TY - JOUR A1 - Kannengießer, Thomas A1 - Rethmeier, Michael A1 - Portella, Pedro Dolabella A1 - Ewert, Uwe A1 - Redmer, Bernhard T1 - Assessment of hot cracking behaviour in welds N2 - Hot crack assessment during production and processing of metallic materials is an essential prerequisite for the safety of welded structures. The hot cracking investigations presented here were carried out as part of a study aimed at the development of Cr/Ni low transformation temperature filler materials. Low transformation temperature alloys open up the possibility for welding high strength steels. The externally loaded Modified Varestraint Transvarestraint hot cracking test was employed in the experiments described. The hot cracking resistance was evaluated with the help of light microscopy applied at the specimen surface. The proportionality between hot cracking susceptibility and Cr/Ni alloy content was explained by the altered solidification kinetics and by the enlarged solidification interval. The internal crack paths and the three-dimensional structure of the crack net in the material volume were examined using X-ray computer tomography. The total crack lengths for different material depths and circumjacent rectangular volumes, respectively, were be determined. An increasing hot cracking susceptibility with increasing Cr/Ni alloy content was also be established for the specimen volume. KW - Hot cracking KW - Low transformation temperature (LTT) alloy KW - Modified varestraint transvarestraint (MVT) hot cracking test KW - X-ray computer tomography PY - 2011 DO - https://doi.org/10.3139/146.110545 SN - 1862-5282 VL - 102 IS - 8 SP - 1001 EP - 1006 PB - Carl Hanser CY - München AN - OPUS4-24400 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Üstündag, Ömer A1 - Bakir, Nasim A1 - Brunner-Schwer, C. A1 - Knöfel, F. A1 - Gook, S. A1 - Gumenyuk, Andrey A1 - Rethmeier, Michael T1 - Laserstrahlhybridschweissen von Türmen für Windkraftanlagen Ökonomische und ökologische Vorteile N2 - Das Laserstrahlhybridschweißen ist beim Schweißen von Türmen für Windkraftanlagen eine Alternative zum Unterpulverschweißen von Dickblechen in Mehrlagentechnik und bietet hier ökonomische und ökologische Vorteile. Der industrielle Einsatz des Verfahrens ist jedoch durch prozessspezifische Herausforderungen eingeschränkt. Die im Beitrag beschriebene kontaktlose elektromagnetische Badstütze dient zur Erweiterung des Verfahrenspotenzials im Dickblechbereich >15 mm. KW - Elektromagnetische Badstütze KW - Laserhybridschweißen KW - Windkraftanlagen KW - Ökonomische und ökologische Vorteile PY - 2022 VL - 7 SP - 340 EP - 347 PB - DVS Media GmbH AN - OPUS4-56372 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Gook, Sergej A1 - El-sari, Bassel A1 - Biegler, Max A1 - Rethmeier, Michael T1 - Application of AI-based welding process monitoring for quality control in pipe production N2 - The paper presents the experimental results into the development of a multi-channel system for monitoring and quality assurance of the multi-wire submerged arc welding (SAW) process for the manufacture of large diameter pipes. Process signals such as welding current, arc voltage and the acoustic signal emitted from the weld zone are recorded and processed to provide information on the stability of the welding process. It was shown by the experiments that the acoustic pattern of the SAW process in a frequency range between 30 Hz and 2.5 kHz contains the most diagnostic information. The on-line quality assessment of the weld seam produced is carried out in combination with methods of artificial intelligence (AI). From the results obtained, it can be concluded that the use of the latest concepts in welding and automation technology, combined with the high potential of AI, can achieve a new level of quality assurance in pipe manufacturing. KW - Submerged arc welding KW - Artificial intelligence KW - High-strength fine-grain steels KW - Quality control PY - 2024 DO - https://doi.org/10.37434/tpwj2024.06.01 SN - 0957-798X IS - 6 SP - 3 EP - 8 PB - The Paton Publishing House AN - OPUS4-61000 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Neumann, Benedikt A1 - Biegler, Max A1 - Goecke, Sven-Frithjof A1 - Rethmeier, Michael T1 - Detektion von Bindefehlern beim DED-Arc T1 - Detection of Lack of Fusion during DED-Arc N2 - Based on artificial intelligence (AI) developed for monitoring arc welding, this article presents a deep neural network for monitoring lack of fusion defects in wire arc additive manufacturing of aluminium. The aim is to detect defects in built-up volumes on the basis of weld source data. These can be successfully processed by the algorithm presented and a trained AI. The achieved accuracy of the network is 90 percent. N2 - In dem Beitrag wird aufbauend auf Machine-Learning-Modellen, welche bereits zum Überwachen des Schutzgasschweißen erforscht wurden, ein tiefes neuronales Netz (DNN) zum Monitoring beim DED-Arc von Aluminium vorgestellt. Ziel ist die Detektion von Bindefehlern in den aufgebauten Volumina auf Grundlage von in Echtzeit gemessenen Schweißstromquellensignalen. Es werden Merkmalsvariablen durch Vorverarbeitung extrahiert sowie die Korrelation zwischen den Merkmalsvariablen und den Defekten analysiert. Durch den vorgestellten Algorithmus werden diese automatisiert als Input an ein DNN übergeben. Das entwickelte und trainierte neuronale Netz erkennt anhand signifikanter Merkmale aus den Strom- und Spannungsdaten Bindefehler mit einer Genauigkeit von ca. 90 Prozent. KW - DED-Arc KW - Aluminiumschweißen KW - Bindefehler KW - Prozessüberwachung KW - Maschinelles Lernen KW - Deep Neural Network PY - 2024 DO - https://doi.org/10.1515/zwf-2024-1107 VL - 8 SP - 577 EP - 583 PB - Walter de Gruyter GmbH AN - OPUS4-61630 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Marquardt, R. A1 - Gook, S. A1 - Biegler, M. A1 - Rethmeier, Michael T1 - Handgeführtes Laserstrahlschweißen am T-Stoß eines niedrig legierten Stahls N2 - Das handgeführte Laserstrahlschweißen gewinnt in der Industrie zunehmend an Bedeutung, da die hohe Produktivität und die einfache Handhabung Unternehmen wirtschaftliche Vorteile bieten. Derzeit ist der Einsatz in der Industrie jedoch auf Teile mit ästhetischen Anforderungen beschränkt, die häufig aus hochlegiertem Stahl bestehen. Um das Handschweißen mit Laserstrahl auch für Bauteile aus kostengünstigen Stähle mit guten mechanischen Eigenschaften einsetzen zu können, untersucht diese Studie den Einfluss des Schutzgases auf die Porosität am mikrolegierten Stahl HX340LAD mit einer Dicke von 1,5 mm. Getestet wurden die Gase Argon, Stickstoff, CO2 sowie Mischungen aus Argon und CO2 an T-Stöße mit Zusatzdraht. Die Qualifizierung der Porosität erfolgte gemäß DIN EN ISO 13919-1 an Querschliffen als auch mittels Röntgenuntersuchung. Die Ergebnisse zeigen, dass für diesen Stahl die Bewertungsgruppe B mittels CO2 als Schutzgas erreicht werden kann. Stickstoff führt zu Gruppe C, Argon zu D. KW - Handgeführtes Laserstrahlschweißen KW - HHLW KW - Stahl KW - Laser PY - 2025 VL - 2025 IS - 06 SP - 44 EP - 47 PB - DVS Media AN - OPUS4-63345 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Huo, Wenjie A1 - Schmies, Lennart A1 - Gumenyuk, Andrey A1 - Rethmeier, Michael A1 - Wolter, Katinka T1 - Prediction of mean strain from laser beam welding images and detection of defects via strain curves based on machine learning N2 - With the advancement of machine learning, many predictions and measurements in visual tasks can be achieved by convolutional neural networks (CNNs). Solidification hot cracking is a significant defect in laser beam welding, commonly encountered in practical applications. Existing theories indicate that the formation of cracks is closely related to strain accumulation near the solidification front. In this paper, we first leverage supervised Regression networks to design CNNs that achieve real-time average strain estimation for each frame in the collected welding videos. Two different architectures are proposed and compared: the first model stacks two frames at a set interval and feeds them into the network, while the second model extracts image features individually and predicts the results by calculating the correlation between them. Each network has its own advantages in Terms of computational efficiency and accuracy. Finally, we further train a multilayer perceptron (MLP) classification model that can detect the occurrence of cracks based on the predicted strain behaviors. KW - Laser beam welding KW - Mean strain prediction KW - Solidification cracking detection Convolutional neural networks KW - Convolutional neural networks PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-644495 DO - https://doi.org/10.1016/j.optlastec.2025.113975 SN - 0030-3992 VL - 192, Part F SP - 1 EP - 8 PB - Elsevier Ltd. AN - OPUS4-64449 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Schlichting, Joachim A1 - Brauser, Stephan A1 - Pepke, Lutz-Alexander A1 - Maierhofer, Christiane A1 - Rethmeier, Michael A1 - Kreutzbruck, Marc T1 - Thermographic testing of spot welds N2 - Spot welding is one of the most important technologies for joining sheet metal. While there are lot of approaches to non-destructive testing, quality assurance still mainly relies on welding parameter monitoring and destructive testing, leading to significant failure rates. In this paper an approach to spot weld testing using flash thermography is presented. The main focus of attention is on the identification of two typical error classes: stick welds and welds at the splash limit. Besides investigating the principal feasibility of thermography for zinc plated samples the results of a series test of spot welds joining 1 mm thick TRIP steel are shown. Based upon these results a statistical criterion is developed which allows a reliable classification of the named error classes. KW - Thermography KW - Spot welding KW - Automotive industry PY - 2012 DO - https://doi.org/10.1016/j.ndteint.2012.02.003 SN - 0963-8695 VL - 48 SP - 23 EP - 29 PB - Butterworth-Heinemann CY - Oxford AN - OPUS4-26276 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Chaurasia, Prashant Kumar A1 - Fabry, Çağtay A1 - Pittner, Andreas A1 - De, Amitava A1 - Rethmeier, Michael T1 - Automated in situ monitoring and analysis of process signatures and build profiles during wire arc directed energy deposition N2 - Wire arc directed energy deposition (DED-Arc) is an emerging metal additive manufacturing process to build near-net shaped metallic parts in a layer-by-layer with minimal material wastage. Automated in situ monitoring and fast-responsive analyses of process signatures and deposit profiles during DED-Arc are in ever demand to print dimensionally consistent parts and reduce post-deposition machining. A comprehensive experimental investigation is presented here involving real-time synchronous measurement of arc current, voltage, and the deposit profile using a novel multi-sensor monitoring framework integrated with the DED-Arc set-up. The recorded current–voltage transients are used to estimate the time-averaged arc power, and energy input in real time for an insight of the influence of wire feed rate and printing travel speed on the deposit characteristics. A unique attempt is made to represent the geometric profiles of the single-track deposits in a generalized mathematical form corresponding to a segmented ellipse, which has exhibited the minimum root-mean-square error of 0.03 mm. The dimensional inconsistency of multi-track deposits is evaluated quantitatively in terms of waviness using build profile monitoring and automated estimation, which is found to increase with an increase in step-over ratio and energy input. For the multi-track mild steel deposits, the suitable range of step-over ratio for the minimum surface waviness is observed to lie between 0.6 and 0.65. Collectively, the proposed framework of synchronized process monitoring and real-time analysis provides a pathway to achieve dimensionally consistent and defect-free parts, and highlights the potential for closed-loop control systems for a wider industrial application of DED-Arc. KW - Additive Manufacturing KW - Arc welding KW - DED-arc KW - Real-time monitoring and control KW - Dimensional inconsistency PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-642029 DO - https://doi.org/10.1007/s40964-025-01333-9 SN - 2363-9512 SP - 1 EP - 20 PB - Springer Science and Business Media LLC CY - Cham AN - OPUS4-64202 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - El-Sari, B. A1 - Biegler, M. A1 - Gook, S. A1 - Rethmeier, Michael T1 - KI zur Prozessüberwachung im Unterpulverschweißen N2 - Beim Unterpulverschweißen sind die Prozessgeräusche ein Indikator für eine gute Fügequalität. Diese Beurteilung kann i.d.R. nur von einer erfahrenen Fachkraft durchgeführt werden. Eine kürzlich entwickelte künstliche Intelligenz kann automatisch das akustische Prozesssignal anhand vortrainierter Merkmale klassifizieren und die Fügequalität anhand des Geräuschs beurteilen. Der Algorithmus, einmal richtig trainiert, kann den Prüfaufwand beim Unterpulverschweißen deutlich reduzieren. KW - Unterpulverschweißen KW - Künstliche Intelligenz KW - Prozessüberwachung KW - Körperschall PY - 2024 SP - 1 EP - 2 AN - OPUS4-59483 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Üstündag, Ömer A1 - Bakir, Nasim A1 - Gook, S. A1 - Gumenyuk, Andrey A1 - Rethmeier, Michael T1 - Hybrid Laser-Arc Welding of Thick-Walled, Closed, Circumferential Pipe Welds N2 - The application of hybrid laser-arc welding (HLAW) for joining closed circumferential welds is a challenge due to the high risk of forming a defective overlap area with a shrinkage void or solidification cracks in the material thickness. A series of HLAW experiments were performed to understand the development of a faulty overlap area when closing the circumferential weld. Welding trials on flat specimens and pipe segments were supported by numerical analyses in which the thermomechanical behavior of the welds in the overlap area was investigated. Different process control strategies were tested, including variations in defocusing levels and the overlap length. The newly developed HLAW head, including laser optics with a motor-driven collimation system, made it possible to defocus the laser beam during welding without disturbing the stability of the welding process. High-level defocusing of the laser beam of more than 40 mm relative to the specimen surface with a resulting beam diameter of > 2.9 mm, and in combination with a short overlap length of 15 mm, was promising with respect to the formation of a desired cup-shaped weld profile that is resistant to solidification cracks. KW - Hybrid Laser-Arc Welding KW - Thick-Walled Steel KW - High-Power Welding KW - Crater KW - Pipe Welding PY - 2022 DO - https://doi.org/10.29391/2022.101.002 SN - 0043-2296 VL - 101 IS - 1 SP - 15 EP - 26 PB - American Welding Society CY - New York, NY AN - OPUS4-55508 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -