TY - JOUR A1 - Üstündag, Ömer A1 - Bakir, Nasim A1 - Gook, S. A1 - Gumenyuk, Andrey A1 - Rethmeier, Michael T1 - Hybrid laser‑arc welding of laser‑ and plasma‑cut 20‑mm‑thick structural steels JF - Welding in the World 2022 N2 - It is already known that the laser beam welding (LBW) or hybrid laser-arc welding (HLAW) processes are sensitive to manufacturing tolerances such as gaps and misalignment of the edges, especially at welding of thick-walled steels due to its narrow beam diameter. Therefore, the joining parts preferably have to be milled. The study deals with the influence of the edge quality, the gap and the misalignment of edges on the weld seam quality of hybrid laser-arc welded 20-mm-thick structural steel plates which were prepared by laser and plasma cutting. Single-pass welds were conducted in butt joint configuration. An AC magnet was used as a contactless backing. It was positioned under the workpiece during the welding process to prevent sagging. The profile of the edges and the gap between the workpieces were measured before welding by a profile scanner or a digital camera, respectively. With a laser beam power of just 13.7 kW, the single-pass welds could be performed. A gap bridgeability up to 1 mm at laser-cut and 2 mm at plasma-cut samples could be reached respectively. Furthermore, a misalignment of the edges up to 2 mm could be welded in a single pass. The new findings may eliminate the need for cost and time-consuming preparation of the edges. KW - Hybrid laser-arc welding KW - Thick-walled steel KW - Edge quality KW - Gap bridgeability KW - Laser cutting KW - Plasma cutting PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-552013 DO - https://doi.org/10.1007/s40194-022-01255-y SN - 0043-2288 VL - 66 SP - 507 EP - 514 PB - Springer AN - OPUS4-55201 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Fabry, Cagtay A1 - Hirthammer, Volker A1 - Scherer, Martin K. T1 - weldx - welding data exchange format N2 - Scientific welding data covers a wide range of physical domains and timescales and are measured using various different sensors. Complex and highly specialized experimental setups at different welding institutes complicate the exchange of welding research data further. The WelDX research project aims to foster the exchange of scientific data inside the welding community by developing and establishing a new open source file format suitable for the documentation of experimental welding data and upholding associated quality standards. In addition to fostering scientific collaboration inside the national and international welding community an associated advisory committee will be established to oversee the future development of the file format. The proposed file format will be developed with regard to current needs of the community regarding interoperability, data quality and performance and will be published under an appropriate open source license. By using the file format objectivity, comparability and reproducibility across different experimental setups can be improved. KW - Welding KW - Research data management KW - Open science KW - Open Data KW - WelDX PY - 2021 DO - https://doi.org/10.5281/zenodo.6563282 PB - Zenodo CY - Geneva AN - OPUS4-55226 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Fabry, Cagtay A1 - Hirthammer, Volker A1 - Pittner, Andreas A1 - Rethmeier, Michael T1 - WelDX - a file format for processing and archiving welding research data N2 - The talk gives an introduction into gas metal arc welding and its relation to plasma science as well as current challenges in welding research concerning research data management and the application of the FAIR principles. The WelDX project is introduced and the main goals are discussed and contrasted with the current features of the weldx API. Different internal and public facing use cases focusing on research data management and their implementation using weldx are presented. The interactive part of the presentation displays some advanced multi layer use cases and data analysis using the weldx API as well as the integration of materials properties into weldx. T2 - International Workshop on FAIR Data in Plasma Science CY - Online meeting DA - 16.05.2022 KW - WelDX KW - Open science KW - Research data management KW - Arc welding KW - Digital transformation PY - 2022 AN - OPUS4-55153 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ávila Calderón, Luis Alexander A1 - Rehmer, Birgit A1 - Graf, B. A1 - Ulbricht, Alexander A1 - Skrotzki, Birgit A1 - Rethmeier, Michael T1 - Low cycle fatigue behavior of DED-L Ti-6AL-4V N2 - Laser powder-based directed energy deposition (DED-L) is a technology that offers the possibility for 3D material deposition over hundreds of layers and has thus the potential for application in additive manufacturing (AM). However, to achieve broad industrial application as AM technology, more data and knowledge about the fabricated materials regarding the achieved properties and their relationship to the manufacturing process and the resulting microstructure is still needed. In this work, we present data regarding the low-cycle fatigue (LCF) behavior of Ti-6Al-4V. The material was fabricated using an optimized DED-L process. It features a low defect population and excellent tensile properties. To assess its LCF behavior two conventionally manufactured variants of the same alloy featuring different microstructures were additionally tested. The strain-controlled LCF tests were carried out in fully reversed mode with 0.3 % to 1.0 % axial strain amplitude from room temperature up to 400°C. The LCF behavior and failure mechanisms are described. For characterization, optical microscopy (OM), scanning electron microscopy (SEM), and micro-computed tomography (µCT) were used. The low defect population allows for a better understanding of the intrinsic material’s properties and enables a fairer comparison against the conventional variants. The fatigue lifetimes of the DED-L material are nearly independent of the test temperature. At elevated test temperatures, they are similar or higher than the lifetimes of the conventional counterparts. At room temperature, they are only surpassed by the lifetimes of one of them. The principal failure mechanism involves multiple crack initiation sites. T2 - Ninth International Conference on Low Cycle Fatigue (LCF9) CY - Berlin, Germany DA - 21.06.2022 KW - AGIL KW - Additive Manufacturing KW - Ti-6Al-4V KW - Low-Cycle-Fatigue KW - Microstructure PY - 2022 AN - OPUS4-55123 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Marko, A. A1 - Bähring, S. A1 - Raute, J. A1 - Biegler, M. A1 - Rethmeier, Michael T1 - Transferability of ANN-generated parameter sets from welding tracks to 3D-geometries in Directed Energy Deposition JF - Material Testing 2022 N2 - Directed energy deposition (DED) has been in industrial use as a coating process for many years. Modern applications include the repair of existing components and additive manufacturing. The main advantages of DED are high deposition rates and low energy input. However, the process is influenced by a variety of parameters affecting the component quality. Artificial neural networks (ANNs) offer the possibility of mapping complex processes such as DED. They can serve as a tool for predicting optimal process parameters and quality characteristics. Previous research only refers to weld beads: a transferability to additively manufactured three-dimensional components has not been investigated. In the context of this work, an ANN is generated based on 86 weld beads. Quality categories (poor, medium, and good) are chosen as target variables to combine several quality features. The applicability of this categorization compared to conventional characteristics is discussed in detail. The ANN predicts the quality category of weld beads with an average accuracy of 81.5%. Two randomly generated parameter sets predicted as “good” by the network are then used to build tracks, coatings,walls, and cubes. It is shown that ANN trained with weld beads are suitable for complex parameter predictions in a limited way. KW - Welding parameter KW - Quality assurance KW - DED KW - Artificial neural network KW - Additive manufacturing PY - 2022 DO - https://doi.org/10.1515/mt-2022-0054 SN - 0025-5300 VL - 64 IS - 11 SP - 1586 EP - 1596 PB - De Gruyter AN - OPUS4-56278 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Straße, Anne A1 - Gumenyuk, Andrey A1 - Rethmeier, Michael T1 - Study on Duplex Stainless Steel Powder Compositions for the Coating of thick Plates of Laser Beam Welding JF - Advanced Engineering Materials N2 - Duplex stainless steels combine the positive properties of its two phases, austenite and ferrite. Due to its good corrosion resistance, high tensile strength, and good ductility, it has multiple applications. But laser beam welding of duplex steels changes the balanced phase distribution in favor of ferrite. This results in a higher vulnerability to corrosion and a lower ductility. Herein, different powder combinations consisting of duplex and nickel for coating layers by laser metal deposition (LMD) are investigated. Afterward, laser tracks are welded, and the temperature cycles are measured. The ferrite content of the tracks is analyzed by feritscope, metallographic analysis, and electron backscatter diffraction. The goal is the development of a powder mixture allowing for a duplex microstructure in a two-step process, where first the edges of the weld partners are coated with the powder mixture by LMD and second those edges are laser beam welded. The powder mixture identified by the pretests is tested in the two-step process and analyzed by metallographic analysis, energy-dispersive X-ray spectroscopy, and Vickers hardness tests. The resulting weld seams show a balanced duplex microstructure with a homogenous nickel distribution and a hardness of the weld seam similar to the base material. KW - Duplex AISI 2205 KW - Laser metal deposition KW - Laser beam welding KW - Nickel KW - Stainless steels PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-547024 DO - https://doi.org/10.1002/adem.202101327 SN - 1438-1656 SP - 1 EP - 11 PB - Wiley-VCH CY - Weinheim AN - OPUS4-54702 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Marko, A. A1 - Bähring, S. A1 - Raute, J. A1 - Biegler, M. A1 - Rethmeier, Michael T1 - Quality Prediction in Directed Energy Deposition Using Artificial Neural Networks Based on Process Signals JF - Applied Sciences N2 - The Directed Energy Deposition process is used in a wide range of applications including the repair, coating or modification of existing structures and the additive manufacturing of individual parts. As the process is frequently applied in the aerospace industry, the requirements for quality assurance are extremely high. Therefore, more and more sensor systems are being implemented for process monitoring. To evaluate the generated data, suitable methods must be developed. A solution, in this context, was the application of artificial neural networks (ANNs). This article demonstrates how measurement data can be used as input data for ANNs. The measurement data were generated using a pyrometer, an emission spectrometer, a camera (Charge-Coupled Device) and a laser scanner. First, a concept for the extraction of relevant features from dynamic measurement data series was presented. The developed method was then applied to generate a data set for the quality prediction of various geometries, including weld beads, coatings and cubes. The results were compared to ANNs trained with process parameters such as laser power, scan speed and powder mass flow. It was shown that the use of measurement data provides additional value. Neural networks trained with measurement data achieve significantly higher prediction accuracy, especially for more complex geometries. KW - DED KW - Artificial neural network KW - Process monitoring KW - Quality assurance KW - Data preparation PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-547039 DO - https://doi.org/10.3390/app12083955 VL - 12 IS - 8 SP - 1 EP - 13 PB - MDPI AN - OPUS4-54703 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Geisen, O. A1 - Müller, V. A1 - Graf, B. A1 - Rethmeier, Michael T1 - Integrated weld preparation designs for the joining of L‑PBF and conventional components via TIG welding JF - Progress in Additive Manufacturing N2 - size limitations and high production costs of L-PBF make it competitive for smaller, highly complex components, while the less complex elements of an assembly are manufactured conventionally. This leads to scenarios that use L-PBF only where it’s beneficial, and it require an integration and joining to form the final product. For example, L-PBF combustion swirlers are welded onto cast parts to produce combustion systems for stationary gas turbines. Today, the welding process requires complex welding fixtures and tack welds to ensure the correct alignment and positioning of the parts for repeatable weld results. In this paper, L-PBF and milled weld preparations are presented as a way to simplify the Tungsten inert gas (TIG) welding of rotationally symmetrical geometries using integrated features for alignment and fixation. Pipe specimens with the proposed designs are manufactured in Inconel 625 using L-PBF and milling. The pipe assembly is tested and TIG welding is performed for validation. 3D scans of the pipes before and after welding are evaluated, and the weld quality is examined via metallography and computed tomography (CT) scans. All welds produced in this study passed the highest evaluation group B according to DIN 5817. Thanks to good component alignment, safe handling, and a stable welding process, the developed designs eliminate the need for part-specific fixtures, simplify the process chain, and increase the process reliability. The results are applicable to a wide range of components with similar requirements. KW - L-PBF KW - AM feature integration KW - TIG welding KW - Integrated alignment features KW - Pipe weld preparation KW - Dissimilar joints KW - Inconel 625 PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-547042 DO - https://doi.org/10.1007/s40964-021-00221-2 SN - 2363-9512 SP - 1 EP - 11 PB - Springer AN - OPUS4-54704 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Gook, S. A1 - El-Sari, B. A1 - Biegler, M. A1 - Rethmeier, Michael A1 - Lichtenthäler, F. A1 - Stark, M. T1 - Multiple-wire submerged arc welding of high-strength fine-grained steels JF - The Patron Welding Journal N2 - Ensuring the required mechanical-technological properties of welds is a critical issue in the application of multi-wire submerged arc welding processes for welding high-strength fine-grained steels. Excessive heat input is one of the main causes for microstructural zones with deteriorated mechanical properties of the welded joint, such as a reduced notched impact strength and a lower structural robustness. A process variant is proposed which reduces the weld volume as well as the heat input by adjusting the welding wire configuration as well as the energetic parameters of the arcs, while retaining the advantages of multiwire submerged arc welding such as high process stability and production speed. KW - Submerged arc welding KW - High-strength fine-grained steels KW - Mechanical properties of the joints KW - Energy parameters of the arc PY - 2022 DO - https://doi.org/10.37434/tpwj2022.01.02 SN - 0957-798X IS - 1 SP - 9 EP - 13 PB - Paton Publishing House CY - Kiev AN - OPUS4-54701 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - El-Sari, B. A1 - Biegler, M. A1 - Rethmeier, Michael T1 - Investigation of the Extrapolation Capability of an Artificial Neural Network Algorithm in Combination with Process Signals in Resistance Spot Welding of Advanced High-Strength Steels JF - Metals N2 - Resistance spot welding is an established joining process for the production of safetyrelevant components in the automotive industry. Therefore, consecutive process monitoring is essential to meet the high quality requirements. Artificial neural networks can be used to evaluate the process parameters and signals, to ensure individual spot weld quality. The predictive accuracy of such algorithms depends on the provided training data set, and the prediction of untrained data is challenging. The aim of this paper was to investigate the extrapolation capability of a multi-layer perceptron model. That means, the predictive performance of the model was tested with data that clearly differed from the training data in terms of material and coating composition. Therefore, three multi-layer perceptron regression models were implemented to predict the nugget diameter from process data. The three models were able to predict the training datasets very well. The models, which were provided with features from the dynamic resistance curve predicted the new dataset better than the model with only process parameters. This study shows the beneficial influence of process signals on the predictive accuracy and robustness of artificial neural network algorithms. Especially, when predicting a data set from outside of the training space. KW - Automotive KW - Artificial intelligence KW - Quality monitoring KW - Resistance spot welding KW - Quality assurance PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-539783 DO - https://doi.org/10.3390/met11111874 VL - 11 IS - 11 SP - 1 EP - 11 PB - MDPI AN - OPUS4-53978 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -