results show significant changes in the melt pool dimensions and temperature distribution over the scan angle rotation. A first explanation approach is presented that connects the observed changes to phenomena of beam attenuation by metal vapor plume. T2 - 1st Workshop on In-situ Monitoring and Microstructure Development in Additive Manufacturing CY - Online meeting DA - 12.10.2020 KW - Laser Powder Bed Fusion KW - Thermography KW - In-situ Monitoring KW - Angle dependency PY - 2020 AN - OPUS4-51953 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Scheuschner, Nils T1 - Emissivity Prediction of Rough Cast Iron for Laser Thermography N2 - This study presents a method for predicting the emissivity of rough cast iron surfaces to enhance the accuracy of laser thermography in industrial non-destructive testing (NDT). Traditional emissivity measurements are impractical in industrial settings due to environmental interference and equipment limitations. The proposed approach replaces complex setups like integrating spheres with a simplified illumination unit and reflection measurements. By leveraging known material properties and surface characteristics, an algorithm predicts directional emissivity. Experimental results of an initial feasibility study show strong agreement between predicted and measured values, demonstrating the method’s potential for real-time emissivity correction in industrial thermographic inspections. T2 - Advanced Infrared Technology and Applications (AITA) 2023 CY - Venice, Italy DA - 10.09.2023 KW - Thermography KW - Laser KW - NDT KW - Cast iron PY - 2023 AN - OPUS4-63121 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Chaudhuri, Somsubhro T1 - Infrared thermography as an inspection tool for wind turbine rotor blades N2 - This presentation explores the use of infrared thermography as an inspection tool for wind turbine rotor blades. It highlights the implementation of ground-based thermographic techniques, demonstrates what can be achieved through such inspections, and presents a case study conducted in partnership with Statkraft. Particular attention is given to the discussion of thermal contrast and how it can be obtained for effective defect detection. To assess the potential and limitations of thermography, results from controlled climate chamber experiments and complementary simulations are also presented. T2 - IQPC Wind Week 2025 CY - Bremen, Germany DA - 26.08.2025 KW - Thermography KW - Thermografie KW - Wind turbine rotor blades KW - Windenergie anlage rotorblätte KW - Damage detection PY - 2025 AN - OPUS4-64056 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - D'Accardi, E. A1 - Krankenhagen, Rainer A1 - Ulbricht, Alexander A1 - Pelkner, Matthias A1 - Pohl, Rainer A1 - Palumbo, D. A1 - Galietti, U. T1 - Capability to detect and localize typical defects of laser powder bed fusion (L‑PBF) process: an experimental investigation with different non‑destructive techniques N2 - Additive manufacturing (AM) technologies, generally called 3D printing, are widely used because their use provides a high added value in manufacturing complex-shaped components and objects. Defects may occur within the components at different time of manufacturing, and in this regard, non-destructive techniques (NDT) represent a key tool for the quality control of AM components in many industrial fields, such as aerospace, oil and gas, and power industries. In this work, the capability of active thermography and eddy current techniques to detect real imposed defects that are representative of the laser powder bed fusion process has been investigated. A 3D complex shape of defects was revealed by a μCT investigation used as reference results for the other NDT methods. The study was focused on two different types of defects: porosities generated in keyhole mode as well as in lack of fusion mode. Different thermographic and eddy current measurements were carried out on AM samples, providing the capability to detect volumetric irregularly shaped defects using non-destructive methods. KW - Additive Manufacturing KW - Defect detection KW - Thermography KW - Eddy-current testing KW - Micro-computed tomography PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-546680 DO - https://doi.org/10.1007/s40964-022-00297-4 SN - 2363-9512 VL - 7 IS - 6 SP - 1239 EP - 1256 PB - Springer AN - OPUS4-54668 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 T1 - Local porosity prediction in metal powder bed fusion using in-situ thermography: A comparative study of machine learning techniques N2 - The formation of flaws such as internal porosity in parts produced by Metal-based Powder Bed Fusion with Laser Beam (PBF-LB/M) significantly hinders its broader industrial application, as porosity can potentially lead to part failure. Addressing this issue, this study explores the efficacy of in-situ thermography, particularly short-wave infrared thermography, for detecting and predicting porosity during manufacturing. This technique is capable of monitoring the part’s thermal history which is closely connected to the flaw formation process. Recent advancements in Machine Learning (ML) have been increasingly leveraged for porosity prediction in PBF-LB/M. However, previous research primarily focused on global rather than localized porosity prediction which simplified the complex prediction task. Thereby, the opportunity to correlate the predicted flaw position with expected part strain to judge the severity of the flaw for part performance is neglected. This study aims to bridge this gap by studying the potential of SWIR thermography for predicting local porosity levels using regression models. The models are trained on data from two identical HAYNES®282® specimens. We compare the effectiveness of feature-based and raw data-based models in predicting different porosity types and examine the importance of input data in porosity prediction. We show that models trained on SWIR thermogram data can identify systematic trends in local flaw formation. This is demonstrated for forced flaw formation using process parameter shifts and, moreover, for randomly formed flaws in the specimen bulk. Furthermore, we identify features of high importance for the prediction of lack-of-fusion and keyhole porosity from SWIR monitoring data. KW - PBF-LB/M KW - In situ monitoring KW - Thermography KW - Additive Manufacturing KW - Process monitoring KW - Porosity prediction KW - Machine Learning KW - Feature extraction PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-621798 DO - https://doi.org/10.1016/j.addma.2024.104502 SN - 2214-7810 VL - 95 SP - 1 EP - 17 PB - Elsevier B.V. AN - OPUS4-62179 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Altenburg, Simon A1 - Straße, Anne A1 - Gumenyuk, Andrey A1 - Maierhofer, Christiane T1 - In-situ monitoring of a laser metal deposition (LMD) process: comparison of MWIR, SWIR and high-speed NIR thermography N2 - Additive manufacturing offers a range of novel applications. However, the manufacturing process is complex and the production of almost defect-free parts with high reliability and durability is still a challenge. Thermography is a valuable tool for process surveillance, especially in metal additive manufacturing processes. The high process temperatures allow one to use cameras usually operating in the visible spectral range. Here, we compare the results of measurements during the manufacturing process of a commercial laser metal deposition setup using a mid wavelength infrared camera with those from a short wavelength infrared camera and those from a visual spectrum high-speed camera with band pass filter in the near infrared range. KW - Additive Manufacturing KW - Process monitoring KW - Thermography KW - LMD KW - Metal KW - MWIR KW - SWIR KW - NIR PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-516318 DO - https://doi.org/10.1080/17686733.2020.1829889 VL - 19 IS - 2 SP - 97 EP - 114 PB - Taylor & Francis Group CY - London, UK AN - OPUS4-51631 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CHAP A1 - Maierhofer, Christiane A1 - Altenburg, Simon A1 - Scheuschner, Nils ED - Meyendorf, N. ED - Ida, N. ED - Singh, R. ED - Vrana, J. T1 - In Situ Real-Time Monitoring Versus Post NDE for Quality Assurance of Additively Manufactured Metal Parts N2 - In this chapter, the current state-of-the-art of in situ monitoring and in situ NDE methods in additive manufacturing is summarized. The focus is set on methods, which are suitable for making statements about the quality and usability of a component currently being manufactured. This includes methods which can be used to determine state properties like temperature or density, other physical properties like electrical or thermal conductivity, the microstructure, the chemical composition, the actual geometry, or which enable the direct detection of defects like cracks, voids, delaminations, or inclusions. Thus, optical, thermographic, acoustic, and electromagnetic methods, as well as methods being suitable for investigating particle and fume emission are presented. The requirements of in situ monitoring methods with a focus on thermographic methods are discussed by considering different additive manufacturing processes like laser powder bed fusion (PBF-LB/M) and direct energy deposition (DED-LB/M). Examples of the successful implementation and applications of such monitoring methods at BAM are given. The in situ monitoring and NDE methods are compared against post-process NDE methods. The advantages and challenges of in situ methods concerning real-time data analysis and the application of AI algorithms are addressed and discussed. KW - Additive manufacturing KW - In situ monitoring KW - In situ NDE KW - Post NDE KW - Thermography KW - Laser powder bed fusion KW - Direct energy deposition PY - 2021 SN - 978-3-030-48200-8 DO - https://doi.org/10.1007/978-3-030-48200-8_51-1 SP - 1 EP - 37 PB - Springer Nature Switzerland AG CY - Cham, Switzerland ET - 1 AN - OPUS4-52824 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Scheuschner, Nils T1 - In-situ Monitoring der Additiven Fertigung von Metallen im LPA Prozess mittels Optischer Emissionsspektrometrie (OES) und Thermografie (TT) N2 - Einer der aussichtsreichsten Ansätze, die Qualität und Sicherheit der gefertigten Teile in der metallbasierten additiven Fertigung (AM) zu erhöhen und die Notwendigkeit aufwändiger und zeitintensiver, zerstörender oder zerstörungsfreier Prüfungen (ZfP) nach der Fertigung zu verringern, liegt in dem Einsatz von in-situ Prozessüberwachungstechniken. Viele wichtige Prozessgrößen bei der additiven Fertigung sind thermischer Natur, wie z.B. die Temperatur des Schmelzbades. Aufgrund der Zugänglichkeit zum Werkstück während des Bauprozesses bieten sich optische Verfahren zur Temperaturbestimmung an. Für die Thermografie und Optische Emissionsspektrometrie im IR-Bereich, welche für die in-situ Anwendung prinzipiell als geeignet angesehen werden können, gibt es allerdings noch wenig konkrete praktische Umsetzungen, da die Möglichkeiten und individuellen Grenzen dieser Methoden, angewendet auf AM, noch nicht ausreichend erforscht sind. Aus diesem Grund verfolgt die BAM mit dem Projekt „Process Monitoring of AM“ (ProMoAM) im Themenfeld Material das Ziel, Verfahren des Prozessmonitorings zur in-situ Bewertung der Qualität additiv gefertigter Metallbauteile weiterzuentwickeln. Im Beitrag wird der Fokus auf eine Versuchsserie gelegt, bei der Aufbau von Probekörpern aus dem austenitischen Edelstahl 316L mittels Laser-Pulver-Auftragschweißen (LPA) durch od. mit Hilfe von IR-Spektrometrie und Thermografie in-situ überwacht wurde. Hierbei stellen u.a. die hohe Bandbreite der zu messenden Temperaturen, die Bestimmung der Emissivität und ihre Änderung bei Phasenübergängen des Metalls große experimentelle Herausforderungen dar, wobei jede Methode individuelle Vor- und Nachteile aufweist, welche verglichen werden. T2 - DGZfP-Jahrestagung 2021 CY - Online meeting DA - 10.05.2021 KW - Additive Manufacturing KW - Thermography KW - Direct Energy Deposition KW - Additive Fertigung KW - Thermografie KW - Laserauftragschweißen PY - 2021 AN - OPUS4-52744 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Scheuschner, Nils A1 - Altenburg, Simon A1 - Pignatelli, Giuseppe A1 - Maierhofer, Christiane A1 - Straße, Anne A1 - Gornushkin, Igor B. A1 - Gumenyuk, Andrey T1 - Vergleich der Messungen der Schmelzbadtemperatur bei der Additiven Fertigung von Metallen mittels IR-Spektroskopie und Thermografie T1 - Comparison of measurements of the melt pool temperature during the additive production of metals by means of IR spectroscopy and thermography N2 - Im Rahmen des Themenfeldprojektes „Process Monitoring of AM“ (ProMoAM) evaluiert die Bundesanstalt für Materialforschung und -Prüfung (BAM) gegenwärtig die Anwendbarkeit verschiedenster ZfP-Verfahren zur in-situ Prozessüberwachung in der additiven Fertigung (AM) von Metallen in Hinblick auf die Qualitätssicherung. Einige der wichtigsten Messgrößen sind hierbei die Temperatur des Schmelzbades und die Abkühlrate, welche starken Einfluss auf das Gefüge und die Eigenspannung haben. Aufgrund der Zugänglichkeit zum Werkstück während des Bauprozesses bieten sich optische Verfahren zu Temperaturbestimmung an. Hierbei stellen jedoch u. a. die hohe Bandbreite der zu messenden Temperaturen, die Bestimmung der Emissivität und ihre Änderung bei Phasenübergängen der verwendeten Legierung große experimentelle Herausforderungen dar. Eine weitere Herausforderung stellt für die IR-Spektroskopie die Absorption durch das Schutzgas und weitere optische Elemente dar. Um diese auch in einem industriellen Umfeld kompensieren zu können, wurde eine Methode entwickelt, die das gemessene Spektrum bei der Verfestigung des Werkstoffes als Referenz nutzt. In diesem Beitrag wird die Anwendung dieser Methode für die IR-Spektrometrie als auch Thermografische Messungen beim Laser-Pulver-Auftragschweißen von 316L gezeigt, wobei beide Methoden weiterhin in Hinblick auf ihre individuellen Vor- und Nachteile miteinander verglichen werden. N2 - Within the topic area project “Process Monitoring of AM” (ProMoAM) the Federal Institute for Materials Research and Testing is currently evaluating the applicability of various NDT methods for in-situ process Monitoring in the additive manufacturing (AM) of metals with regard to quality assurance. Two of the most important variables to measure are the temperature of the molten pool and the cooling rate, which have a strong influence on the microstructure and the residual stress. Due to the accessibility of the workpiece during the construction process, optical methods for temperature determination are suitable. However, the wide range of temperatures to be measured, the determination of emissivity and its change during phase transitions of the alloy pose great experimental challenges. Another challenge for IR spectroscopy is the absorption by the inert gas and other optical elements. In order to be able to compensate for this in an industrial environment, a method was developed which uses the measured spectrum as a reference when the material is solidified. This paper shows the application of this method for IR spectrometry as well as thermographic measurements during laser powder cladding of 316L. Furthermore both methods are compared with respect to their individual Advantages and disadvantages. KW - Laser-Pulver-Auftragschweißen KW - Thermografie KW - Direct Energy Deposition KW - IR-Spektroskopie KW - Additive Fertigung KW - Laser metal deposition KW - Thermography KW - IR-spectroscopy KW - Additive manufacturing PY - 2021 DO - https://doi.org/10.1515/teme-2021-0056 VL - 88 IS - 10 SP - 626 EP - 632 PB - De Gruyter CY - Oldenburg AN - OPUS4-52987 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Altenburg, Simon T1 - Improving additive manufacturing technologies by in-situ monitoring: Thermography N2 - Additive manufacturing of metals gains increasing relevance in the industrial field for part production. However, especially for safety relevant applications, a suitable quality assurance is needed. A time and cost efficient route to achieve this goal is in-situ monitoring of the build process. Here, the BAM project ProMoAM (Process monitoring in additive manufacturing) is briefly introduced and recent advances of BAM in the field of in-situ monitoring of the L-PBF and the LMD process using thermography are presented. T2 - Anwenderkonferenz Infratec GmbH CY - Online meeting DA - 04.11.2021 KW - Additive Manufacturing KW - Process monitoring KW - Thermography PY - 2021 AN - OPUS4-54026 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Maierhofer, Christiane T1 - Summary of in-situ monitoring in additive manufacturing - ProMoAM N2 - The quality of additively manufactured components is significantly influenced by the process parameters used during production. Thus, sensors and measuring systems are already commercially available for process monitoring, at least in metal-based additive manufacturing. However, it is not yet possible to detect defects and inhomogeneities directly or indirectly during the building process. The aim of the project ProMoAM is to develop spectroscopic and non-destructive testing methods for the in-situ evaluation of the quality of additively manufactured metal components in laser- or arc-based AM processes. In addition to passive and active methods of thermography, this includes optical tomography, optical emission spectroscopy, eddy current testing, laminography (radiography), X-ray backscattering, particle emission spectroscopy and photoacoustic methods. T2 - 2nd Workshop on In-situ Monitoring and Microstructure Development in Additive Manufactured Alloys CY - Online meeting DA - 19.04.2021 KW - In-situ monitoring KW - Additive manufacturing KW - Metals KW - Thermography PY - 2021 AN - OPUS4-52539 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Oster, Simon T1 - Multispectral in-situ monitoring of a L-PBF manufacturing process using three thermographic camera systems N2 - The manufacturing of metal parts for the use in safety-relevant applications by Laser Powder Bed Fusion (L-PBF) demands a quality assurance of both part and process. Thermography is a nondestructive testing method that allows the in-situ determination of the thermal history of the produced part which is connected to the mechanical properties and the formation of defects [1]. A wide range of commercial thermographic camera systems working in different spectral ranges is available on the market. The understanding of the applicability of these cameras for qualitative and quantitative in-situ measurements in L-PBF is of vital importance [2]. In this study, the building process of a cylindrical specimen (Inconel 718) is monitored by three camera systems simultaniously. These camera systems are sensitive in various spectral bandwidths providing information in different temperature ranges. The performance of each camera system is explored in the context of the extraction of image features for the detection of defects. It is shown that the high temporal and thermal process dynamics are limiting factors on this matter. The combination of different spectral camera systems promises the potential of an improved defect detection by data fusion. T2 - LASER SYMPOSIUM & ISAM 2021 CY - Online meeting DA - 07.12.2021 KW - Laser Powder Bed Fusion KW - Thermography KW - In-situ Monitoring KW - Defect detection PY - 2021 AN - OPUS4-54141 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Scheuschner, Nils T1 - In-situ temperature measurements of the LMD process by IR-spectroscopy and Thermography N2 - Temperature measurements of the LMD process by IR-spectroscopy and Thermography are presenet and compared. T2 - 2st Workshop on In-situ Monitoring and Microstructure Development in Additive Manufacturing CY - Online meeting DA - 19.04.2021 KW - Additive Manufacturing KW - Thermography PY - 2021 AN - OPUS4-52565 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Altenburg, Simon T1 - Towards hyperspectral in-situ temperature measurement in metal additive manufacturing N2 - The industrial use of additive manufacturing for the production of metallic parts with high geometrical complexity and lot sizes close to one is rapidly increasing as a result of mass individualisation and applied safety relevant constructions. However, due to the high complexity of the production process, it is not yet fully understood and controlled, especially for changing (lot size one) part geometries. Due to the thermal nature of the Laser-powder bed fusion (L-PBF) process – where parts are built up layer-wise by melting metal powder via laser - the properties of the produced part are strongly governed by its thermal history. Thus, a promising route for process monitoring is the use of thermography. However, the reconstruction of temperature information from thermographic data relies on the knowledge of the surface emissivity at each position on the part. Since the emissivity is strongly changing during the process due to phase changes, great temperature gradients, possible oxidation, and other potential influencing factors, the extraction of real temperature data from thermographic images is challenging. While the temperature development in and around the melt pool, where melting and solidification occur is most important for the development of the part properties. Also, the emissivity changes are most severe in this area, rendering the temperature deduction most challenging. A possible route to overcome the entanglement of temperature and emissivity in the thermal radiation is the use of hyperspectral imaging in combination with temperature emissivity separation (TES) algorithms. As a first step towards the combined temperature and emissivity determination in the L-PBF process, here, we use a hyperspectral line camera system operating in the short-wave infrared region (0.9 µm to 1.7 µm) to measure the spectral radiance emitted. In this setup, the melt pool of the L-PBF process migrates through the camera’s 1D field of view, so that the radiation intensities are recorded simultaneously for multiple different wavelength ranges in a spatially resolved manner. At sufficiently high acquisition frame rate, an effective melt pool image can be reconstructed. Using the grey body approximation (emissivity is independent of the wavelength), a first, simple TES is performed, and the resulting emissivity and temperature values are compared to literature values. Subsequent work will include reference measurements of the spectral emissivity in different states allowing its analytical parametrisation as well as the adaption and optimisation of the TES algorithms. An illustration of the proposed method is shown in Fig.1. The investigated method will allow to gain a deeper understanding of the L-PBF process, e.g., by quantitative validation of simulation results. Additionally, the results will provide a data basis for the development of less complex and cheaper sensor technologies for L-PBF in-process monitoring (or for related process), e.g., by using machine learning. T2 - 21st International Conference on Photoacoustic and Photothermal Phenomena CY - Bled, Slovenia DA - 19.06.2022 KW - Thermography KW - Additive manufacturing KW - L-PBF KW - Hyperspectral PY - 2022 AN - OPUS4-55152 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Oster, Simon T1 - Porosity prediction in metal based additive manufacturing utilizing in situ thermography N2 - Quality assessment of components produced by metal based additive manufacturing (AM) technologies such as laser powder bed fusion is rising in importance due to the increased use of AM in industrial production. Here, the presence of internal porosity was identified as a limiting factor for the final component quality. The utilization of thermography as an in-situ monitoring technique allows the determination of the part’s thermal history which was found to be connected to the porosity formation [1]. Combining the local thermal information derived from thermography with the porosity information obtained by x-ray micro computed tomography, machine learning algorithms can be utilized to predict the porosity distribution in the part. In this study, a first approach for the prediction of keyhole porosity in a cylindric specimen from AISI 316L stainless steel is presented. It is based on data augmentation using the “SmoteR” algorithm [2] to cure the dataset imbalance and a 1-dimensional convolutional neural network. [1] C.S. Lough et al., Local prediction of Laser Powder Bed Fusion porosity by short-wave infrared thermal feature porosity probability maps. Journal of Materials Processing Technology, 302, p. 117473 (2022) https://dx.doi.org/10.1016/j.imatprotec.2021.117473 [2] L. Torgo et al., SMOTE for Regression. Progress in Artificial Intelligence, Chapter 33, p. 378-289 (2013) https://dx.doi.org/10.1007/978-3-642-40669-0_33 T2 - KI-Tag Arbeitskreis Chemometrik & Qualitätssicherung - Chemometrics meets Artificial Intelligence CY - Berlin, Germany DA - 01.04.2022 KW - Laser Powder Bed Fusion KW - Thermography KW - Defect Prediction KW - Convolutional Neural Networks PY - 2022 AN - OPUS4-54621 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 - CONF A1 - Oster, 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 - Becker, Tina T1 - Multi measurand in-situ monitoring of the laser powder bed fusion process by means of multispectral optical tomography N2 - Laser Powder Bed Fusion (L-PBF), as one of the most promising production process in the field of metal additive manufacturing, enables traditional constructive solutions to be rethought and the manufacturing of optimized components according to the "form follows function" principle. The most significant obstacle for a broad industrial application of the L-PBF process is the inadequate quality assurance during the manufacturing process so far, leading to high production costs. Although several mainly camera based commercial in-process monitoring systems are already available, a deep understanding of the interpretation of the monitored data and correlation with actual defects is still lacking. One reason for this is the reduction of the complex process signature to just one measurement value. The focus of this contribution is the presentation of the multispectral optical tomography as alternative to single measurand in-situ monitoring systems. The potential of this approach is hereby shown on L-PBF printed samples with induced process instabilities. Beyond that, an in-house developed L-PBF printer for further testing of multi-sensor in-situ monitoring systems is presented. T2 - ICAM2021 CY - Online meeting DA - 01.11.2021 KW - In-situ monitoring KW - L-PBF KW - Optical tomography KW - 3d printing KW - Thermography PY - 2021 AN - OPUS4-54388 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Chaudhuri, Somsubhro T1 - WTB-IRT: Modelling and Measurement of Thermal Contrast in wind turbine rotor blades (WTBs) N2 - To understand the results that can be obtained with thermography of operational wind turbine blades, a climate chamber based experiment was performed. The results of these experiments were additionally modelled using COMSOL Multiphysics. The results have been presented at the conference. T2 - Advanced Infrared Technology and Applications (AITA) 2025 CY - Kobe, Japan DA - 15.09.2025 KW - Thermography KW - Thermografie KW - Wind turbine rotor blades KW - Windenergie Anlage Rotorblätter KW - FEA PY - 2025 AN - OPUS4-64222 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Mohr, Gunther A1 - Sommer, Konstantin A1 - Knobloch, Tim A1 - Altenburg, Simon A1 - Recknagel, Sebastian A1 - Bettge, Dirk A1 - Hilgenberg, Kai T1 - Process Induced Preheating in Laser Powder Bed Fusion Monitored by Thermography and Its Influence on the Microstructure of 316L Stainless Steel Parts N2 - Undetected and undesired microstructural variations in components produced by laser powder bed fusion are a major challenge, especially for safety-critical components. In this study, an in-depth analysis of the microstructural features of 316L specimens produced by laser powder bed fusion at different levels of volumetric energy density and different levels of inter layer time is reported. The study has been conducted on specimens with an application relevant build height (>100 mm). Furthermore, the evolution of the intrinsic preheating temperature during the build-up of specimens was monitored using a thermographic in-situ monitoring set-up. By applying recently determined emissivity values of 316L powder layers, real temperatures could be quantified. Heat accumulation led to preheating temperatures of up to about 600 °C. Significant differences in the preheating temperatures were discussed with respect to the individual process parameter combinations, including the build height. A strong effect of the inter layer time on the heat accumulation was observed. A shorter inter layer time resulted in an increase of the preheating temperature by more than a factor of 2 in the upper part of the specimens compared to longer inter layer times. This, in turn, resulted in heterogeneity of the microstructure and differences in material properties within individual specimens. The resulting differences in the microstructure were analyzed using electron back scatter diffraction and scanning electron microscopy. Results from chemical analysis as well as electron back scatter diffraction measurements indicated stable conditions in terms of chemical alloy composition and austenite phase content for the used set of parameter combinations. However, an increase of the average grain size by more than a factor of 2.5 could be revealed within individual specimens. Additionally, differences in feature size of the solidification cellular substructure were examined and a trend of increasing cell sizes was observed. This trend was attributed to differences in solidification rate and thermal gradients induced by differences in scanning velocity and preheating temperature. A change of the thermal history due to intrinsic preheating could be identified as the main cause of this heterogeneity. It was induced by critical combinations of the energy input and differences in heat transfer conditions by variations of the inter layer time. The microstructural variations were directly correlated to differences in hardness. KW - Additive manufacturing KW - Laser powder bed fusion KW - In-situ process monitoring KW - Thermography KW - Heat accumulation KW - Inter layer time KW - Cellular substructure PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-529240 DO - https://doi.org/10.3390/met11071063 VL - 11 IS - 7 SP - 1063 PB - MDPI CY - Basel, Schweiz AN - OPUS4-52924 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Lay, Vera T1 - Material research and multi-sensory monitoring for concrete sealing structure in rock salt underground repositories N2 - Within the project SealWasteSafe, we advance construction materials and non-destructive monitoring concepts of sealing structures applied for underground disposal of nuclear waste. As these engineered barriers have high demands regarding structural integrity, an innovative alkali-activated material (AAM) that is highly suitable for the application in salt as a host rock is improved and tested on two laboratory scales. This AAM has a low heat evolution due to the reaction mechanism in comparison to common salt concretes based on Portland cement or magnesium oxychloride binders. Hence, crack formation due to thermally induced stress during the hardening process is reduced. After successful laboratory tests with small specimens (height ~5 cm), comparably manufactured large cubic (edge length 70 cm) and cylindrical specimens (height 120 cm, diameter 40 cm) are equipped with sensing technologies to demonstrate the sensors´ technical capabilities. A comprehensive multi-sensory monitoring scheme is developed and investigated to characterize and compare the different material behaviour during the setting and hardening process of two materials: (1) the newly developed AAM-based mortars with salt aggregate, and (2) a blended Portland cement-based salt concrete as reference. The analysed parameters include temperature and humidity of the material, acoustic emissions, and strain variations recorded by fiber optic cables. Passive sensor systems based on radiofrequency identification technology (RFID) embedded in the concrete provide an interface for the wireless readout of various sensors. In parallel to the embedded RFID sensors, conventional cabled systems to read out the temperature and humidity measurements are installed for comparison. Additionally, a detailed inspection of the two large cubic specimens after a monitoring period of more than six months has been undertaken. Active thermography and ultrasonic echo measurements are used to reveal potentially occurring inner cracks from the surface. To verify the non-invasive results, a core sample (diameter 2 cm) was extracted from each of the investigated cubic specimens and analysed in detail with X-ray computed tomography. Furthermore, ultrasonic methods are used for quality assurance to detect obstacles, cracks, and delamination at in-situ scale sealing structures. Experimental layout and applied imaging techniques are optimised to enhance the image quality for measurements from the front side of the engineered barrier. To characterize the inside of the test sealing structure and to improve the detection of potentially existing cracks, an ultrasonic borehole probe using the phased array technique is developed. First analyses at a half-spherical specimen coincide with modelling results and prove the reliability of the directional response caused by the phased array technique of the newly constructed ultrasonic borehole probe. Overall, the project SealWasteSafe helps to characterize construction materials and improves multi-sensory monitoring concepts and ultrasonic equipment for the sake of quality assurance. Particularly for salt as a host rock, this will help to design safe sealing structures for nuclear waste disposal. T2 - EGU General Assembly 2023 CY - Vienna, Austria DA - 23.04.2023 KW - SealWasteSafe KW - Engineered barriers KW - Salt concrete KW - Quality assurance KW - Ultrasound KW - CT KW - Thermography PY - 2023 DO - https://doi.org/10.5194/egusphere-egu23-11582 AN - OPUS4-57500 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Chaudhuri, Somsubhro A1 - Purayil, Sruthi Krishna Kunji A1 - Kruse, Julius A1 - Madia, Mauro A1 - Nielsen, Sören T1 - Thermography-Assisted Mechanical Testing of Cold-Spray (AM) Repair N2 - Cold Spray Additive Manufacturing (CSAM) is a solid-state process that is being increasingly used for structural repairs in aerospace and energy sectors. It enables the deposition of dense material at low temperatures by accelerating metal particles to supersonic velocities, thereby reducing thermal distortion. However, the structural integrity of CSAM repairs—particularly at the interface between the deposited layer and the substrate—remains a critical concern. Various post-treatments and characterization methods have been explored to optimize performance. While X-ray Computed Tomography (XCT) is effective for sub-surface inspection, it cannot be applied in situ during mechanical testing. Digital Image Correlation (DIC), a surface-based method, also lacks sub-surface sensitivity. To address this, Infrared Thermography (IRT) was employed alongside DIC during the tensile and fatigue testing of aluminum CSAM-repaired specimens. A cooled IRT camera operating at 200 FPS captured thermal data, with lock-in processing subsequently applied in post-processing. IRT successfully detected early interfacial damage and enabled the tracking of crack propagation, which was later confirmed through fracture surface analysis. This extended abstract presents findings from fatigue tests using IRT. T2 - 18th International Workshop on Advanced Infrared Technology and Applications (AITA 2025) CY - Kobe, Japan DA - 15.09.2025 KW - Thermography KW - Thermografie KW - Thermal stress analysis KW - Cold spray KW - Damage detection PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-642299 DO - https://doi.org/10.3390/proceedings2025129018 VL - 129 IS - 1 SP - 1 EP - 5 PB - MDPI CY - Basel AN - OPUS4-64229 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Chaudhuri, Somsubhro A1 - Krankenhagen, Rainer A1 - Lapšanská, Ivana A1 - Stamm, Michael T1 - WTB-IRT: Modelling and Measurement of Thermal Contrast in Wind Turbine Rotor Blades (WTBs) N2 - The rapid growth of wind energy infrastructure over the past two to three decades has led to an urgent need for advanced non-destructive testing (NDT) methods—both for newly installed wind turbine blades (WTBs) and for ageing components nearing the end of their service life. Among emerging techniques, passive infrared thermography (IRT) offers a promising solution by enabling contactless, time-efficient inspection based on naturally occurring thermal variations. The effectiveness of passive IRT depends on the presence of sufficient thermal contrast to distinguish surface features, subsurface structures, and defects. To better understand the possibility of obtaining such contrast in composite structures such as WTBs, a controlled study was carried out on a blade section exposed to programmed temperature transients in a climate chamber. Infrared measurements were recorded, and the thermal behaviour of the specimen was simulated using finite element models (FEM) in COMSOL Multiphysics 6.3. Although direct validation is limited by measurement uncertainties and transient effects, the comparison provides insight into the capabilities and limitations of FEM in replicating real-world thermal behaviour. This paper focuses specifically on the challenges related to the modelling approach. T2 - 18th International Workshop on Advanced Infrared Technology and Applications (AITA 2025) CY - Kobe, Japan DA - 15.09.2025 KW - Thermography KW - Wind energy KW - Wind turbine rotor blades KW - Windenergie Anlage Rotorblätter KW - FEA PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-642308 DO - https://doi.org/10.3390/proceedings2025129015 VL - 129 IS - 1 SP - 1 EP - 5 PB - MDPI CY - Basel AN - OPUS4-64230 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Chaudhuri, Somsubhro T1 - Thermography-Assisted Mechanical Testing of Cold-Spray (AM) Repair N2 - Cold Spray Additive Manufacturing (CSAM) is a solid-state process that is being increasingly used for structural repairs in aerospace and energy sectors. It enables the deposition of dense material at low temperatures by accelerating metal particles to supersonic velocities, thereby reducing thermal distortion. However, the structural integrity of CSAM repairs—particularly at the interface between the deposited layer and the substrate—remains a critical concern. Various post-treatments and characterization methods have been explored to optimize performance. While X-ray Computed Tomography (XCT) is effective for sub-surface inspection, it cannot be applied in situ during mechanical testing. Digital Image Correlation (DIC), a surface-based method, also lacks sub-surface sensitivity. To address this, Infrared Thermography (IRT) was employed alongside DIC during the tensile and fatigue testing of aluminum CSAM-repaired specimens. A cooled IRT camera operating at 200 FPS captured thermal data, with lock-in processing subsequently applied in post-processing. IRT successfully detected early interfacial damage and enabled the tracking of crack propagation, which was later confirmed through fracture surface analysis. T2 - Advanced Infrared Technology and Applications (AITA) 2025 CY - Kobe, Japan DA - 15.09.2025 KW - Thermography KW - Thermografie KW - Thermal stress analysis KW - Cold spray KW - Damage detection PY - 2025 AN - OPUS4-64227 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Mohr, Gunther A1 - Scheuschner, Nils A1 - Hilgenberg, Kai T1 - In situ heat accumulation by geometrical features obstructing heat flux and by reduced inter layer times in laser powder bed fusion of AISI 316L stainless steel N2 - Material qualification for laser powder bed fusion (L-PBF) processes are often based on results derived from additively manufactured (AM) bulk material or small density cubes, although it is well known that the part geometry has a tremendous influence on the heat flux and, therefore, on the thermal history of an AM component. This study shows experimentally the effect of simple geometrical obstructions to the heat flux on cooling behavior and solidification conditions of 316L stainless steel processed by L-PBF. Additionally, it respects two distinct inter layer times (ILT) as well as the build height of the parts. The cooling behavior of the parts is in-situ traced by infrared (IR) thermography during the built-up. The IR signals reveal significant differences in cooling conditions, which are correlated to differences in melt pool geometries. The acquired data and results can be used for validation of computational models and improvements of quality assurance. KW - Selective laser beam melting KW - Thermography KW - Melt pool depth KW - Inter layer time KW - Ppreheating temperature KW - Additive Manufacturing PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-512549 DO - https://doi.org/10.1016/j.procir.2020.09.030 VL - 94 SP - 155 EP - 160 PB - Elsevier B.V. AN - OPUS4-51254 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 - CONF A1 - D'Accardi, E. T1 - Capability of active thermography to detect and localize pores in Metal Additive Manufacturing materials N2 - A powerful tool to understand, demonstrate and explain the limits of the pulsed technique in terms of detectability and localizability of AM keyhole pores has been assessed by comparing the active thermographic approach (both experimental and FEM simulations) to Computed Tomography results; ✓ µCT results demonstrate that the intended defect geometry is not achieved; indeed a network of voids (microdefects consisting of small sharp-edged hollows with a complicated, almost fractal, inner surface) was found; ✓ both Exp-PT and FEM results explains clearly why no indication of defect related to the thermal contrasts could be found during the investigation of an uncoated surface. However, the application of further data evaluations focusing on the thermal behavior and emissivity evaluation (PPT post data processing) enable the detection of some defects; ✓ coating facilitates a closer inspection of inner defects, but inhomogeneities of the coating could impair the spatial resolution and lead to the emergence of hotspots (the FEM simulation reached its limit with this extreme geometry where a 25 µm thin disc is considered at a 1 cm thick specimen in millisecond time resolution); ✓ both Exp-PT and FEM results allow the conclusion that very short pulses of 200 ms or shorter should be sufficient to detect these defects below, but near the surface; besides a short duration of the thermal phenomenon it should be emphasized, about 0.04 s (high frame rate camera); T2 - Convegno AIAS 2020 CY - Online meeting DA - 02.09.2020 KW - Additive Manufacturing KW - Laser powderbed fusion KW - Pores KW - Thermography PY - 2020 AN - OPUS4-51922 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Breese, Philipp Peter 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). T2 - 13th CIRP Conference on Photonic Technologies - LANE 2024 CY - Fürth, Germany DA - 15.09.2024 KW - Additive Manufacturing KW - Laser Powder Bed Fusion KW - Nondestructive Testing KW - Defect Detection KW - Thermography PY - 2024 AN - OPUS4-61285 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Becker, Tina A1 - Breese, Philipp Peter A1 - Metz, Christian A1 - Altenburg, Simon T1 - In-situ monitoring for PBF-LB/M processes: Does multispectral optical tomography add value in recognizing process deviations? N2 - Laser powder bed fusion of metallic components (PBF-LB/M) is gaining acceptance in industry. However, the high costs and lengthy qualification processes required for printed components create the need for more effective in-situ monitoring and testing methods. This article proposes multispectral Optical Tomography (OT) as a new approach for monitoring the PBF-LB/M process. Compared to other methods, OT is a low-cost process monitoring method that uses long-time exposure imaging to observe the build process. However, it lacks time resolution compared to expensive thermographic sensor systems. Monochromatic OT (1C-OT) is already commercially available and observes the building process layer-wise using a single wavelength window in the NIR range. Multispectral OT (nC-OT) utilizes a similar setup but can measure multiple wavelength ranges per location simultaneously. By comparing the classical 1C-OT and nC-OT approaches, this article examines the advantages of nC-OT (two channel OT and RGB-OT) in reducing the false positive rate for process deviations and approximating maximum temperatures for a better comparison between different build processes and materials. This could ultimately reduce costs and time for part qualification. The main goal of this contribution is to assess the advantages of nC-OT compared to 1C-OT for in-situ process monitoring of PBF-LB/M. T2 - Nolamp 2023 CY - Turku, Finland DA - 22.08.2023 KW - Thermography KW - Process Monitoring KW - Additive manufacturing KW - BPF-LB/M KW - In-situ PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-592498 DO - https://doi.org/10.1088/1757-899X/1296/1/012008 VL - 1296 SP - 1 EP - 11 PB - IOP Publishing CY - Bristol, UK AN - OPUS4-59249 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 - Oster, Simon 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 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 - TY - CONF A1 - Breese, Philipp Peter A1 - Becker, Tina A1 - Oster, Simon A1 - Metz, C. A1 - Altenburg, Simon T1 - In-situ defect detection for laser powder bed fusion with active laser thermography N2 - Defects are still common in metal components built with Additive Manufacturing (AM). Process monitoring methods for laser powder bed fusion (PBF-LB/M) are used in industry, but relationships between monitoring data and defect formation are not fully understood yet. Additionally, defects and deformations may develop with a time delay to the laser energy input. Thus, currently, the component quality is only determinable after the finished process. Here, active laser thermography, a nondestructive testing method, is adapted to PBF-LB/M, using the defocused process laser as heat source. The testing can be performed layer by layer throughout the manufacturing process. We study our proposed testing method along experiments carried out on a custom research PBF-LB/M machine using infrared (IR) cameras. Our work enables a shift from post-process testing of components towards in-situ testing during the AM process. The actual component quality is evaluated in the process chamber and defects can be detected between layers. T2 - 2023 International Solid Freeform Fabrication Symposium CY - Austin, TX, USA DA - 14.08.2023 KW - Additive Manufacturing KW - Laser Powder Bed Fusion KW - Nondestructive Testing KW - Thermography KW - Defect Detection PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-606288 DO - https://doi.org/10.26153/tsw/51096 SP - 1978 EP - 1989 PB - University of Texas at Austin CY - Austin, TX, USA AN - OPUS4-60628 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Oster, Simon T1 - A comparison of machine learning approaches for porosity prediction in PBF-LB/M based on thermography N2 - Metal-based additive manufacturing processes are increasingly used in industry to produce complex-shaped components. In this regard, the laser-based Powder Bed Fusion process (PBF-LB/M) is one of the key technologies due to its capability to produce components in high spatial accuracy. The formation of porosity during manufacturing poses a serious risk to the safety of the printed parts. For quality assessment, in-situ monitoring technologies such as thermography can be used to capture the thermal history during production. It was shown that discontinuities within the thermal history can be correlated with the probability of porosity or defect formation. In this context, Machine Learning (ML) algorithms have achieved promising results for the task of porosity prediction based on thermographic in-situ monitoring data. One important technique is the use of thermogram features for porosity prediction that are extracted from the raw data (e.g., features related to the melt pool geometry or spatter generation). However, the reduction from large thermogram data to discrete features holds the risk of losing potentially important thermal information and, thereby, introducing bias in the model. Therefore, we present a raw data-based deep learning approach that uses thermographic image sequences for the prediction of local porosity. The model takes advantage of the self-attention mechanism that considers not only the thermogram information but also its positional context within the sequence. The model is used to predict porosity in the form of a many-to-one regression. It is trained and tested on a dataset retrieved from the manufacturing of HAYNES282 cuboid specimens. The model results are compared against state-of-the-art thermogram feature-based ML models and artificial neural networks. The raw data model outperforms its feature-based counterparts in terms of prediction scores and, therefore, seems to make better use of the information available in the thermogram data. T2 - 4th Symposium on Materials and Additive Manufacturing CY - Berlin, Germany DA - 12.06.2024 KW - PBF-LB/M KW - In situ monitoring KW - Thermography KW - Additive Manufacturing KW - Machine learning KW - Porosity prediction PY - 2024 AN - OPUS4-62472 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Scheuschner, Nils T1 - SAMMIE - Research PBF-LB/M system for the development of in-situ monitoring methods N2 - By allowing economic on-demand manufacturing of highly customized and complex workpieces, metal based additive manufacturing (AM) has the prospect to revolutionize many industrial areas. Since AM is prone to the formation of defects during the building process, a fundamental requirement for AM is to find ways to assure the safety and reliability of the additively manufactured parts to become applicable in most fields. A possible solution for this problem lies in the deployment of various in-situ monitoring techniques. However, only a few of these techniques are commercially available and are not researched to an extent that allows to guarantee the adherence to strict quality and safety standards. Since commercial AM machines are not designed for research applications, they provide only limited access to the build chamber during the process and little control over the exact timing and parameters of the process. Therefore, for our research at BAM, we built a laser powder bed fusion system (PBF-LB/M), called “Sensor-based Additive Manufacturing MachInE” (SAMMIE). It provides a fully open system architecture with flexible accesses to the build camber and full control of the complete process. In this contribution, we show first results using thermographic cameras and optical tomography. The flexibility of SAMMIE allows us to use the multiple cameras either fixed relatively to the build plate or coaxially to the process laser. T2 - 20th World Conference on Non-Destructive Testing (WCNDT) CY - Incheon, South Korea DA - 27.05.2024 KW - PBF-LB/M KW - In situ monitoring KW - Custom machine KW - Additive Manufacturing KW - Thermography PY - 2024 AN - OPUS4-62471 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Mohr, Gunther A1 - Altenburg, Simon A1 - Hilgenberg, Kai T1 - Effects of inter layer time and build height on resulting properties of 316L stainless steel processed by laser powder bed fusion N2 - Laser powder bed fusion (L-PBF) is the most prominent additive manufacturing (AM) technology for metal part production. Among the high number of factors influencing part quality and mechanical properties, the inter layer time (ILT) between iterative melting of volume elements in subsequent layers is almost completely unappreciated in the relevant literature on L-PBF. This study investigates the effect of ILT with respect to build height and under distinct levels of volumetric energy density (VED) using the example of 316L stainless steel. In-situ thermography is used to gather information on cooling conditions during the process, which is followed by an extensive metallographic analysis. Significant effects of ILT and build height on heat accumulation, sub-grain sizes, melt pool geometries and hardness are presented. Furthermore, the rise of defect densities can be attributed to a mutual interplay of build height and ILT. Hence, ILT has been identified as a crucial factor for L-PBF of real part components especially for those with small cross sections. KW - Laser powder bed fusion (L-PBF) KW - Laser beam melting (LBM) KW - Selective laser melting (SLM) KW - Dwell-time KW - Thermography PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-503300 DO - https://doi.org/10.1016/j.addma.2020.101080 SN - 2214-8604 VL - 32 SP - 101080-1 EP - 101080-13 PB - Elsevier CY - Amsterdam AN - OPUS4-50330 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Mohr, Gunther A1 - Altenburg, Simon A1 - Hilgenberg, Kai T1 - On the limitations of small cubes as test coupons for process parameter optimization in laser powder bed fusion of metals N2 - The capability to produce complexly and individually shaped metallic parts is one of the main advantages of the laser powder bed fusion (PBF LB/M) process. Development of material and machine specific process parameters is commonly based on results acquired from small cubic test coupons of about 10 mm edge length. Such cubes are usually used to conduct an optimization of process parameters to produce dense material. The parameters are then taken as the basis for the manufacturing of real part geometries. However, complex geometries go along with complex thermal histories during the manufacturing process, which can significantly differ from thermal conditions prevalent during the production of simply shaped test coupons. This may lead to unexpected and unpredicted local inhomogeneities of the microstructure and defect distribution in the final part and it is a root cause of reservations against the use of additive manufacturing for the production of safety relevant parts. In this study, the influence of changing thermal conditions on the resulting melt pool depth of 316L stainless steel specimens is demonstrated. A variation of thermo-graphically measured intrinsic preheating temperatures was triggered by an alteration of inter layer times and a variation of cross section areas of specimens for three distinct sets of process parameters. Correlations between the preheating temperature, the melt pool depth, and occurring defects were analyzed. The limited expressiveness of the results of small density cubes is revealed throughout the systematic investigation. Finally, a clear recommendation to consider thermal conditions in future process parameter optimizations is given. T2 - Icaleo 2023 CY - Chicago, IL, USA DA - 16.10.2023 KW - Additive manufacturing KW - Heat accumulation KW - Thermal history KW - In situ monitoring KW - Representative specimens KW - Thermography PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-586558 DO - https://doi.org/10.2351/7.0001080 SN - 1042-346X VL - 35 IS - 4 SP - 1 EP - 10 AN - OPUS4-58655 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Altenburg, Simon A1 - Scheuschner, Nils A1 - Maierhofer, Christiane A1 - Mohr, Gunther A1 - Hilgenberg, Kai T1 - Thermography in laser powder bed fusion of metals: time over threshold as feasible feature in thermographic data N2 - Thermography is one on the most promising techniques for in-situ monitoring of metal additive manufacturing processes. Especially in laser powder bed fusion processes, the high process dynamics and the strong focus of the laser beam cause a very complex thermal history within the produced specimens, such as multiple heating cycles within single layer expositions. This complicates data interpretation, e.g., in terms of cooling rates. A quantity that is easily calculated is the time a specific area of the specimen is at a temperature above a chosen threshold value (TOT). Here, we discuss variations occurring in time-over-threshold-maps during manufacturing of an almost defect free cuboid specimen. T2 - 15th Quantitative InfraRed Thermography conference CY - Online meeting DA - 21.09.2020 KW - Additive Manufacturing KW - Process monitoring KW - Thermography KW - L-PBF KW - Time over threshold PY - 2020 DO - https://doi.org/10.21611/qirt.2020.005 SP - 1 EP - 5 PB - QIRT Council CY - Quebec, Canada AN - OPUS4-52014 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Scheuschner, Nils T1 - Infrared Thermography of the DED-LB/M and PBF LB/M processes N2 - Infrared thermography is a technique that allows to measure the temperatures of objects by analyzing the intensity of the thermal emission without the need of direct contact with very high spatial and temporal resolution. As the temperature is a fundamental factor for the additive manufacturing processes of metals, infrared thermography can provide experimental data that can be used for the validation of simulations and improving the understanding of the processes as well as for in-situ process monitoring for nondestructive evaluation (NDE) for quality control. In this talk we will provide an overview over the possibilities of state of the art thermographic in-situ monitoring systems for the DED-LB/M and PBF-LB/M processes and the challenges such as phase transitions and unknown emissivity values in respect to the determination of real temperatures. We define the requirements for different camera systems in various configurations and give examples on the selection of appropriate measurement parameters and data acquisition techniques as well as on techniques for data analysis and interpretation. Finally, we compare in-situ monitoring methods against post NDE methods by analyzing the advantages and disadvantages of both. This research was funded by BAM within the Focus Area Materials. T2 - Coupled2021 - IX International Conference on Coupled Problems in Science and Engineering CY - Online meeting DA - 13.06.2021 KW - Additive Manufacturing KW - Thermography KW - Direct Energy Deposition PY - 2021 AN - OPUS4-54399 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Chaudhuri, Somsubhro T1 - EvalTherm: Evaluation of passive thermography for the inspection of operational wind turbine rotor blades N2 - Wind turbine rotor blades (WTBs) have undergone numerous developments related to their design, manufacturing, and material selection. The drive for cost efficiency has resulted in increasingly longer WTBs over the past two decades [1]. Despite advancements in wind turbine technology, WTB inspection methods have seen little change over the years. While drones are increasingly used, inspections still rely primarily on visual assessments. However, critical structural damage - often originating from manufacturing defects - typically begins within the composite blade structure. With the increase in wind turbine blade (WTB) sizes, now exceeding lengths of 120 meters, passive infrared thermography (IRT) has been explored as an alternative inspection method when thermal or optical excitation is impractical [2]. Despite its advantages, employing IRT in wind industry presents significant challenges due to inherent uncertainties related to material properties, environmental conditions, and calibration factors [3]. The effects of these conditions can be analysed through finite element (FE) modelling, albeit with certain limitations [4, 5]. This work is part of the multi-partner project “EvalTherm,” which aims to evaluate passive IRT as a non-destructive inspection tool for WTBs in operation (schematic of the project is shown in Figure 1:). One parameter that can be used to investigate its effectiveness is thermal contrast, which allows two distinct features to be distinguished in thermal data. Selected results from the project “EvalTherm” are discussed and shown in the presentation. They are primarily categorised into inspections done under controlled conditions: i.e. in laboratory (with active excitation in the form of irradiation) and a climate chamber (with a temperature transient introduced with airflow). Results from these trials help understand the combined effect that are observed in outside measurements, with exposure to the sun and diurnal temperature changes. The presentation will include results obtained from field inspections on operational WTBs. T2 - Wind Energy Science Conference (WESC) 2025 CY - Nantes, France DA - 24.06.2025 KW - Thermography KW - Wind energy KW - Wind turbine rotor blades KW - Windenergie Anlage Rotorblätter KW - Damage detection PY - 2025 AN - OPUS4-63558 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Altenburg, Simon T1 - Measurement of real temperatures in metal powder bed fusion: Hyperspectral thermography N2 - Detailed knowledge about the physics of the PBF-LB/M process is still lacking, and the simulation of the fast and small-scale process is challenging. Especially the experimental validation of complex simulations lacks a suitable measurement technique for temperature distributions at high speeds and spatial resolution. The complicated process physics, specifically the rapidly changing emissivity in and around the meltpool, pose a severe challenge for usual thermographic approaches. Here, we present first results of a hyperspectral measurement approach to reconstruct temperature and emissivity maps during the PBF-LB/M process in a custom manufacturing machine. The camera setup measures the thermal radiation of the process along a line at a rate of 20 kHz, spectrally resolved between 1 µm and 1.6 µm. When the meltpool travels perpendicularly across this line, a typical meltpool can be reconstructed by pointwise fitting for temperature emissivity separation, based on typical spectral emissivities from reference measurements. T2 - Lasers in Manufacturing Conference - LiM CY - Munich, Germany DA - 23.06.2025 KW - PBF-LB/M KW - In situ monitoring KW - Thermography KW - Additive Manufacturing KW - Process monitoring KW - Hyperspectral PY - 2025 AN - OPUS4-63564 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Oster, 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 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 - Altenburg, Simon T1 - Thermography in laser powder bed fusion of metals: time over threshold as feasible feature in thermographic data N2 - Thermography is one on the most promising techniques for in-situ monitoring for metal additive manufacturing processes. The high process dynamics and the strong focus of the laser beam cause a very complex thermal history within the produced specimens, such as multiple heating cycles within single layer expositions. This complicates data interpretation, e.g., in terms of cooling rates. A quantity that is easily calculated is the time a specific area of the specimen is at a temperature above a chosen threshold value (TOT). Here, we discuss variations occurring in time-over-threshold-maps during manufacturing of a defect free cuboid specimen. T2 - 15th Quantitative InfraRed Thermography conference CY - Online meeting DA - 21.09.2020 KW - Additive Manufacturing KW - Process monitoring KW - Thermography KW - L-PBF KW - Time over threshold PY - 2020 AN - OPUS4-51630 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Mohr, Gunther T1 - On the limitations of small cubes as test coupons for process parameter optimization in laser powder bed fusion of metals N2 - The capability to produce complexly and individually shaped metallic parts is one of the main advantages of the laser powder bed fusion (PBF LB/M) process. Development of material and machine specific process parameters is commonly based on results acquired from small cubic test coupons of about 10 mm edge length. Such cubes are usually used to conduct an optimization of process parameters to produce dense material. The parameters are then taken as the basis for the manufacturing of real part geometries. However, complex geometries go along with complex thermal histories during the manufacturing process, which can significantly differ from thermal conditions prevalent during the production of simply shaped test coupons. This may lead to unexpected and unpredicted local inhomogeneities of the microstructure and defect distribution in the final part and it is a root cause of reservations against the use of additive manufacturing for the production of safety relevant parts. In this study, the influence of changing thermal conditions on the resulting melt pool depth of 316L stainless steel specimens is demonstrated. A variation of thermo-graphically measured intrinsic preheating temperatures was triggered by an alteration of inter layer times and a variation of cross section areas of specimens for three distinct sets of process parameters. Correlations between the preheating temperature, the melt pool depth, and occurring defects were analyzed. The limited expressiveness of the results of small density cubes is revealed throughout the systematic investigation. Finally, a clear recommendation to consider thermal conditions in future process parameter optimizations is given. T2 - Icaleo 2023 CY - Chicago, IL, USA DA - 16.10.2023 KW - Additive manufacturing KW - Heat accumulation KW - Thermal history KW - In situ monitoring KW - Representative specimens KW - Thermography PY - 2023 AN - OPUS4-58656 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Altenburg, Simon T1 - Machine Learning and Thermography as Tools for Local Porosity Prediction in AM of Metals N2 - Quality assurance of metal additive manufacturing (PBF-LB/M) is still a challenge. Offering deep process insights, thermography is a well-suited monitoring technique. Here, we show how machine learning based on thermographic data enables a local part porosity prediction. T2 - Laser Applications Conference (LAC) CY - Prague, Czech Republic DA - 19.10.2025 KW - PBF-LB/M KW - In situ monitoring KW - Thermography KW - Additive Manufacturing KW - Process monitoring KW - Porosity prediction KW - Machine Learning KW - Feature extraction PY - 2025 AN - OPUS4-64669 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Stamm, Michael T1 - The KI-VISIR Reference Dataset: A Compilation of Thermal and Visual Inspection Data for Quantifying Leading Edge Rain Erosion N2 - This presentation introduces thermography (thermal imaging) as a key method for non-destructive testing (NDT) of wind turbine rotor blades. By leveraging solar heating and aerodynamic effects, passive thermography detects internal defects, delaminations, and surface issues like leading edge erosion. We discuss field measurements, the role of AI/Machine Learning (LATODA) for automated defect classification, and its importance for improving O&M efficiency and lifetime extension in the wind energy sector. T2 - WindEurope Annual Event 2025 CY - Copenhagen, Denmark DA - 08.04.2025 KW - Thermography KW - Wind Turbine Blades KW - AI KW - NDT KW - KI-VISIR PY - 2025 AN - OPUS4-64721 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Stamm, Michael T1 - Thermografische Ferninspektion von Windenergieanlagen im Betrieb –� Potenziale und Herausforderungen N2 - Rotorblätter zählen zu den kostenintensivsten Komponenten von Windenergieanlagen in Bezug auf Wartung und Ausfallzeiten. Mit der zunehmenden Blattlänge – aktuell bis zu 115 m – steigen auch die Anforderungen an effektive Inspektionsmethoden. Die Bundesanstalt für Materialforschung und -prüfung (BAM) entwickelt hierfür eine passive thermografische Ferninspektion vom Boden aus, die die klassische visuelle Prüfung durch Industriekletterer ergänzen oder perspektivisch ersetzen soll. Feldmessungen und Laborexperimente belegen das Potenzial dieser Technik, sowohl strömungsbedingte thermische Signaturen als auch strukturelle Anomalien innerhalb der Rotorblätter zu erfassen. Eine besondere Herausforderung liegt in der Trennung dieser überlagerten Effekte sowie in der eingeschränkten Kenntnis der inneren Blattstruktur aufgrund fehlender Designdaten. Um die Methode zur Marktreife zu führen, sind Fortschritte in der Bildverarbeitung, etwa durch eine patentierte Differenzbildung, erforderlich. Der Beitrag stellt die zugrunde liegende Messtechnik, Ergebnisse einer groß angelegten Feldstudie mit 30 Anlagen sowie die identifizierten physikalischen Einflussgrößen (Strömung, Struktur, Emissivität) vor und gibt einen Ausblick auf die nächsten Entwicklungsschritte. T2 - Thermo25 CY - Munich, Germany DA - 12.11.2025 KW - Thermography KW - Wind Turbine Blades KW - Wind KW - Rotorblätter PY - 2025 AN - OPUS4-64718 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Lecompagnon, Julien T1 - Thermographic Reference Dataset: Experimentally simulated Gaussian-shaped internal defects in GFRP N2 - We introduce a thermographic reference dataset consisting of 100 experimentally recorded temperature fields that replicate defect-like heat signatures in glass fiber reinforced polymer (GFRP) composites. Instead of relying on the fabrication of numerous damaged specimens, the dataset was generated by directly imprinting defect signatures onto an intact laminate using a near-infrared laser projector with spatial light modulation. The projected patterns are derived from parameterized Gaussian distributions, enabling systematic variation in defect size, shape, and orientation. The resulting steady-state thermal responses were captured with infrared thermography, providing high-resolution temperature distribution measurements for each case. This novel approach ensures that the heat transfer physics are preserved experimentally, while no special samples with different defects need to be manufactured to acquire datasets for different defect parameters. The dataset is ultimately designed as a resource for the benchmarking of thermographic non-destructive testing techniques, the validation of numerical heat transfer simulations, and the training of data-driven algorithms for defect detection in composite materials. KW - Thermography KW - Temperature KW - Composites KW - GFRP KW - Reference Data KW - Laser Projector KW - Nondestructive Testing PY - 2025 DO - https://doi.org/10.5281/zenodo.17378681 PB - Zenodo CY - Geneva AN - OPUS4-64472 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Lecompagnon, Julien T1 - Active laser thermography with spatially non-uniform heating N2 - Active laser thermography has proven to be a powerful technique for the detection and characterization of surface and subsurface defects in a wide range of materials. A crucial aspect of this success lies in the ability to tailor the spatial distribution of heating through laser-based excitation, providing a level of control unattainable with most conventional sources. In this presentation, two applications of active laser thermography developed at BAM are presented, both exploiting structured laser illumination to enhance defect detection. In the first application, a moving laser line operated in conjunction with a robotic arm is used for the automated thermographic inspection of complex metallic components. The continuous line-scan excitation induces localized and directional heat flow along the surface, enabling reliable detection of narrow cracks even on curved or irregular geometries. An initial 3D surface reconstruction is employed to compensate for deviations and deformations of the inspected object. The second part introduces the use of a digital spatial light modulator to project arbitrarily structured heating patterns. By combining multiple projected patterns with numerical reconstruction techniques, deeply buried internal defects can be resolved beyond the classical diffusion-limited resolution of thermography, effectively achieving thermographic super-resolution. T2 - Research Seminar of the Department of Robotics and Mechatronics, AGH Krakow CY - Krakow, Poland DA - 27.11.2025 KW - Thermography KW - Laser KW - NDT KW - Super resolution KW - Automation KW - Crack detection PY - 2025 AN - OPUS4-64889 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ulbricht, Alexander T1 - Separation of the Formation Mechanisms of Residual Stresses in LPBF 316L N2 - Rapid cooling rates and steep temperature gradients are characteristic of additively manufactured parts and important factors for the residual stress formation. This study examined the influence of heat accumulation on the distribution of residual stress in two prisms produced by Laser Powder Bed Fusion (LPBF) of austenitic stainless steel 316L. The layers of the prisms were exposed using two different border fill scan strategies: one scanned from the centre to the perimeter and the other from the perimeter to the centre. The goal was to reveal the effect of different heat inputs on samples featuring the same solidification shrinkage. Residual stress was characterised in one plane perpendicular to the building direction at the mid height using Neutron and Lab X-ray diffraction. Thermography data obtained during the build process were analysed in order to correlate the cooling rates and apparent surface temperatures with the residual stress results. Optical microscopy and micro computed tomography were used to correlate defect populations with the residual stress distribution. The two scanning strategies led to residual stress distributions that were typical for additively manufactured components: compressive stresses in the bulk and tensile stresses at the surface. However, due to the different heat accumulation, the maximum residual stress levels differed. We concluded that solidification shrinkage plays a major role in determining the shape of the residual stress distribution, while the temperature gradient mechanism appears to determine the magnitude of peak residual stresses. T2 - MLZ User Meeting 2020 CY - Online meeting DA - 08.12.2020 KW - Computed tomography KW - Neutron diffraction KW - X-ray diffraction KW - Additive manufacturing KW - Residual stress KW - Thermography KW - LPBF KW - Laser Powder Bed Fusion PY - 2020 AN - OPUS4-51793 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Ulbricht, Alexander A1 - Altenburg, Simon A1 - Sprengel, Maximilian A1 - Sommer, Konstantin A1 - Mohr, Gunther A1 - Fritsch, Tobias A1 - Mishurova, Tatiana A1 - Serrano-Munoz, Itziar A1 - Evans, Alexander A1 - Hofmann, M. A1 - Bruno, Giovanni T1 - Separation of the Formation Mechanisms of Residual Stresses in LPBF 316L N2 - Rapid cooling rates and steep temperature gradients are characteristic of additively manufactured parts and important factors for the residual stress formation. This study examined the influence of heat accumulation on the distribution of residual stress in two prisms produced by Laser Powder Bed Fusion (LPBF) of austenitic stainless steel 316L. The layers of the prisms were exposed using two different border fill scan strategies: one scanned from the centre to the perimeter and the other from the perimeter to the centre. The goal was to reveal the effect of different heat inputs on samples featuring the same solidification shrinkage. Residual stress was characterised in one plane perpendicular to the building direction at the mid height using Neutron and Lab X-ray diffraction. Thermography data obtained during the build process were analysed in order to correlate the cooling rates and apparent surface temperatures with the residual stress results. Optical microscopy and micro computed tomography were used to correlate defect populations with the residual stress distribution. The two scanning strategies led to residual stress distributions that were typical for additively manufactured components: compressive stresses in the bulk and tensile stresses at the surface. However, due to the different heat accumulation, the maximum residual stress levels differed. We concluded that solidification shrinkage plays a major role in determining the shape of the residual stress distribution, while the temperature gradient mechanism appears to determine the magnitude of peak residual stresses. KW - Additive Manufacturing KW - Laser Powder Bed Fusion KW - LPBF KW - AISI 316L KW - Online Process Monitoring KW - Thermography KW - Residual Stress KW - Neutron Diffraction KW - X-ray Diffraction KW - Computed Tomography PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-512903 DO - https://doi.org/10.3390/met10091234 VL - 10 IS - 9 PB - MDPI CY - Basel AN - OPUS4-51290 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bruno, Giovanni A1 - Maierhofer, Christiane T1 - Advanced Characterization and On-Line Process Monitoring of Additively Manufactured Materials and Components N2 - Additive manufacturing (AM) techniques have risen to prominence in many industrial sectors. This rapid success of AM is due to the freeform design, which offers enormous possibilities to the engineer, and to the reduction of waste material, which has both environmental and economic advantages. Even safety-critical parts are now being produced using AM. This enthusiastic penetration of AM in our daily life is not yet paralleled by a thorough characterization and understanding of the microstructure of materials and of the internal stresses of parts. The same holds for the understanding of the formation of defects during manufacturing. While simulation efforts are sprouting and some experimental techniques for on-line monitoring are available, still little is known about the propagation of defects throughout the life of a component (from powder to operando/service conditions). This Issue was aimed at collecting contributions about the advanced characterization of AM materials and components (especially at large-scale experimental facilities such as Synchrotron and Neutron sources), as well as efforts to liaise on-line process monitoring to the final product, and even to the component during operation. The goal was to give an overview of advances in the understanding of the impacts of microstructure and defects on component performance and life at several length scales of both defects and parts. KW - Non-destructive Testing KW - Additive Manufacturing KW - Materials Characterization KW - Online Monitoring KW - Residual Stress KW - Thermography KW - Computed Tomography PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-556833 DO - https://doi.org/10.3390/met12091498 VL - 12 IS - 9 SP - 1 EP - 3 PB - MDPI CY - Basel, Schweiz AN - OPUS4-55683 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hirsch, Philipp Daniel T1 - Robotic-assisted 3D scanning and laser thermography for crack inspection on complex components N2 - The integration of automation and robotics into inspection processes has marked a transformative shift in the evaluation of complex components. This study presents a novel approach employing robotic-assisted laser thermography for the automated identification and in-depth analysis of cracks in these intricate structures. This method not only streamlines the inspection process but also eliminates the need for numerous manual steps and the use of chemicals associated with traditional methods such as dye penetrant testing. With the increasing com-plexity of components, this is an important step, especially with regard to additively manufactured components, in order to be able to guarantee component safety for a long lifecycle. T2 - 17th Quantitative InfraRed Thermography Conference (QIRT) CY - Zagreb, Croatia DA - 01.07.2024 KW - Robot KW - Flying line KW - Crack detection KW - Robot path planning KW - Thermography PY - 2024 AN - OPUS4-60914 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Lecompagnon, Julien A1 - Ricci, M. A1 - Laureti, S. A1 - Ziegler, Mathias ED - Maldague, X. T1 - Practical study on the thermographic detectability of internal defects using temporally structured laser heating N2 - Modern laser systems have proven to be highly versatile heat sources for active thermographic testing. Compared to more traditional light sources, e.g. flash or halogen lamps, their output power can be easily modulated at high rates, allowing a wide variety of complex excitations to be realized. Although their total optical output power can be theoretically scaled to arbitrary values, the maximum output power is practically limited by many factors: the maximum power that the sample under test can absorb without altering the lighted surface itself, the trade-off between irradiance and inspected area, the cost of the laser system, etc. Furthermore, when working with spatial modulator systems, the output power must be limited to avoid damaging such devices. Nevertheless, to guarantee a sufficient amount of heating even for highly thermally conductive materials and/or deeply buried defects, the heating times can be extended, e.g., either by using step heating, long pulse thermography, or by lock-in thermography with a continuously modulated heating. However, for all these approaches, the ranging capabilities of the thermographic defect detection are reduced due to the limited frequency content of the excitation. To tackle this problem, i.e. to increase the excitation energy while preserving its frequency content, new approaches have been developed in the last two decades, among them the use of coded excitations in combination with pulse-compression, and the use of multiple lock-in analysis or a frequency modulated excitation signal. The challenges of such temporally structured heating techniques are manifold, for example, the DC component inherent in optical heating must be taken into account. In general, a wider frequency bandwidth or greater variability of the frequency components also means greater complexity for signal generation and data processing. In this paper, temporally structured excitation schemes with different degrees of complexity are compared on a high-power laser system. T2 - 17th International Conference on Quantitative InfraRed Thermography 2024 CY - Zagreb, Croatia DA - 01.07.2024 KW - Thermography KW - Laser KW - NDT KW - Coded excitation KW - Defect identification PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-653790 DO - https://doi.org/10.21611/qirt-2024-077 SN - 2371-4085 SP - 1 EP - 9 PB - QIRT Council AN - OPUS4-65379 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hirsch, Philipp Daniel A1 - Kunji Purayil, Sruthi Krishna A1 - Lecompagnon, Julien A1 - Pech May, Nelson Wilbur A1 - Ziegler, Mathias ED - Maldague, X. T1 - Robotic-Assisted 3D Scanning and Laser Thermography for Crack Inspection on Complex Components N2 - The integration of automation and robotics into non-destructive testing (NDT) marks a significant advancement in evaluating complex components. This paper introduces a novel approach using robotic-assisted laser thermography combined with automated 3D scanning to detect and analyze cracks in complex structures. The system uses an integrated line scanner with a robotic arm to capture high-resolution data, creating detailed 3D models for adaptive path planning and precise alignment correction. Laser thermography, based on localized heating and the "flying spot" approach, detects surfacenear cracks with high precision. Crack detection is achieved using the Canny algorithm optional on Fourier-transformed thermograms, offering robust results with minimal computation. This study highlights the potential of robotic-assisted 3D scanning and laser thermography as efficient and precise methods for crack inspection, advancing NDT technologies and ensuring the structural integrity of modern components. T2 - 17th International Conference on Quantitative InfraRed Thermography 2024 CY - Zagreb, Croatia DA - 01.07.2024 KW - Thermography KW - Non-destructive testing KW - Laser line KW - Robotic arm KW - Defect identification KW - Crack detection PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-653809 DO - https://doi.org/10.21611/qirt-2024-078 SP - 1 EP - 8 PB - QIRT Council AN - OPUS4-65380 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ziegler, Mathias T1 - How thermographic NDT can contribute to achieving the Sustainable Development Goals N2 - In 2015, the global community adopted the UN 2030 Agenda - a roadmap for the future. The Agenda covers economic, environmental and social aspects of development. They formulated the so-called 17 Sustainable Development Goals (SDGs). And what it means is: We are facing a profound systemic transformation! But the question is: how can we achieve these goals? Of course, each of us can contribute by changing some of our habits. But for us as a research organisation, the answer must be science! There are a number of key technologies that will be needed to manage this change. Our contribution as the NDT community should be to support this global transformation by making it a safe one! And infrared thermography is an integral part of this NDT community and represents one of these key technologies. Of the 17 SDGs, we have identified five SDGs (Goal 7: affordable and clean energy, Goal 9: industry, innovation and infrastructure, Goal 11: sustainable cities and communities, Goal 12: responsible consumption and production, Goal 13: climate action) that we believe we can support in line with our mission. In this paper, four specific examples from BAM (Bundesanstalt für Materialforschung und –prüfung / German Federal Institute for Materials Research and Testing) will be used to show how thermographic NDT can contribute to the achievement of the SDGs. As a first example, we want to establish the quality control of novel, resource-conserving high-temperature additive manufacturing processes by establishing thermography-based in-situ measurement techniques. The aim is to achieve zero-defect manufacturing for all processes involving heat, such as the PBF-LB/M and DED-LB/M processes, which are currently entering industrial mass applications. As a second example, our research is focused on replacing traditional resource-intensive inspection methods such as liquid penetrant or magnetic particle inspection with robotic high-resolution laser thermography, which we call photothermal CT and which will enable fully automated inspection of complex geometries at high speed and high resolution. As a third example, we want to support the energy transition and climate protection as a whole through modern IRT inspection techniques by establishing thermographic inspection of wind turbines, in particular of rotor blades. Such inspections will make wind energy safer to use, enable longer operating times and make it more economical by improving its efficiency. In addition to industrial applications, IRT inspection techniques can be applied to the civil engineering sector to support sustainable infrastructure. Useful application scenarios include the early detection of corrosion under protective coatings and the inspection of concrete coatings found on parking decks, which are mainly applied by hand. T2 - Pan-American Conference for Nondestructive Testing (VIII PANNDT) CY - Niagara Falls, Ontario, Canada DA - 09.06.2025 KW - Thermography KW - Additive manufacturing KW - Wind energy KW - Infrastructure PY - 2025 AN - OPUS4-63523 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hirsch, Philipp Daniel T1 - Inspection with multisensor laser thermography N2 - The automated testing of complex, highly stressed components requires advanced methods capable of detecting various forms of defects with precision and reliability. In this work we present our approach by combining robotic automation with laser thermography, as a versatile and efficient solution for inspecting intricate geometries under demanding industrial conditions. Central to this approach is the integration of a fiber-coupled laser as a flexible heat source, a high-performance thermal imaging system, an industrial robot, additional visible camera systems, different IR emitters and intelligent signal processing algorithms. This allows, after an initial fully-automated mapping of the part geometry and position that the object under test is thermographically tested for surface or bulk defects. Interferences caused by unideal surface conditions can be corrected for using simultaneously obtained optical images. Finally, all test results can be mapped onto a digital representation of the object leading to a fully digital and machine-readable documentation that can be used for quality assurance. This combination therefore enables accurate defect detection and characterization, overcoming traditional limitations associated with material variability and surface inhomogeneities. The system's adaptability allows for tailored solutions that address real-world challenges, ensuring reliable and repeatable results across diverse applications. The proposed methodology emphasizes the synergy between robotics, active thermography, and advanced data analysis to achieve high levels of precision and automation. This work highlights the potential of these technologies to optimize quality assurance processes and contribute to innovation in various industrial sectors. T2 - Pan-American Conference for Nondestructive Testing (VIII PANNDT) CY - Niagara Falls, Kanada DA - 09.06.2025 KW - ZFP4.0 KW - Automatisation KW - Crack detection KW - Laser KW - Thermography KW - Robot PY - 2025 AN - OPUS4-63646 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - D'Accardi, E. A1 - Ulbricht, Alexander A1 - Krankenhagen, Rainer A1 - Palumbo, D. A1 - Galietti, U. T1 - Capability of active thermography to detect and localize pores in Metal Additive Manufacturing materials N2 - Active thermography is a fast, contactless and non-destructive technique that can be used to detect internal defects in different types of material. Volumetric irregularities such as the presence of pores in materials produced by the Additive Manufacturing processes can strongly affect the thermophysical and the mechanical properties of the final component. In this work, an experimental investigation aimed at detecting different pores in a sample made of stainless AISI 316L produced by Laser Powder Bed Fusion (L-PBF) was carried out using pulsed thermography in reflection mode. The capability of the technique and the adopted setups in terms of geometrical and thermal resolution, acquisition frequency and energy Density of the heating source were assessed to discern two contiguous pores as well as to detect a single pore. Moreover, a quantitative indication about the minimum resolvable pore size among the available and analysed defects was provided. A powerful tool to assess the Limits and the opportunities of the pulsed technique in terms of detectability and localizability was provided by comparing active thermography results to Computed Tomography as well as a related Finite Element Analysis (FEA) to simulate the pulsed heating transfer with Comsol. T2 - 49th Italian Association for Stress Analysis Conferencee (AIAS 2020) CY - Online meeting DA - 02.09.2020 KW - Additive manufacturing KW - Laser powderbed fusion KW - Pores KW - Thermography KW - Micro-CT PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-519231 DO - https://doi.org/10.1088/1757-899X/1038/1/012018 VL - 1038 SP - 1 EP - 17 PB - Institute of Physics CY - London AN - OPUS4-51923 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Averin, Anton A1 - Hirsch, Philipp Daniel A1 - Lecompagnon, Julien T1 - Thermographic Reference Dataset: Defect Detection in Nuclear Waste Barrel Cutouts Using Long Pulse Thermography N2 - We introduce a thermographic dataset for subsurface defect detection in radioactive waste storage drums, comprising thermal sequences from 7 barrel specimens with artificially manufactured internal defects. The dataset was acquired using cost-effective halogen-lamp excitation (2kW per lamp) as an alternative to laser-based systems, with dual-camera thermal imaging (CMOS and bolometric) to enable performance comparison across imaging modalities. The specimens include both new and aged barrel types with controlled defects — FBHs, lines, crosses, triangles, and rectangles — simulating internal corrosion at varying scales (4mm to 60mm). Three heating regimes (both lamps, left only, right only) were systematically applied across multiple measurement regions per sample, yielding normalized thermal sequences. To lower the barrier for machine learning practitioners without thermography expertise, the dataset provides pre-computed features derived from principal component analysis, pulse phase thermography, and independent component analysis extracted using experimentally optimized time windows. Ground-truth binary masks mapping defect locations are included to enable supervised learning. This resource is designed to support the development and benchmarking of automated defect detection algorithms for non-destructive testing of curved, thin-walled metallic structures under realistic surface conditions (paint inhomogeneity, dirt, geometric artifacts), while validating low-cost thermographic inspection alternatives for industrial deployment. KW - Nondestructive Testing KW - Corrosion detection KW - Waste storage KW - Thermography KW - Safety PY - 2026 DO - https://doi.org/10.5281/zenodo.18916290 PB - Zenodo CY - Geneva AN - OPUS4-65666 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -