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Die Anwendung von Leichtbauprinzipien im Stahlbau, wie z.B. die Verwendung von aufgelösten Tragstrukturen, sog. Jackets-Fundamenten, bietet ein großes Potential zur Reduzierung des Ressourcenverbrauchs, insbesondere im Hinblick auf die benötigte Stahlmenge.
In diesem Vortrag zeigen wir die Potenziale einer vollständigen Digitalisierung der schweißtechnischen Fertigungs- und Prüfkette auf, um eine vollautomatische Fertigung sowie Qualitätsprüfung von Rohrknoten als Schlüsselelement von Jackets-Fundamenten zu ermöglichen. Darüber hinaus wird der Zusammenhang zwischen der Geometrie der Nahtform und der resultierenden Ermüdungsfestigkeit durch numerische Methoden unter Einbeziehung bionischer Prinzipien bewertet. Es wird gezeigt, dass Hohlprofilknoten vollautomatisch unter Berücksichtigung von Geometrietoleranzen geschweißt werden können. Darüber hinaus konnte die Nahtform gemäß den numerischen Modellen hergestellt werden, was ein großes Potenzial für eine verlängerte Lebensdauer bietet. Anschließend werden die Verbesserungen bei der Ressourceneffizienz und die Reduzierung der Kohlendioxidemissionen durch eine Lebenszyklusanalyse bewertet.
Single-pass Hybrid Laser Arc Welding of Thick Materials Using Electromagnetic Weld Pool Support
(2019)
Hybrid laser-arc welding process allows single-pass welding of thick materials, provides good quality formation of joints with minimal thermal deformations and a high productivity in comparison with arc-based welding processes. Nevertheless, thick-walled steels with a thickness of 20 mm or more are still multi-pass welded using arc welding processes, due to increased process instability by increasing laser power. One limitation factor is the inadmissible formation of gravity drop-outs at the root. To prevent this, an innovative concept of electromagnetic weld pool support is used in this study. With help of such system a stable welding process can be established for 25 mm thick steel plates and beyond. Sound welds could be obtained which are tolerant to gaps and misalignment of the welded parts. The adaptation of this system to laser and hybrid laser-arc welding process can dramatically increase the potential field of application of these technologies for real industrial implementation.
Single-pass Hybrid Laser Arc Welding of Thick Materials Using Electromagnetic Weld Pool Support
(2019)
Hybrid laser-arc welding process allows single-pass welding of thick materials, provides good quality formation of joints with minimal thermal deformations and a high productivity in comparison with arc-based welding processes. Nevertheless, thick-walled steels with a thickness of 20 mm or more are still multi-pass welded using arc welding processes, due to increased process instability by increasing laser power. One limitation factor is the inadmissible formation of gravity drop-outs at the root. To prevent this, an innovative concept of electromagnetic weld pool support is used in this study. With help of such system a stable welding process can be established for 25 mm thick steel plates and beyond. Sound welds could be obtained which are tolerant to gaps and misalignment of the welded parts. The adaptation of this system to laser and hybrid laser-arc welding process can dramatically increase the potential field of application of these technologies for real industrial implementation.
Motivated by the tensile strength loss of 9%Ni steel arc welded joints made using Ni-based austenitic filler metals, the feasibility of maintaining the tensile strength using matching ferritic filler metal has been demonstrated. In comparison with shielded metal arc welded joint made using Ni-based austenitic electrode ENiCrMo-6, higher tensile strength comparable to that of the base metal was obtained using matching ferritic electrode. Besides, sufficient impact toughness energies with much lower mismatch were obtained for weld metal and heat-affected zone.
Welded joint with a lower mechanical mismatching is of considerable importance for achieving acceptable combination of tensile strength and impact toughness.Abetter combination of These mechanical properties is ensured by applying a post weld heat treatment.
Motivated by the tensile strength loss of 9%Ni steel arc welded joints made using Ni-based austenitic filler metals, the feasibility of maintaining the tensile strength using matching ferritic filler metal has been demonstrated. In comparison with shielded metal arc welded joint made using Ni-based austenitic electrode ENiCrMo-6, higher tensile strength comparable to that of the base metal was obtained using matching ferritic electrode. Besides, sufficient impact toughness energies with much lower mismatch were obtained for weld metal and heat-affected zone.
Welded joint with a lower mechanical mismatching is of considerable importance for achieving acceptable combination of tensile strength and impact toughness. A better combination of These mechanical properties is ensured by applying a post weld heat treatment.
In this paper, the friction stir welding process was numerically investigated for 6 mm thick aluminum alloy AA2024-T3. The finite element software COMSOL Multiphysics was used to calculate the transient thermal field during welding and the mechanical reaction depending on different mechanical clamping conditions and hardening models subsequently. A thermal pseudo-mechanical (TPM) heat source was implemented.
Softening effects of the material due to precipitation hardening dissolution caused by the frictional heat were accounted for. The transient temperature evolution measured by thermocouple elements at various locations was compared to the numerical results. A good agreement was found for the thermal field. A sensitivity study of the mechanical models showed the strong influence of the clamping conditions and the softening model.
Der Trend zum Leichtbau und die Transformation zur E-Mobilität in der
Automobilindustrie befeuern die Entwicklung neuer hochfester Stähle für den Karosseriebau. Derartige Werkstoffe sind beim Widerstandspunktschweißen besonders rissanfällig (LME). Das Schweißen unter Zug stellt eine effektive Methode um die LME-Anfälligkeit unterschiedlicher Werkstoffe qualitativ zu bestimmen.
Die Anwendung der additiven Fertigung nimmt stark zu. Eine neue Designfreiheit erlaubt es hier den Konstrukteurinnen und Konstrukteuren, die Bauteilfunktion in den Vordergrund der Entwicklungen zu stellen. Leichtbaustrukturen sowie Funktionsintegration machen es möglich, vormals Bauteile aus vielen Einzelkomponenten jetzt in einem einzigen Bauteil zu vereinigen. Experten am Fraunhofer IPK qualifizieren das Laser-Pulver-Auftragschweißen für die additive Fertigung und legen dabei besonderen Wert auf die Automatisierung des Verfahrens.
Manganese alloyed stainless steels represent a cost-effective alternative to conventional CrNi- stainless steels due to strong fl uctuations of the market prices for nickel seen during the last years. In CrMnNi steels, nickel is partially replaced by lower-cost manganese and small amounts of nitrogen for stabilization of the austenitic phase. This also brings benefi ts regarding the mechanical properties, as it results in an increased material strength. Laser beam welding of such materials was investigated for direct comparison with Standard CrNi steels. Main emphasis was laid on fi nding adequate process parameters to achieve a stable welding process and obtain a good weld quality. Two different laser sources, a 4.4 kW Nd:YAG and a 5 kW CO2 laser, were used to weld 1.5 mm stainless steel sheets in continuous wave mode. A high-Mn austenitic (1.4376) and a lean duplex (1.4162) steel, as well as the standard austenitic (1.4301) and duplex (1.4362) grades were selected as test materials. Both butt and lap joint confi gurations were studied. Experiments were carried out systematically, varying the welding speed, laser power and focal point position in order to determine adequate process windows. The infl uence of the shielding gas type and fl ow rate on the process stability and the weld quality were investigated. The effects of weld edge preparation on the weld appearance and quality levels attained were also examined. The obtained welded joints were subjected to radiographic tests for detection of internal imperfections. Also a metallurgical characterization of the samples regarding the resulting phase composition or balance and hardness depending on the welding process parameters was conducted. Furthermore, tensile and potentiodynamic tests were performed to evaluate the mechanical and corrosion properties, respectively. The results provide an insight into the advantages and limitations of the laser beam welding process for joining high-manganese alloyed stainless steels. Conditions for the production of defect-free and corrosion-resistant welds having good mechanical properties could be determined.
The application of magnesium (Mg) inevitably involves dissimilar welding with steel. A novel solid state spot welding method, refill friction stir spot welding (refill FSSW), was utilized to weld AZ31 Mg alloy to galvanized DP600 steel. Although Mg/Fe is an immiscible alloy system, defect-free welds with high strength were successfully obtained in a wide parameter window. The results of microstructure, interfacial reactions, and mechanical properties are reported to reveal the underlying joining mechanism. Due to the melting of Zn coating and subsequent Mg-Zn reactions, Mg-Zn eutectic and intermetallic compounds were detected within welds. Heterogeneous interfacial reactions occur along Mg/steel interface, and the relationship between interfacial structure and fracture behavior was investigated. The joining mechanism is associated with Zn coating and Fe-Al layer: 1) the presence of Zn coating is beneficial for achieving high-quality welding between Mg and steel, it protects the interface from oxidation and contributes to brazing of the weld; 2) the Al present in Mg alloy reacts with Fe, resulting in the growth of Fe-Al layer, which contributes to the diffusion bonding in the interface. The overall results clearly show that Refill FSSW is a competitive welding method for joining Mg and galvanized steel.
Motivated by the loss of tensile strength in 9%Ni steel arc-welded joints performed using commercially available Ni-based austenitic filler metals, the viability of retaining tensile strength using an experimentally produced matching ferritic filler metal was confirmed. Compared to the austenitic Ni-based filler metal (685 MPa), higher tensile strength in gas metal arc (GMA) welded joints was achieved using a ferritic filler metal (749 MPa) due to its microstructure being similar to the base metal (645 MPa). The microstructure of hard martensite resulted in an impact energy of 71 J (-196 °C), which was two times higher than the specified minimum value of _>34 J. The
tensile and impact strength of the welded joint is affected not only by its microstructure, but also by the degree of its mechanical mismatch depending on the type of filler metal. Welds with a harder microstructure and less mechanical mismatch are important for achieving an adequate combination of tensile strength and notched impact strength. This is achievable with the cost-effective ferritic filler metal. A more desirable combination of mechanical properties is guaranteed by applying low preheating temperature (200 °C), which is a more practicable and economical solution compared to the high post-weld heat treatment (PWHT) temperature (580 °C) suggested by other research.
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.
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.
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 parameters. 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 %.
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.
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.
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.
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.
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.
Because of its excellent corrosion resistance, high tensile strength and high ductility, duplex stainless steel 2205 offers many areas of application. Though laser beam welding accompanied by high cooling rates, duplex steels tend to perform higher ferrite contents in weld metal as the base metal, which leads to a reduction of ductility and corrosion resistance of the weld joint. To overcome this problem, a solution, based on buttering the plate edges by laser metal deposition (LMD) with material containing higher Ni concentrations prior to laser welding was suggested.
In this context different process parameters for LMD process were investigated. In a second step the possibility of welding those edges defect free while achieving balanced austenite-ferrite ratio was verified with metallographic analysis, Electron Backscatter Diffraction (EBSD) and impact testing according to Charpy.