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 - CONF A1 - Breese, Philipp Peter A1 - Becker, Tina A1 - Oster, Simon A1 - Altenburg, Simon A1 - Metz, C. A1 - Maierhofer, Christiane T1 - Aktive Laserthermografie im L-PBF-Prozess zur in-situ Detektion von Defekten N2 - Die zerstörungsfreie Prüfung von metallischen Bauteilen hergestellt mit additiver Fertigung (Additive Manufacturing - AM) gewinnt zunehmend an industrieller Bedeutung. Grund dafür ist die Feststellung von Qualität, Reproduzierbarkeit und damit auch Sicherheit für Bauteile, die mittels AM gefertigt wurden. Jedoch wird noch immer ex-situ geprüft, wobei Defekte (z.B. Poren, Risse etc.) erst nach Prozessabschluss entdeckt werden. Übersteigen Anzahl und/oder Abmessung die vorgegebenen Grenzwerte für diese Defekte, so kommt es zu Ausschuss, was angesichts sehr langer Bauprozessdauern äußerst unrentabel ist. Eine Schwierigkeit ist dabei, dass manche Defekte sich erst zeitverzögert zum eigentlichen Materialauftrag bilden, z.B. durch thermische Spannungen oder Schmelzbadaktivitäten. Dementsprechend sind reine Monitoringansätze zur Detektion ggf. nicht ausreichend. Daher wird in dieser Arbeit ein Verfahren zur aktiven Thermografie an dem AM-Prozess Laser Powder Bed Fusion (L-PBF) untersucht. Das Bauteil wird mit Hilfe des defokussierten Prozesslasers bei geringer Laserleistung zwischen den einzelnen gefertigten Lagen unabhängig vom eigentlichen Bauprozess erwärmt. Die entstehende Wärmesignatur wird ort- und zeitaufgelöst durch eine Infrarotkamera erfasst. Durch diese der Lagenfertigung nachgelagerte Prüfung werden auch zum Bauprozess zeitversetzte Defektbildungen nachweisbar. In dieser Arbeit finden die Untersuchungen als Proof-of-Concept, losgelöst vom AM-Prozess, an einem typischen metallischen Testkörper statt. Dieser besitzt eine Nut als oberflächlichen Defekt. Die durchgeführten Messungen finden an einer eigens entwickelten L-PBF-Forschungsanlage innerhalb der Prozesskammer statt. Damit wird ein neuartiger Ansatz zur aktiven Thermografie für L-PBF erforscht, der eine größere Bandbreite an Defektarten auffindbar macht. Der Ansatz wird validiert und Genauigkeit sowie Auflösungsvermögen geprüft. Eine Anwendung am AM-Prozess wird damit direkt forciert und die dafür benötigten Zusammenhänge werden präsentiert. T2 - DGZfP-Jahrestagung 2022 CY - Kassel, Germany DA - 23.05.2022 KW - Additive Manufacturing KW - Laser Powder Bed Fusion KW - Thermografie KW - Zerstörungsfreie Prüfung KW - Aktive Laserthermografie PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-550353 SN - 978-3-947971-25-1 VL - 177 SP - 1 EP - 9 PB - Deutsche Gesellschaft für Zerstörungsfreie Prüfung e.V. AN - OPUS4-55035 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Oster, Simon A1 - Scheuschner, Nils A1 - Chand, Keerthana A1 - Breese, Philipp Peter A1 - Becker, Tina A1 - Heinrichsdorff, F. A1 - Altenburg, Simon T1 - From Thermographic In-situ Monitoring to Porosity Detection – A Deep Learning Framework for Quality Control in Laser Powder Bed Fusion N2 - In this study, we present an enhanced deep learning framework for the prediction of porosity based on thermographic in-situ monitoring data of laser powder bed fusion processes. The manufacturing of two cuboid specimens from Haynes 282 (Ni-based alloy) powder was monitored by a short-wave infrared camera. We use thermogram feature data and x-ray computed tomography data to train a convolutional neural network classifier. The classifier is used to perform a multi-class prediction of the spatially resolved porosity level in small sub-volumes of the specimen bulk. T2 - SMSI - Sensor and Measurement Science International 2023 CY - Nürnberg, Germany DA - 08.05.2023 KW - Porosity KW - Laser powder bed fusion KW - In-situ monitoring KW - Thermography KW - Machine Learning PY - 2023 UR - https://www.ama-science.org/proceedings/details/4404 DO - https://doi.org/10.5162/SMSI2023/C5.4 SP - 179 EP - 180 AN - OPUS4-57616 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Oster, Simon A1 - 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 - Breese, Philipp Peter A1 - Becker, Tina A1 - Oster, Simon A1 - Metz, C. A1 - Altenburg, Simon T1 - In-situ defect detection via active laser thermographic testing for PBF-LB/M N2 - Great complexity characterizes Additive Manufacturing (AM) of metallic components via laser powder bed fusion (PBF-LB/M). Due to this, defects in the printed components (like cracks and pores) are still common. Monitoring methods are commercially used, but the relationship between process data and defect formation is not well understood yet. Furthermore, defects and deformations might develop with a temporal delay to the laser energy input. The component’s actual quality is consequently only determinable after the finished process. To overcome this drawback, thermographic in-situ testing is introduced. The defocused process laser is utilized for nondestructive testing performed layer by layer throughout the build process. The results of the defect detection via infrared cameras are shown for a research PBF-LB/M machine. This creates the basis for a shift from in-situ monitoring towards in-situ testing during the AM process. Defects are detected immediately inside the process chamber, and the actual component quality is determined. T2 - Lasers in Manufacturing (LiM) CY - Munich, Germany DA - 26.06.2023 KW - Additive manufacturing KW - Laser powder bed fusion KW - Nondestructive testing KW - Laser thermography KW - Defect detection PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-585060 SP - 1 EP - 10 PB - Wissenschaftliche Gesellschaft Lasertechnik und Photonik (WLT) CY - Hannover AN - OPUS4-58506 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 - JOUR A1 - Oster, Simon A1 - Breese, Philipp Peter A1 - Ulbricht, Alexander A1 - Mohr, Gunther A1 - Altenburg, Simon T1 - A deep learning framework for defect prediction based on thermographic in-situmonitoring in laser powder bed fusion N2 - The prediction of porosity is a crucial task for metal based additive manufacturing techniques such as laser powder bed fusion. Short wave infrared thermography as an in-situ monitoring tool enables the measurement of the surface radiosity during the laser exposure. Based on the thermogram data, the thermal history of the component can be reconstructed which is closely related to the resulting mechanical properties and to the formation of porosity in the part. In this study, we present a novel framework for the local prediction of porosity based on extracted features from thermogram data. The framework consists of a data pre-processing workflow and a supervised deep learning classifier architecture. The data pre-processing workflow generates samples from thermogram feature data by including feature information from multiple subsequent layers. Thereby, the prediction of the occurrence of complex process phenomena such as keyhole pores is enabled. A custom convolutional neural network model is used for classification. Themodel is trained and tested on a dataset from thermographic in-situ monitoring of the manufacturing of an AISI 316L stainless steel test component. The impact of the pre-processing parameters and the local void distribution on the classification performance is studied in detail. The presented model achieves an accuracy of 0.96 and an f1-Score of 0.86 for predicting keyhole porosity in small sub-volumes with a dimension of (700 × 700 × 50) μm3. Furthermore, we show that pre-processing parameters such as the porosity threshold for sample labeling and the number of included subsequent layers are influential for the model performance. Moreover, the model prediction is shown to be sensitive to local porosity changes although it is trained on binary labeled data that disregards the actual sample porosity. KW - Laser Powder Bed Fusion (PBF-LB/M, L-PBF) KW - Selective Laser Melting (SLM) KW - SWIR thermography KW - Online monitoring KW - Flaw detection KW - Machine learning KW - Convolutional neural networks (CNN) PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-575148 DO - https://doi.org/10.1007/s10845-023-02117-0 SN - 0956-5515 SP - 1 EP - 20 PB - Springer AN - OPUS4-57514 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Oster, Simon A1 - Maierhofer, Christiane A1 - Mohr, Gunther A1 - Hilgenberg, Kai A1 - Ulbricht, Alexander A1 - Altenburg, Simon T1 - Investigation of the thermal history of L-PBF metal parts by feature extraction from in-situ SWIR thermography N2 - Laser powder bed fusion is used to create near net shape metal parts with a high degree of freedom in geometry design. When it comes to the production of safety critical components, a strict quality assurance is mandatory. An alternative to cost-intensive non-destructive testing of the produced parts is the utilization of in-situ process monitoring techniques. The formation of defects is linked to deviations of the local thermal history of the part from standard conditions. Therefore, one of the most promising monitoring techniques in additive manufacturing is thermography. In this study, features extracted from thermographic data are utilized to investigate the thermal history of cylindrical metal parts. The influence of process parameters, part geometry and scan strategy on the local heat distribution and on the resulting part porosity are presented. The suitability of the extracted features for in-situ process monitoring is discussed. T2 - Thermosense: Thermal Infrared Applications XLIII CY - Online meeting DA - 12.04.2021 KW - SWIR camera KW - Additive manufacturing (AM) KW - Selective laser melting (SLM) KW - Laser beam melting (LBM) KW - In-situ monitoring KW - Infrared thermography PY - 2021 SN - 978-1-5106-4324-6 DO - https://doi.org/10.1117/12.2587913 VL - 11743 SP - 1 EP - 11 PB - SPIE - The international society for optics and photonics AN - OPUS4-52535 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Oster, Simon A1 - Scheuschner, Nils A1 - Chand, Keerthana A1 - Altenburg, Simon A1 - Gerlach, G. T1 - Erratum to: Potentials and challenges of deep-learning-assisted porosity prediction based on thermographic in-situ monitoring in laser powder bed fusion N2 - In this erratum to our previously published study section, we correct an error related to the first paragragh of section 5 "Prediction framework". PY - 2024 DO - https://doi.org/10.1515/teme-2023-0166 SN - 0171-8096 SN - 2196-7113 VL - 91 IS - 2 SP - 139 EP - 141 PB - De Gruyter CY - Berlin AN - OPUS4-59471 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Ulbricht, Alexander A1 - Mohr, Gunther A1 - Altenburg, Simon A1 - Oster, Simon A1 - Maierhofer, Christiane A1 - Bruno, Giovanni ED - Czujko, T. ED - Benedetti, M. T1 - Can Potential Defects in LPBF Be Healed from the Laser Exposure of Subsequent Layers? A Quantitative Study N2 - Additive manufacturing (AM) of metals and in particular laser powder bed fusion (LPBF) enables a degree of freedom in design unparalleled by conventional subtractive methods. To ensure that the designed precision is matched by the produced LPBF parts, a full understanding of the interaction between the laser and the feedstock powder is needed. It has been shown that the laser also melts subjacent layers of material underneath. This effect plays a key role when designing small cavities or overhanging structures, because, in these cases, the material underneath is feed-stock powder. In this study, we quantify the extension of the melt pool during laser illumination of powder layers and the defect spatial distribution in a cylindrical specimen. During the LPBF process, several layers were intentionally not exposed to the laser beam at various locations, while the build process was monitored by thermography and optical tomography. The cylinder was finally scanned by X-ray computed tomography (XCT). To correlate the positions of the unmolten layers in the part, a staircase was manufactured around the cylinder for easier registration. The results show that healing among layers occurs if a scan strategy is applied, where the orientation of the hatches is changed for each subsequent layer. They also show that small pores and surface roughness of solidified material below a thick layer of unmolten material (>200 µm) serve as seeding points for larger voids. The orientation of the first two layers fully exposed after a thick layer of unmolten powder shapes the orientation of these voids, created by a lack of fusion. KW - Computed tomography KW - Laser Powder Bed Fusion KW - In situ monitoring KW - infrared Thermography KW - Optical Tomography KW - Additive manufacturing KW - AISI 316L PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-528778 DO - https://doi.org/10.3390/met11071012 VL - 11 IS - 7 SP - 1012 PB - MDPI CY - Basel AN - OPUS4-52877 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Oster, Simon A1 - Fritsch, Tobias A1 - Ulbricht, Alexander A1 - Mohr, Gunther A1 - Bruno, Giovanni A1 - Maierhofer, Christiane A1 - Altenburg, Simon T1 - On the Registration of Thermographic In Situ Monitoring Data and Computed Tomography Reference Data in the Scope of Defect Prediction in Laser Powder Bed Fusion N2 - The detection of internal irregularities is crucial for quality assessment in metal-based additive manufacturing (AM) technologies such as laser powder bed fusion (L-PBF). The utilization of in-process thermography as an in situ monitoring tool in combination with post-process X-ray micro computed tomography (XCT) as a reference technique has shown great potential for this aim. Due to the small irregularity dimensions, a precise registration of the datasets is necessary as a requirement for correlation. In this study, the registration of thermography and XCT reference datasets of a cylindric specimen containing keyhole pores is carried out for the development of a porosity prediction model. The considered datasets show variations in shape, data type and dimensionality, especially due to shrinkage and material elevation effects present in the manufactured part. Since the resulting deformations are challenging for registration, a novel preprocessing methodology is introduced that involves an adaptive volume adjustment algorithm which is based on the porosity distribution in the specimen. Thus, the implementation of a simple three-dimensional image-to-image registration is enabled. The results demonstrate the influence of the part deformation on the resulting porosity location and the importance of registration in terms of irregularity prediction. KW - Selective laser melting (SLM) KW - Laser powder bed fusion (L-PBF) KW - Additive manufacturing (AM) KW - Process monitoring KW - Infrared thermography KW - X-ray computed tomography (XCT) KW - Defect detection KW - Image registration PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-549412 DO - https://doi.org/10.3390/met12060947 VL - 12 IS - 6 SP - 1 EP - 21 PB - MDPI AN - OPUS4-54941 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Oster, Simon A1 - Mann, S. A1 - Sharma, R. A1 - Reisgen, U. T1 - In-situ Identifikation der Schweißnahtgeometrie bei der Anwendung von MSG-Schweißprozessen N2 - Lichtbogenbasierte Schweißverfahren wie das Metallschutzgasschweißen (MSG) zählen zu den Standardverfahren der Fügetechnik und werden in vielen Industriebereichen automatisiert unter Verwendung von Industrierobotern eingesetzt. Dabei können Schweißnahtabweichungen auftreten, die aus Änderungen der Prozessrandbedingungen und der hohen Prozessdynamik resultieren. Hier ist die Kontrolle von Schmelzbad- und Schweißnahtgeometrie für die Sicherung der Nahtqualität bedeutsam. Durch den Einsatz optischer Sensorsysteme können mit hoher zeitlicher Auflösung in-situ Informationen des Prozesszustands ermittelt werden. Dabei stellen die rauen Prozessbedingungen und die hohe Strahlungsintensität des Lichtbogens eine Herausforderung für die optischen Komponenten dar. Forschungsarbeiten am Institut für Schweißtechnik und Fügetechnik der RWTH Aachen haben gezeigt, dass durch den Einsatz einer HDR-Kamera in Kombination mit einer strukturierten Laserbelichtung gezielt geometrische Informationen des Lichtbogens und des Schmelzbads aus den Prozessaufnahmen gewonnen werden können. In dieser Arbeit wird eine parallele Schweißnaht- und Schmelzbadbeobachtung durchgeführt, wobei geometrische Informationen durch die Anwendung von Bildverarbeitungsalgorithmen extrahiert werden. Dabei wird ein nachlaufendes Sensorsystem eingesetzt, welches aus einer HDR-Kamera und einer Laserbeleuchtung besteht. Es werden diffraktive optische Elemente (DOE) zur Erzeugung von verschiedenen Laserprojektionsmustern verwendet, um sowohl eine unidirektionale als auch eine multidirektionale Prozessbeobachtung durchführen zu können. Aus den geometrischen Informationen werden Kenngrößen berechnet, anhand derer der Prozesszustand beurteilt werden kann. Es zeigt sich, dass anhand der Kenngrößenverläufe Abweichungen in der Schweiß- und Schmelzbadgeometrie und Positionierungsfehler des Roboters identifiziert werden können. T2 - Große Schweißtechnische Tagung - DVS CAMPUS CY - Online meeting DA - 14.09.2020 KW - Schweißnahtgeometrie KW - MSG-Schweißen KW - In-situ Monitoring KW - Schmelzbadbeobachtung PY - 2020 SN - 978-3-96144-098-6 VL - 365 SP - 41 EP - 47 PB - DVS Media GmbH CY - Düsseldorf AN - OPUS4-51576 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Oster, Simon T1 - In-situ Identifikation der Schweißnahtgeometrie bei der Anwendung von MSG-Schweißprozessen N2 - Lichtbogenbasierte Schweißverfahren wie das Metallschutzgasschweißen (MSG) zählen zu den Standardverfahren der Fügetechnik und werden in vielen Industriebereichen automatisiert unter Verwendung von Industrierobotern eingesetzt. Dabei können Schweißnahtabweichungen auftreten, die aus Änderungen der Prozessrandbedingungen und der hohen Prozessdynamik resultieren. Hier ist die Kontrolle von Schmelzbad- und Schweißnahtgeometrie für die Sicherung der Nahtqualität bedeutsam. Durch den Einsatz optischer Sensorsysteme können mit hoher zeitlicher Auflösung in-situ Informationen des Prozesszustands ermittelt werden. Dabei stellen die rauen Prozessbedingungen und die hohe Strahlungsintensität des Lichtbogens eine Herausforderung für die optischen Komponenten dar. Forschungsarbeiten am Institut für Schweißtechnik und Fügetechnik der RWTH Aachen haben gezeigt, dass durch den Einsatz einer HDR-Kamera in Kombination mit einer strukturierten Laserbelichtung gezielt geometrische Informationen des Lichtbogens und des Schmelzbads aus den Prozessaufnahmen gewonnen werden können. In dieser Arbeit wird eine parallele Schweißnaht- und Schmelzbadbeobachtung durchgeführt, wobei geometrische Informationen durch die Anwendung von Bildverarbeitungsalgorithmen extrahiert werden. Dabei wird ein nachlaufendes Sensorsystem eingesetzt, welches aus einer HDR-Kamera und einer Laserbeleuchtung besteht. Es werden diffraktive optische Elemente (DOE) zur Erzeugung von verschiedenen Laserprojektionsmustern verwendet, um sowohl eine unidirektionale als auch eine multidirektionale Prozessbeobachtung durchführen zu können. Aus den geometrischen Informationen werden Kenngrößen berechnet, anhand derer der Prozesszustand beurteilt werden kann. Es zeigt sich, dass anhand der Kenngrößenverläufe Abweichungen in der Schweiß- und Schmelzbadgeometrie und Positionierungsfehler des Roboters identifiziert werden können. T2 - Große Schweißtechnische Tagung - DVS CAMPUS CY - Online meeting DA - 14.09.2020 KW - Schweißnahtgeometrie KW - MSG-Schweißen KW - In-situ Monitoring KW - Schmelzbadbeobachtung PY - 2020 AN - OPUS4-51579 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Scheuschner, Nils A1 - Heinrichsdorff, F. A1 - Oster, Simon A1 - Uhlmann, E. A1 - Polte, J. A1 - Gordei, A. A1 - Hilgenberg, Kai T1 - In-situ monitoring of the laser powder bed fusion process by thermography, optical tomography and melt pool monitoring for defect detection N2 - For the wide acceptance of the use of additive manufacturing (AM), it is required to provide reliable testing methods to ensure the safety of the additively manufactured parts. A possible solution could be the deployment of in-situ monitoring during the build process. However, for laser powder bed fusion using metal powders (PBF-LB/M) only a few in-situ monitoring techniques are commercially available (optical tomography, melt pool monitoring), which have not been researched to an extent that allows to guarantee the adherence to strict quality and safety standards. In this contribution, we present results of a study of PBF-LB/M printed parts made of the nickel-based superalloy Haynes 282. The formation of defects was provoked by local variations of the process parameters and monitored by thermography, optical tomography and melt pool monitoring. Afterwards, the defects were characterized by computed tomography (CT) to identify the detection limits of the used in-situ techniques. T2 - Lasers in Manufacturing Conference 2023 CY - Munich, Germany DA - 26.06.2023 KW - Thermography KW - Optical tomography KW - Melt-pool-monitoring KW - Laser powder bed fusion KW - Haynes 282 KW - Additive Manufacturing PY - 2023 UR - https://www.wlt.de/lim2023-proceedings/system-engineering-and-process-control SP - 1 EP - 10 AN - OPUS4-58466 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Massimo, Carraturo A1 - Breese, Philipp P. A1 - Oster, Simon ED - Rank, E. ED - Kollmannsberger, S. T1 - Thermal model for laser-based powder bed fusion of metal process: modelling, calibration, and experimental validation N2 - In the present contribution, we propose an effective numerical thermal modeling solution for melt pool simulations in Laser-based Powder Bed Fusion of Metals processes. The proposed model employs an anisotropic conductivity to represent melt pool dynamics effects in a homogeneous material model. The numerical implementation of the proposed physical model is first experimentally calibrated and then validated with respect to a series of melt pool measurements as acquired by using a short-wave infrared (SWIR) camera monitoring system. T2 - IVth International Conference on Simulation for Additive Manufacturing (Sim-AM 2023) CY - Munich, Germany DA - 26.07.2023 KW - Laser powder bed fusion of metals KW - Melt pool measurements KW - Experimental validation KW - Thermal analysis KW - SS 316L PY - 2023 DO - https://doi.org/10.23967/c.simam.2023.015 SP - 1 EP - 9 AN - OPUS4-62525 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 - 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 - 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 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 - 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 - 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 -