TY - CONF A1 - Schwenk, C. A1 - Kannengießer, Thomas A1 - Rethmeier, Michael T1 - Restraint conditions and welding resiudal stresses in self-restrained cold cracking test N2 - In the last decade, high-strength fine grained steels and welding consumables have gained a strong raise of application ranging from mobile cranes to bridge constructions. However, the cracking susceptibility of these steels increases significantly in correspondence with the achieved improvements in yield strength and the loss in plastic deformation reserves. In order to determine this behavior a series of different standardized cold cracking tests has been developed. One remaining major problem of these tests is the uncertainty about the quantitative intensity of the restraint conditions as well as the corresponding welding residual stresses. Consequently, the comparison of different tests and welding conditions as well as the transferability of the results onto real parts is difficult at best. The main topic of this paper is the analysis of the restraint conditions and their link with the welding induced residual stresses. The importance of the given standardized selfrestrained tests and first results about the transferability of results onto real parts are discussed. The influence of the test specimen geometry on the restraint conditions of the test is investigated for a selected test with numerical Simulation using commercial FEA software. Additionally, the residual stresses caused by the welding process are measured and linked with the restraint conditions which are defined mainly by the geometry parameters. Finally the transferability of the selected cold cracking test results is validated experimentally. The test results of a multilayer weld on high-strength fine grained steel of real size weldments are investigated. For these experiments a 16 MN large scale testing facility is used which is capable of applying the high reaction forces and clamping conditions found at large scale demonstrator parts. The results show the importance of the quantitative knowledge of the restraint conditions and the welding residual stresses on the cold cracking resistance. T2 - 8th International Conference on Trends in Welding Research CY - Pine Mountain, USA DA - 01.06.2008 KW - Cold Cracking Test KW - Intensity of Restraint PY - 2008 DO - https://doi.org/10.1361/cp2008twr766 SP - 766 EP - 771 PB - ASM international AN - OPUS4-47623 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Frei, J. A1 - Biegler, M. A1 - Rethmeier, Michael T1 - Resistance spot welding under external load for evaluation of LME susceptibility of zinc coated advanced high strength steel sheets N2 - Some zinc coated advanced high strength steels (AHSS), under certain manufacturing conditions, are known to be prone to liquid metal embrittlement (LME) during resistance spot welding. LME is an undesired phenomenon, which can cause both surface and internal cracks in a spot weld, potentially influencing its strength. An effort is made to understand influencing factors of LME better, and evaluate geometry-material combinations regarding their LME susceptibility. Manufacturers benefit from such knowledge because it improves the processing security of the materials. The experimental procedure of welding under external load is performed with samples of multiple AHSS classes with strengths up to 1200 MPa, including dual phase, complex phase and TRIP steels. This way, externally applied tensile load values are determined, which cause liquid metal embrittlement in the samples to occur. In the future, finite element simulation of this procedure gives access to in-situ stress and strain values present during LME formation. The visualization improves the process understanding, while a quantification of local stresses and strains allows an assessment of specific welded geometries. T2 - ESDAD 2019 CY - Dusseldorf, Germany DA - 24.06.2019 KW - RSW KW - LME KW - Advanced high strength steel KW - Testing method KW - Zinc coated steel PY - 2019 AN - OPUS4-49079 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Marko, A. A1 - Schafner, A. A1 - Raute, J. A1 - Rethmeier, Michael T1 - Relative density prognosis for directed energy deposition with the help of artificial neural networks N2 - Additive manufacturing, and therefore directed energy deposition, is gaining more and more interest from industrial users. However, quality assurance for the components produced is still a challenge. Machine learning, especially using artificial neuronal networks, is a potential method for ensuring a high-quality standard. Based on process Parameters and monitoring data, part quality can be predicted. A further advantage is the ability to constantly learn and adopt to slight process changes. First tests using artificial neural networks focus on the prediction of track geometry. The results show that even a small data set is enough to provide high accuracy in the predictions. In this work, an artificial neural network for the predictive analysis of relative density in laser powder cladding has been developed. A central composite experimental design is used to generate 19 data sets. Input variables are laser power, feed rate and powder mass flow. Cubes are built up where density is considered as a target value. Several neural networks are trained and evaluated with these data sets. Different topologies and initial weights are considered. The best network reaches a confidence level of around 90 % for the prediction of relative density based on the process parame� ters. Finally, the optimization of the generalization performance is investigated. To this purpose, methods of variation in error limit as well as cross-validation are applied. In this way, density is predictable by an artificial neural network with an accuracy of about 95 %. KW - Directed energy deposition KW - Artificial neural network PY - 2021 DO - https://doi.org/10.1515/mt-2020-0004 SN - 0025-5300 VL - 63 IS - 1 SP - 41 EP - 47 PB - DE Gruyter CY - Berlin AN - OPUS4-52690 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Müller, Vinzenz A1 - Klement, Oliver A1 - Sander, Steffen A1 - Biegler, Max A1 - Rethmeier, Michael T1 - Recycling of Stainless Steel Milling Chips for Additive Manufacturing: A Three-Stage Comminution Approach Using a Fine Impact Mill N2 - The production of conventional metal powders for additive manufacturing process is energy intensive and costly. This study introduces a sustainable alternative by recycling stainless steel milling chips as feedstock for laser-powder directed energy deposition. The recycling process employs a three-stage mechanical comminution method utilizing a fine impact mill UPZ100 from Hosokawa Alpine AG. Characterization of the resulting powders is conducted through particle morphology analysis, flowability tests, and mechanical property assessments. The chip-derived powders exhibit comparable aspect ratios and sphericity to conventional water atomized powders, though with reduced flowability due to a pronounced fine fraction content. Elevated levels of oxides are observed, leading to the formation of an oxide layer on specimen blocks, without impairing the mechanical properties. Analyses of porosity, microstructure, and hardness indicate no significant differences when compared to conventional powders from water or gas atomization. This recycling approach not only mitigates waste but also enhances the potential for a circular and sustainable manufacturing process in the additive manufacturing industry and beyond. KW - Directed Energy Deposition KW - Recycling KW - Stainless steel KW - Comminution KW - Powder characteristics PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-652037 DO - https://doi.org/10.1088/1757-899X/1332/1/012014 SN - 1757-8981 VL - 1332 IS - 1 SP - 1 EP - 7 PB - IOP Publishing AN - OPUS4-65203 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Müller, Vinzenz A1 - Fasselt, Janek Maria A1 - Klötzer-Freese, Christian A1 - Kruse, Tobias A1 - Kleba-Ehrhardt, Rafael A1 - Biegler, Max A1 - Rethmeier, Michael T1 - Recycling nickel aluminium bronze grinding chips to feedstock for directed energy deposition via impact whirl milling: Investigation on processability, microstructure and mechanical properties N2 - During the production of ship propellers, considerable quantities of grinding chips from nickel aluminium bronze are produced. This paper examines the mechanical comminution of such chips via impact whirl milling and the utilization of two chip-powder batches as feedstock for a laser-based directed energy deposition process. The materials are characterized via digital image analysis, standardized flowability tests, scanning electron microscopy and energy dispersive X-ray spectroscopy and are compared to conventional, gas atomized powder. The specimens deposited via directed energy deposition are analyzed for density, hardness and microstructure and tensile properties for vertical and horizontal build up directions are compared. At elevated mill rotation speeds, the comminution with impact whirl milling produced rounded particles, favorable flow properties and particle size distribution, making them suitable to deposit additive specimens. The microstructure exhibited characteristic martensitic phases due to the high cooling rates of the additive manufacturing process. The presence of ceramic inclusions was observed in both the powder and on the tensile fracture surfaces, partly impairing the mechanical properties. However, specimens in the vertical build-up direction (Z) showed competitive tensile results, with 775 MPa in tensile strength, 455 MPa in yield strength and 12.6 % elongation at break. The findings of this study indicate that recycling of machining chips to additive manufacturing feedstock can be a viable option for reducing material costs and environmental impact. KW - Nickel aluminium bronze KW - Grinding chips KW - Recycling KW - Directed energy deposition KW - Material characterization PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-633442 DO - https://doi.org/10.1016/j.addma.2025.104804 SN - 2214-8604 VL - 105 SP - 1 EP - 9 PB - Elsevier BV AN - OPUS4-63344 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Müller, Vinzenz A1 - Fasselt, Janek Maria A1 - Klötzer-Freese, Christian A1 - Kruse, Tobias A1 - Kleba-Ehrhardt, Rafael A1 - Biegler, Max A1 - Rethmeier, Michael T1 - Recycling nickel aluminium bronze grinding chips to feedstock for directed energy deposition via impact whirl milling: Investigation on processability, microstructure and mechanical properties N2 - During the production of ship propellers, considerable quantities of grinding chips from nickel aluminium bronze areproduced. This paper examines the mechanical comminution of such chips via impact whirl milling and the utilization of twochip-powder batches as feedstock for a laser-based directed energy deposition process. The materials are characterized viadigital image analysis, standardized flowability tests, scanning electron microscopy and energy dispersive X-ray spectroscopyand are compared to conventional, gas atomized powder. The specimens deposited via directed energy deposition areanalyzed for density, hardness and microstructure and tensile properties for vertical and horizontal build up directions arecompared. At elevated mill rotation speeds, the comminution with impact whirl milling produced rounded particles, favorableflow properties and particle size distribution, making them suitable to deposit additive specimens. The microstructureexhibited characteristic martensitic phases due to the high cooling rates of the additive manufacturing process. The presenceof ceramic inclusions was observed in both the powder and on the tensile fracture surfaces, partly impairing the mechanicalproperties. However, specimens in the vertical build-up direction (Z) showed competitive tensile results, with 775 MPa intensile strength, 455 MPa in yield strength and 12.6 % elongation at break. The findings of this study indicate that recyclingof machining chips to additive manufacturing feedstock can be a viable option for reducing material costs and environmentalimpact. KW - Nickel aluminium bronze KW - Grinding chips KW - Recycling KW - Directed energy deposition KW - Material characterization PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-651214 DO - https://doi.org/10.1016/j.addma.2025.104804 SN - 2214-8604 VL - 105 SP - 1 EP - 9 PB - Elsevier B.V. AN - OPUS4-65121 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Fabry, Çağtay A1 - Pittner, Andreas A1 - Hirthammer, Volker A1 - Rethmeier, Michael T1 - Recommendations for an Open Science approach to welding process research data N2 - The increasing adoption of Open Science principles has been a prevalent topic in the welding science community over the last years. Providing access to welding knowledge in the form of complex and complete datasets in addition to peer-reviewed publications can be identified as an important step to promote knowledge exchange and cooperation. There exist previous efforts on building data models specifically for fusion welding applications; however, a common agreed upon implementation that is used by the community is still lacking. One proven approach in other domains has been the use of an openly accessible and agreed upon file and data format used for archiving and sharing domain knowledge in the form of experimental data. Going into a similar direction, the welding community faces particular practical, technical, and also ideological challenges that are discussed in this paper. Collaboratively building upon previous work with modern tools and platforms, the authors motivate, propose, and outline the use of a common file format specifically tailored to the needs of the welding research community as a complement to other already established Open Science practices. Successfully establishing a culture of openly accessible research data has the potential to significantly stimulate progress in welding research. KW - Welding KW - Research data management KW - Open science KW - Digitalization KW - Weldx KW - Open source PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-529332 DO - https://doi.org/10.1007/s40194-021-01151-x SN - 1878-6669 SN - 0043-2288 SP - 1 EP - 9 PB - Springer CY - Heidelberg AN - OPUS4-52933 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Häberle, Nicolas A1 - Pittner, Andreas A1 - Rethmeier, Michael T1 - Rechenzeitersparnis bei der numerischen Lösung der nicht- linearen transienten 3D-Wärmeleitungsgleichung durch explizite Zeitintegration N2 - Die transiente nichtlineare 3D-Wärmeleitungsgleichung wurde zur numerischen Lösung mittels der Finite-Elemente-Methode im Ort und mittels explizitem Eulerschema in der Zeit diskretisiert. Der resultierende Algorithmus wurde in ein Computerprogramm überführt wobei besonderer Wert auf paralleles Rechnen gelegt wurde. Das Programm wurde auf die numerische Berechnung eines schweißtypischen transienten Temperaturfelds angewandt. Die Rechenzeit und Skalierbarkeit des Computerprogramms bezüglich der Anzahl verwendeter CPU Kerne wurde untersucht und mit dem kommerziellen FEM Programm Abaqus 6.14 verglichen. Die Anwendung der expliziten Zeitintegration resultiert in verbesserter Skalierbarkeit bezüglich der Anzahl verwendeter CPU Kerne und Rechenzeitersparnis gegenüber der in Abaqus implementierten impliziten Zeitintegrationsmethode. T2 - 37. Assistentenseminar Füge- und Schweißtechnik CY - Päwesin, Germany DA - 5. September 2016 KW - Schweißsimulation PY - 2017 SN - 978-3-96144-025-2 VL - 339 SP - 115 EP - 120 PB - DVS Media GmbH CY - Düsseldorf AN - OPUS4-44282 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Javaheri, E. A1 - Kumala, V. A1 - Javaheri, A. A1 - Rawassizadeh, R. A1 - Lubritz, J. A1 - Graf, B. A1 - Rethmeier, Michael T1 - Quantifying Mechanical Properties of Automotive Steels with Deep Learing Based Computer Vision Algorithms N2 - This paper demonstrates that the instrumented indentation test (IIT), together with a trained artificial neural network (ANN), has the capability to characterize the mechanical properties of the local parts of a welded steel structure such as a weld nugget or heat affected zone. Aside from force-indentation depth curves generated from the IIT, the profile of the indented surface deformed after the indentation test also has a strong correlation with the materials’ plastic behavior. The profile of the indented surface was used as the training dataset to design an ANN to determine the material parameters of the welded zones. The deformation of the indented surface in three dimensions shown in images were analyzed with the computer vision algorithms and the obtained data were employed to train the ANN for the characterization of the mechanical properties. Moreover, this method was applied to the images taken with a simple light microscope from the surface of a specimen. Therefore, it is possible to quantify the mechanical properties of the automotive steels with the four independent methods: (1) force-indentation depth curve; (2) profile of the indented surface; (3) analyzing of the 3D-measurement image; and (4) evaluation of the images taken by a simple light microscope. The results show that there is a very good Agreement between the material parameters obtained from the trained ANN and the experimental uniaxial tensile test. The results present that the mechanical properties of an unknown steel can be determined by only analyzing the images taken from its surface after pushing a simple indenter into its surface. KW - Deep learning KW - Computer vision KW - Artificial neural network KW - Clustering KW - Mechanical properties KW - High strength steels KW - Instumented indentation test PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-503038 DO - https://doi.org/10.3390/met10020163 VL - 10 IS - 2 SP - 163 PB - MDPI AN - OPUS4-50303 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 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 -