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Eingeladener Vortrag
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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.
Additive Fertigungsverfahren, speziell das selektive Laserschmelzen sowie das Laserpulverauftragsschweißen, ermöglichen eine enorme Steigerung der Flexibilität und erlauben Kleinserienteile mit hoher Genauigkeit und geringen Kosten herzustellen.
Für den erfolgreichen wirtschaftlichen Einsatz dieser neuartigen Fertigungsverfahren spielt die Einhaltung des First-time-right-Prinzips eine entscheidende Rolle: Bauteile sollten bereits im ersten Versuch allen Anforderungen genügen. Aufgrund der jungen Geschichte dieses Fertigungszweigs und der damit einhergehenden fehlenden Erfahrungen und Richtlinien ist diese elementare Forderung heute nur in wenigen Fällen realisierbar. Die geforderten Qualitätsstandards können aktuell nur über experimentelle Iterationsschleifen eingehalten werden, sodass das große Potential einer flexiblen und schnellen Fertigung in erheblichem Maß reduziert wird. Die Komplexität der gefertigten Bauteile und die des Prozesses an sich lassen eine erfahrungsbasierte Vorhersage der Verzüge und Eigenspannungen kaum zu. Zudem werden auch in Zukunft Richtlinien und Normen nicht das komplette Anwendungsspektrum abbilden können. Die eigenspannungsbedingten Verzüge spielen demnach eine bedeutende Rolle und stellen zusammen mit dem Erreichen der Maßhaltigkeit eine entscheidende technologische Herausforderung beim Einsatz additiver Fertigungsverfahren dar. Die numerische Simulation ermöglicht die Vorhersage von Bauteilverzügen und –spannungen und kann durch virtuelle Abprüfung von Herstellstrategien die Anzahl von Experimente reduzieren. Bisherige numerische Betrachtungen von zusatzwerkstoffbasierten Verfahren, zu denen unter anderem das Laserpulverauftragschweißen (LPA) gehört, beschränkten sich primär auf akademische Beispiele mit geringer Komplexität. Für die Simulation von konkreten Anwendungsfällen auf Bauteilebene liegen bisher keine validierten, numerischen Methoden und Ansätze vor, die eine wirtschaftliche Anwendung der Schweißsimulation ermöglichen.
Dieses Projekt wird Simulationsmodelle zur numerischen Betrachtung komplexer additiv gefertigter Bauteile entwickeln. Dafür wird der Prozess in vereinfachten Simulationen nachgebildet und anhand von Experimenten validiert. Anschließend werden Methoden zur automatisierten Pfadgenerierung für komplexe Bauteile erprobt und in der Simulation implementiert. Schließlich werden zur Reduktion der Rechenzeit verschiedene Methoden zur Vereinfachung evaluiert und verglichen. Das Ziel ist die Steigerung der Verlässlichkeit in der Simulation, um prädiktive Aussagen über die Qualität additiv gefertigter Bauteile zu ermöglichen.
Der Einsatz von hochfesten Stählen im Karosseriebereich des Automobilbaus hat während der letzten Jahre stark zugenommen. Hierzu zählen Dual- und Komplexphasenstähle, welche durch Kombination unterschiedlicher Gefügebestandteile auch deren Vorteile kombinieren, sowie TRIP (TRansformation Induced Plasticity) und Mangan-Bor Stähle, welche sehr gute Umformeigenschaften mit hohen Festigkeiten durch Martensitbildung bei der Umformung kombinieren. TWIP (Twinning Induced Plasticity) Stähle erreichen ähnliche Effekte durch forcierte Zwillingsbildung.
Die Ursachen für den Einsatz dieser Stähle liegen in dem Potential dieser Materialien zur Gewichts- und Kostenreduzierung, bei gleichzeitiger Erhöhung der Fahrgastsicherheit. Auf Grund der prinzipiell gegebenen Schweißeignung dieser Stähle, werden die klassischen Fügeverfahren im Karosseriebau wie das kostengünstige und effektive Widerstandspunktschweißen, das Metall-Schutzgas (MSG)-Schweißen oder das Laserschweißen angewendet. Allerdings treten teilweise Herausforderungen, beispielsweise durch Gefügeveränderungen in den Fügestellen auf, die zu ungewollten Aufhärtungen oder Erweichungen führen.
In diesem Projekt wird ein Verfahren entwickelt, mit welchem die lokalen Werkstoffeigenschaften von im Automobilbau typischen Werkstoffen und deren Fügestellen bestimmt werden können. Relevante Kennwerte sind in erster Linie das SpannungsDehnungs-Verhalten der verschiedenen Zonen einer Schweißverbindung; relevante Zonen wiederum sind neben dem Grundwerkstoff die Wärmeeinflusszone und das Schweißgut. Zu diesem Zweck wird das Verfahren der instrumentierten Eindringprüfung für den Einsatz bei hochfesten Stählen weiterentwickelt. Zunächst werden hierzu Zugversuche an einfachen Grundwerkstoffgeometrien durchgeführt. Im Anschluss wird die optische Dehnungsfeldmessung an stark taillierten, geschweißten Zugversuchsproben durchgeführt.
Die Taillierung dient dem Zweck, die WEZ auch mittels WPS über den gesamten Querschnitt der Probe erzeugen zu können, bzw. im Versuch auch Dehnungen in den relevanten Bereichen herbeizuführen.
Das im Projekt angewendete Auswerteverfahren, welches auf nichtlinearen Regressionsmodellen in Form von künstlichen, neuronalen Netzwerken beruht, ermöglicht die Vorhersage des Festigkeitsverhaltens des Werkstoffes anhand der gemessenen Krafteindringwegdaten.
Prognose von Qualitätsmerkmalen durch Anwendung von KI-Methoden beim Directed 10 Energy Deposition
(2022)
Dieser Beitrag enthält die Ergebnisse eines im Rahmen der DVS Forschung entwickelten Ansatzes zur Qualitätssicherung im Directed Energy Deposition. Es basiert auf der Verarbeitung verschiedener während des Prozesses gesammelter Sensordaten unter Anwendung Künstlicher Neuronale Netze (KNN). So ließen sich die Qualitätsmerkmale Härte und Dichte auf der Datenbasis von 50 additiv gefertigten Probenwürfel mit einer Abweichung < 2 % vorhersagen. Des Weiteren wurde die Übertragbarkeit des KNN auf eine Schaufelgeometrie untersucht. Auch hier ließen sich Härte und Dichte hervorragend prognostizieren (Abweichung < 1,5 %), sodass der Ansatz als validiert betrachtet werden kann.
In recent years, in addition to the commonly known wire-based processes of Directed Energy Deposition using lasers, a process variant using the electron beam has also developed to industrial market maturity. The process variant offers particular potential for processing highly conductive, reflective or oxidation-prone materials. However, for industrial usage, there is a lack of comprehensive data on performance, limitations and possible applications. The present study bridges the gap using the example of the high-strength aluminum bronze CuAl8Ni6. Multi-stage test welds are used to determine the limitations of the process and to draw conclusions about the suitability of the parameters for additive manufacturing. For this purpose, optimal ranges for energy input, possible welding speeds and the scalability of the process were investigated. Finally, additive test specimens in the form of cylinders and walls are produced, and the hardness profile, microstructure and mechanical properties are investigated. It is found that the material CuAl8Ni6 can be well processed using wire electron beam additive manufacturing. The microstructure is similar to a cast structure, the hardness profile over the height of the specimens is constant, and the tensile strength
and elongation at fracture values achieved the specification of the raw material.
Advanced high strength steels are usually coated by a zinc layer for an increased resistance against corrosion. During the resistance spot welding of zinc coated steel grades, liquid metal embrittlement (LME)mayoccur. As a result, cracking inside and around the spot weld indentation is observable. The extent of LME cracks is influenced by a variety of different factors. In this study, the impact of the used electrode geometry is investigated over a stepwise varied weld time.
A spot welding finite element simulation is used to analyse and explain the observed effects. Results show significant differences especially for highly increased weld times. Based on identical overall dimensions, electrode geometries with a larger working plane allow for longer weld times, while still preventing LME within the investigated material and maintaining accessibility.
Laser metal deposition (LMD) as an additive manufacturing technique became increasingly important in recent years and thus the demand for component safety. This is the reason, for the need for reliable in-situ defect detection techniques. For laser beam weld seams an optical measurement technique based on an optical flow algorithm was successfully used to define the critical straining conditions that lead to hot cracking. This algorithm was adapted for bead-on-plate weld seams on LMD deposited layers of IN718 alloy while performing external strain on the specimen in an externally loaded hot cacking test facility. The resulting transversal hot cracks along the weld seam were localized via X-Ray inspection and the type of cracking confirmed by Scanning Electron Microscopy (SEM). The strain distribution was measured in the vicinity of the solidification front and correlated to the detected hot cracks. Based on the results this technique could be adopted for LMD experiments.
Pores in additive manufactured metal parts occur due to different reasons and affect the part Quality negatively. Few investigations on the origins of porosity are available, especially for Ni-based super alloys. This paper presents a new study to examine the influence of common processing Parameters on the Formation of pores in parts built by laser metal Deposition using Inconel 718 powder. Further, a comparison between the computed tomography (CT) and the Archimedes method was made. The Investigation Shows that CT is able to identify different kinds of pores and to give further Information about their distribution. The identification of some pores as well as their shape can be dependent on the Parameter Setting of the Analysis tool. Due to limited measurement Resolution, CT is not able to identify correctly pores with Diameters smaller than 0.1 mm, which leads to a false decrease on Overall porosity. The applied Archimedes method is unable to differentiate between gas porosity and other Kinds of holes like internal cracks or lack of Fusion, but it delivered a proper value for Overall porosity. The method was able to provide suitable data for the statistical Evaluation with design of Experiments, which revealed significant Parameters ont he Formation of pores in LMD.
Electromagnetic stirring is known to promote material flow, reduce porosity, uniform elements distribution, and refine grain in laser beam welding (LBW), which enhances the applicability of LBW in various industries. In this study, a phase-field model of dendrite growth in AA5754 Al alloy electromagnetic stirring laser beam welding was established. The model considered the thermal electromagnetic Lorentz force resulting from the interaction between the electric field generated by the Seebeck effect and the magnetic field, as well as the temperature gradient and solidification rate of the solidification interface obtained from the computational fluid dynamics electromagnetic stirring LBW model. The variation rules of dendrite growth with different magnetic parameters and effects are analyzed. Comprehensively, the magnetic field promotes the solidification rate, thus promoting interfacial instability and a large magnetic flux density leads to a faster interface instability. The solidification rate as well as the temperature gradient affect the growth rate, and the accelerated growth caused by the so lidification rate with a high frequency and a large magnetic flux density effectively inhibits the slow growth caused by the temperature gradient. The thermal electromagnetic Lorentz force is the main factor for the branch increment at low frequencies, while both thermal electromagnetic Lorentz force and temperature gradient in crease the number of branches at high frequencies. The calculated average branch numbers considering various factors in the stable stage under different magnetic parameters were consistent with the results of the scanning electron microscope tests.
The safe joining of new, freshly developed steel types keeps challenging the industry–and is assumed to go on in doing so. In the body-in-white production, these materials are mainly joined using resistance spot welding. During the past ten years, various investigations on resistance spot welding of advanced high strength steels have been carried out at Fraunhofer IPK and the Federal Institute for Materials Research and Testing (BAM). This paper aims to give an overview about both the top former and recent studies and results. The investigated topics are the influences of manufacturing conditions as initial gaps and restraints on spot welds, their impact on the fatigue strength of a joint and the cracking-wise safe weldability of AHSS.
Caused by non-ideal manufacturing conditions, cracks in spot welds (e.g. caused by liquid metal embrittlement) are still regarded as a potential risk in industrial practice. Therefore, a method to evaluate the safe weldability regarding the cracking susceptibility was developed for AHSS. The method is easy to perform, even without expensive laboratory equipment. It allows the end user to establish a material ranking regarding the cracking susceptibility of the handled steels. Recently, coupled thermo-mechanical finite element modelling has been used to describe critical stress-strain conditions responsible for the occurrence of liquid metal embrittlement, and to improve the understanding of the process.
Recently developed fibre lasers provide multi-kilowatt beam power with high quality at impressive energy efficiency. Combined with gas metal arc welding (GMAW) equipment these lasers can be used in a hybrid process to weld thick-walled constructions single-pass, that are currently welded using multi-pass techniques. The main benefits are a reduction of heat induced distortions, due to the low heat input, as well as savings in filler material and process time. Probable applications can be found in power generation, ship building and pipeline constructions. An orbital (girth) laser-hybrid process using a 20 kW fibre laser and a GMAW torch is currently examined at the BAM, Berlin. The aim of this research is to obtain a stable and crack free girth welding process and to demonstrate its application in pipeline construction. The experiments are carried out on 16 mm thick plates as well pipe rings with 36" (914 mm) pipe diameter of X65. Particular welding parameters, such as welding speed, GMAW power, arc length are varied and their influence on the appearance of the weld in the different welding positions is analyzed. Even though issues remain that demand further research it could already be shown that the rings can be welded using a girth hybrid process that is divided into two half girth processes in downward direction.