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 - CONF A1 - Hälsig, André A1 - Eßbach, Tim-Bernd A1 - Scharf-Wildenhain, Ronny A1 - Altobelli, Martin Gabriel A1 - Phadnis, Prathamesh Jayant A1 - Hensel, Jonas A1 - Fabry, Çağtay A1 - Pittner, Andreas A1 - Armansyah, T1 - Qualitätssicherung beim Lichtbogenschweißen N2 - Der fortschreitende Trend der Automatisierung und Dokumentation sowie die wirtschaftliche und technische Optimierung von Fügeaufgaben machen es notwendig, Sensoren zielführend in bestehende und vorhandene Anlagentechnik zu integrieren. Jedoch bietet die reine Datenerfassung alleine nur einen geringen Mehrwert. Erst durch eine optimierte Datenspeicherung, eine intelligente sowie automatische Datenauswertung und -bewertung entsteht ein echter Nutzen für den industriellen Anwender. Zur Qualifizierung einer standardisierbaren Herangehensweise der Qualitätssicherung beim Lichtbogenschweißen wurden mit einer nationalen Arbeitsgruppe bestehende Unstetigkeiten der Qualitätsbeeinflussung geschweißter Bauteile aufgeschlüsselt und definiert. Parallel dazu wurde ein automatisiertes Schweißsystem mit hochgenauer Messtechnik (u. a. Stromstärke, Spannung, Drahtvorschub, Thermokamera, Geometriescanner usw.) ausgerüstet und validiert. Hierfür wurde die Open-Source-Technologie WelDX der BAM genutzt. Anschließend wurden bewusst fehlerhaft geschweißte Nähte im Vergleich zu fehlerfreien Referenzschweißungen hergestellt, Messdaten erfasst und analysiert. Im Rahmen dieses Beitrags werden erste Ergebnisse und Erkenntnisse in Bezug auf die Detektion und Bewertung von Unstetigkeiten beim Lichtbogenschweißen dargestellt. Dabei wurde deutlich, dass alternative Darstellungs- und Analysemethoden wie die Anwendung der Mahalanobis-Distanz im Stromstärke-Spannungs-Diagramm eine eindeutigere Korrelation zwischen Unstetigkeiten und Prozesssignalen ermöglichen. T2 - DVS CONGRESS 2025 CY - Essen, Germany DA - 16.09.2025 KW - Lichtbogenschweißen KW - Qualitätssicherung KW - Forschungsdatenmanagement KW - WelDX PY - 2025 SN - 978-3-96144-298-0 VL - 401 SP - 76 EP - 85 PB - DVS Media GmbH CY - Düsseldorf AN - OPUS4-64565 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Fabry, Çağtay T1 - WelDX Projektvorstellung N2 - Der Vortrag gibt eine Übersicht über die fortlaufende Entwicklung des WelDX Projektes an der BAM. Die Notwendigkeit des Datenaustausches für die deutsche Forschungslandschaft im Bereich des Lichtbogenschweißens wird motiviert und die wesentlichen Funktionen der weldx Python-Bibliothek anhand von Forschungsbeispielen dargestellt. T2 - Sitzung des DVS Fachausschuss FA03 - Lichtbogenschweißen CY - Ilmenau, Germany DA - 13.05.2025 KW - Forschungsdatenmanagement KW - Lichtbogenschweißen KW - Digitalisierung KW - Weldx PY - 2025 AN - OPUS4-63278 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Fabry, Çağtay T1 - Welding Process Data Management - openBIS workflows and lab integration N2 - Arc welding processes are an important manufacturing technology applied to a wide range of critical materials and components such as offshore constructions, pressure vessels and additive manufacturing. Data management for experimental arc welding research faces the challenge of constantly changing experimental setups, incorporating a wide range of custom sensor integrations. Measurements include timeseries process and temperature recordings, 3D-geometry data and video recordings of the process from a sub-millisecond scale to multiple hour-long experiments. In addition, various manual pre-processing steps of the workpieces need to be considered to track the complete manufacturing process and its analysis – from raw materials to final dataset and publication. As a unified RDM system, the BAM Data Store offers the capability to incorporate all steps – albeit not without its own challenges. The talk gives an overview of the different workflows and processing steps along the welding experiments together with their integration into the BAM Data Store. Current solutions and ongoing integration work is explained and discussed. This includes the direct integration and upload of automated processing steps into the Data Store from different machines and sensors using custom Python APIs. Ultimately the complete processing chain across multiple internal steps should be represented in the Data Store. T2 - openBIS user group meeting 2025 CY - Berlin, Germany DA - 22.09.2025 KW - Data Store KW - Openbis KW - Research Data Management KW - Research data KW - Digitalisation PY - 2025 AN - OPUS4-64203 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Fabry, Çağtay T1 - Welding Process Data Management - Perspectives on the BAM Data Store N2 - Arc welding processes are an important manufacturing technology applied to a wide range of critical materials and components such as offshore constructions, pressure vessels and additive manufacturing. Data management for experimental arc welding research faces the challenge of constantly changing experimental setups, incorporating a wide range of custom sensor integrations. Measurements include timeseries process and temperature recordings, 3D-geometry data and video recordings of the process from a sub-millisecond scale to multiple hour-long experiments. In addition, various manual pre-processing steps of the workpieces need to be considered to track the complete manufacturing process and its analysis – from raw materials to final dataset and publication. As a unified RDM system, the BAM Data Store offers the capability to incorporate all steps – albeit not without its own challenges. The talk gives an overview of the different workflows and processing steps along the welding experiments together with their integration into the BAM Data Store. Current solutions and ongoing integration work is explained and discussed. This includes the direct integration and upload of automated processing steps into the Data Store from different machines and sensors using custom Python APIs. Ultimately the complete processing chain across multiple internal steps should be represented in the Data Store. T2 - Data Store Days CY - Berlin, Germany DA - 09.03.2025 KW - Data Store KW - Openbis KW - Research Data Management KW - Reference Data KW - Welding PY - 2025 AN - OPUS4-62976 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Fabry, Çağtay A1 - Pittner, Andreas A1 - Rethmeier, Michael T1 - Arc-sidewall-attaching-driven control of swing arc motion in narrow gap GMAW N2 - A novel, event-driven approach to controlling the weaving motion in swing arc narrow gap GMAW is presented in this study. The control method is based on independently detecting the arc attachment event at each sidewall of the narrow groove to adjust the weaving motion in real time. Previous arc sensing approaches for swing arc principles are based on evaluating and comparing arc sensor readings collected during the dwell periods at each sidewall. Not only does this require the torch to be positioned at the groove centre and the arc motion to be symmetric, but previous methods have also been shown to rely on complex parametrization of control parameters. The newly presented approach is based on the real-time monitoring of the welding current progression during the approach of the arc towards the sidewall of the groove independently on each side. As soon as the arc attachment at the sidewall is detected based on a characteristic rise in the current signal, the weaving motion is stopped. For reference experiments in a 21-mm wide groove, the weaving angle amplitude is controlled and limited to 50° on both sides individually, resulting in stable process conditions and uniform sidewall fusion. It is further shown that the newly developed control method can successfully be applied to groove widths of 18 mm and 24 mm without reconfiguration of the control parameters, highlighting the flexibility of the approach. KW - Gas metal arc welding KW - Narrow gap KW - Arc sensor KW - Control PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-647640 DO - https://doi.org/10.1007/s40194-025-02238-5 SN - 0043-2288 SP - 1 EP - 11 PB - Springer Science and Business Media LLC AN - OPUS4-64764 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Fabry, Çağtay A1 - Hirthammer, Volker A1 - Scherer, Martin K. T1 - weldx-widgets: advanced visualisation and jupyter widgets for weldx N2 - This package provides advanced visualisation and interactive widgets for the weldx core package. KW - Weldx KW - Welding KW - Research data KW - Visualisation PY - 2025 DO - https://doi.org/10.5281/zenodo.17790485 PB - Zenodo CY - Geneva AN - OPUS4-64973 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Fabry, Çağtay T1 - pytcs: a TwinCAT Scope text export file reader N2 - A Python package for reading exported TwinCAT Scope Files. Export your TwinCAT Scope .svdx files to .txt/.csv and read them into Python. KW - Python KW - TwinCAT Scope KW - File reader KW - File format KW - Measurement data PY - 2025 DO - https://doi.org/10.5281/zenodo.17791125 PB - Zenodo CY - Geneva AN - OPUS4-64975 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Chaurasia, Prashant Kumar A1 - Fabry, Çağtay A1 - Pittner, Andreas A1 - Rethmeier, Michael T1 - Tandem-GMAW: advanced pulsed and short cicuiting process mode experimental data N2 - This dataset consists of raw recordings for 5 Tandem gas metal arc welding experiments (Tandem-GMAW / T-GMAW). KW - T-GMAW KW - High power welding KW - Deposition efficiency KW - Life Cycle Assessment PY - 2025 DO - https://doi.org/10.5281/zenodo.17951724 PB - Zenodo CY - Geneva AN - OPUS4-65206 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Chaurasia, Prashant Kumar A1 - Cagtay, Fabry A1 - Andreas, Pittner 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. T2 - IIW Annual Assembly 2025 CY - Genova, Italy DA - 23.06.2025 KW - Additive Manufacturing KW - Arc welding KW - Real-time monitoring and control KW - Dimensional inconsistency KW - DED-arc PY - 2025 AN - OPUS4-65231 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Pittner, Andreas A1 - Fabry, Cagtay A1 - Thiele, Marc A1 - Artinov, Antoni A1 - Rethmeier, Michael T1 - Automated Arc Welding and Sensor Based Quality Assessment for Enhancing Fatigue Strength and Structural Reliability of Offshore Wind Turbine Supports N2 - Offshore wind energy plays a crucial role in achieving a carbon dioxide free industrial sector in the coming decades. However, foundation systems for offshore wind turbines significantly impact the overall installation process, posing technical and regulatory challenges. Adopting lightweight design principles in steel construction such as dissolved load bearing structures in jacket foundations offers substantial potential for reducing resource consumption, particularly steel usage. This presentation explores the complete digitalization of welding manufacturing and testing processes, enabling fully automated production and data driven quality assessment of tubular nodes, which are vital components of jacket foundations. The study also investigates the relationship between seam geometry and fatigue strength inspired by bionic design principles. The results demonstrate that tubular nodes can be welded entirely automatically, accommodating geometric tolerances, and that seam geometries ca n be precisely manufactured according to suggestions from e.g. numerical models or other sources . These advancements significantly enhance the fatigue strength and service life of offshore wind turbine support structures. T2 - IIW Annual Assembly 2025 CY - Genova, Italy DA - 23.06.2025 KW - Automated gas metal arc welding KW - Quality assessment and control KW - Support structures KW - Fatigue PY - 2025 AN - OPUS4-65232 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -