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Organisationseinheit der BAM
- 9.3 Schweißtechnische Fertigungsverfahren (236) (entfernen)
Eingeladener Vortrag
- nein (27)
Eine typische Automobilkarosserie kann bis zu 5000 Widerstandspunktschweißverbindungen aufweisen, welche hohen Qualitätsanforderungen genügen müssen. Daher ist eine durchgehende Prozessüberwachung unerlässlich. Die Transformation zur E-Mobilität in der Automobilindustrie und die damit einhergehende Reichweitenproblematik treiben die Entwicklung und Einführung neuer hochfester Stähle an. Dies resultiert in einem gesteigerten Fertigungsaufwand hinsichtlich einer stabilen Prozess-führung in der Fügetechnik. Um diesen Anstieg an Komplexität zu bewältigen, sind die Methoden der künstlichen Intelligenz ein geeignetes Mittel. Mit ihnen kann, durch Auswertung der Prozessparameter und -signale, die individuelle Schweißpunktqualität sichergesellt werden. Die Vorhersagegenauigkeit von neuen Daten, also das extrapolieren, stellt für die meisten Algorithmen eine große Herausforderung dar.
In dieser Arbeit wird ein künstliches neuronales Netz zur Vorhersage des Punktdurchmessers von Widerstandspunktschweißungen anhand von Prozessparametern implementiert. Die Vorhersagegenauigkeit und Extrapolationsfähigkeit des Modells wird durch die Auswertung des dynamischen Widerstandssignals verbessert.
Um die Extrapolationsfähigkeit zu untersuchen, wird die Vorhersagegenauigkeit des Modells mit Daten getestet, die sich in Bezug auf den Werkstoff und der Beschichtungszusammensetzung deutlich von den Trainingsdaten unterscheiden. Dazu wurden mehrere Schweißexperimente mit Werkstoffen verschiedener Hersteller durchgeführt und nur ein Teil der Daten in das Training einbezogen. Die Ergebnisse dieser Arbeit verdeutlichen den positiven Einfluss der Prozesssignale auf die Robustheit des Modells und die Skalierbarkeit der Algorithmen
künstlicher neuronaler Netze auf Daten außerhalb des Trainingsraums.
Durch pulverbasiertes Directed-Energy Deposition lassen sich Gradierungen fertigen, um diskrete Materialübergänge zu vermeiden und die Lebensdauer von Hartschichten zu erhöhen. Die Kombination aus Stahl als Basiswerkstoff und einer verschleiß- und korrosionsbeständigen Co-Cr Legierung verspricht durch Vermeiden von Spannungskonzentrationen das Verhindern von Abplatzungen und Rissen in der Schutzschicht. Um die Qualität des gefertigten Bauteils zu beurteilen, liegen für solche Funktional Gradierten Materialien (FGM) wenig Erkenntnisse vor. Daher wird im Rahmen dieser Studie eine Methodik erarbeitet, um die relative Dichte eines Funktional Gradierten Materials auf Stahl und Co-Cr Basis mittels Maschinendaten zu bestimmen. Anschließend wird unter Einsatz eines künstlichen neuronalen Netzes anhand von Sensordaten die relative Dichte vorhergesagt. Das trainierte Netz erreicht eine Vorhersagegenauigkeiten von 99,83%. Abschließend wird eine Anwendung anhand von einem Demonstrator gezeigt.
The amount of absorbed energy in the keyhole as well as its spatial and temporal distribution is essential to model the laser beam welding process. The recoil pressure, which develops because of the evaporation process induced by the absorbed laser energy at the keyhole wall, is a key determining factor for the macroscopic flow of the molten metal in the weld pool during high-power laser beam welding. Consequently, a realistic implementation of the effect of the laser radiation on the weld metal is crucial to obtain reliable and accurate simulation results.
In this paper, we discuss manyfold different improvements on the laser-material interaction, namely the ray-tracing method, in the numerical simulation of the laser beam welding process. The first improvement relates to locating the exact reflection points in the ray tracing method using a so-called cosine condition in the determination algorithm for the intersection of the reflected rays and the keyhole surface. A second correction refers to the numerical treatment of the Gaussian distribution of the laser beam, whose beam width is defined by a decay of the laser intensity by a factor of 1/e2 thus ignoring around 14 % of the total laser beam energy. In a third step, the changes in the laser radiation distribution in the vertical direction were adapted by using different approximations for the converging and the diverging regions of the laser beam thus mimicking the beam caustic. Finally, a virtual mesh refinement was adopted in the ray tracing routine. The obtained numerical results were validated with experimental measurements.
New developments in nickel-based superalloys and production methods, such as the use of additive manufacturing (AM), can result in innovative designs for turbines. It is crucial to understand how the material behaves during the AM process to advance industrial use of these techniques. An analytical model based on reaction-diffusion formalism is developed to better explain the solidification behavior of the material during laser metal deposition (LMD). The well-known Scheil-Gulliver theory has some drawbacks, such as the assumption of equilibrium at the solid-liquid interface, which is addressed by this method. The solidified fractions under the Scheil model and the pure equilibrium model are calculated using CALPHAD simulations. Differential scanning calorimeter is used to measure the heat flow during the solid-liquid phase transformation, the result of which is further converted to solidified fractions. The analytical model is compared with all the other models for validation.
AbstractLaser beam welding has become widely applied in many industrial fields in recent years. Solidification cracks remain one of the most common welding faults that can prevent a safe welded joint. In civil engineering, convolutional neural networks (CNNs) have been successfully used to detect cracks in roads and buildings by analysing images of the constructed objects. These cracks are found in static objects, whereas the generation of a welding crack is a dynamic process. Detecting the formation of cracks as early as possible is greatly important to ensure high welding quality. In this study, two end-to-end models based on long short-term memory and three-dimensional convolutional networks (3D-CNN) are proposed for automatic crack formation detection. To achieve maximum accuracy with minimal computational complexity, we progressively modify the model to find the optimal structure. The controlled tensile weldability test is conducted to generate long videos used for training and testing. The performance of the proposed models is compared with the classical neural network ResNet-18, which has been proven to be a good transfer learning model for crack detection. The results show that our models can detect the start time of crack formation earlier, while ResNet-18 only detects cracks during the propagation stage.
Im Additive Manufacturing Verfahren Directed Energy Deposition (DED) wird bei der Verarbeitung von Werkzeugstahl in der Regel reines Argon als Schutzgas verwendet. Dabei kann die Verwendung von speziellen Schutzgasgemischen, auch bei geringen Anteilen zugemischter Gase, durchaus die Bauteilqualität positiv beeinflussen.
In Vorarbeiten der Messer SE & Co. KGaA zeigte ein gewisser Sauerstoffanteil im Schutzgas die Tendenz, den Flankenwinkel von Schweißspuren beim DED zu verbessern. In der vorliegenden Studie wurde daher detailliert untersucht in wie weit unterschiedliche Schutzgasgemische einen Einfluss auf die Qualität sowie die geometrischen Eigenschaften der additiv gefertigten Strukturen des Werkzeugstahls 1.2709 beim Laser-DED ausüben. Es erfolgten zunächst Testschweißungen in Form von Einzelspuren mit unterschiedlichen Gemischen aus dem Basisschutzgas Argon mit geringen Anteilen verschiedener Gase. Dabei wurde der Einfluss der Zusätze auf die Spurgeometrie und Aufbauqualität untersucht. Auf Basis dieser Vorversuche wurde eine Auswahl vielversprechender Gasgemische getroffen und Detailuntersuchungen in Form von Spuren, Flächen und Quadern unter Zugabe verschiedener Mengen an Zusätzen durchgeführt. Zur Bewertung des Einflusses der Schutzgasbeimengungen wurden der Flankenwinkel, die Porosität und das Gefüge der Proben anhand metallografischer Schliffe untersucht. Es zeigte sich, dass eine Zugabe von geringen Anteilen an Zusätzen zunächst zu einer Vergrößerung des Flankenwinkels im Vergleich zu reinem Argon führt. Mit steigendem Anteil der Gase nimmt dieser Winkel jedoch ab. So kann je nach Menge des zugesetzten Gases eine individuelle Benetzung des aufgetragenen Materials an der Oberfläche erreicht werden. Auch die Porosität ließ sich durch Schutzgasgemische beeinflussen und zeigt ein abweichendes Verhalten im Vergleich zu reinem Argon.
Through experimental observation and auxiliary numerical simulation, this investigation studies the different types of grain refinement of 5754 aluminum alloy laser beam welding by applying a transverse oscillating magnetic field. Scanning electron microscope results have proved that the application of a magnetic field can reduce the average crystal branch width and increase its number. The interaction between the induced eddy current generated by the Seebeck effect and the applied external magnetic field produces a Lorentz force, which is important for the increase in the number of crystal branches. Based on the theory of dendrite fragmentation and the magnetic field-induced branches increment, the grain size reduction caused by the magnetic field is studied. Furthermore, the effects of the magnetic field are ana lyzed by combining a phase field method model and simulations of nucleation and grain growth. The grain distribution and average grain size after welding verify the reliability of the model. In addition, the introduction of a magnetic field can increase the number of periodic three-dimensional solidification patterns. In the intersection of two periods of solidification patterns, the metal can be re-melted and then re-solidified, which prevents the grains, that have been solidified and formed previously, from further growth and generates some small cel lular grains in the new fusion line. The magnetic field increases the building frequency of these solidification structures and thus promotes this kind of grain refinement.
Through experimental observation and auxiliary numerical simulation, this investigation studies the different types of grain refinement of 5754 aluminum alloy laser beam welding by applying a transverse oscillating magnetic field. Scanning electron microscope results have proved that the application of a magnetic field can reduce the average crystal branch width and increase its number. The interaction between the induced eddy current generated by the Seebeck effect and the applied external magnetic field produces a Lorentz force, which is important for the increase in the number of crystal branches. Based on the theory of dendrite fragmentation and the magnetic field-induced branches increment, the grain size reduction caused by the magnetic field is studied. Furthermore, the effects of the magnetic field are analyzed by combining a phase field method model and simulations of nucleation and grain growth. The grain distribution and average grain
size after welding verify the reliability of the model. In addition, the introduction of a magnetic field can increase the number of periodic three-dimensional solidification patterns. In the intersection of two periods of solidification patterns, the metal can be re-melted and then re-solidified, which prevents the grains, that have been solidified and formed previously, from further growth and generates some small cellular grains in the new fusion line. The magnetic field increases the building frequency of these solidification structures and thus promotes this kind of grain refinement.
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 amount of absorbed energy in the keyhole as well as its spatial and temporal distribution is essential to model the laser beam welding process. The recoil pressure, which develops because of the evaporation process induced by the absorbed laser energy at the keyhole wall, is a key determining factor for the macroscopic flow of the molten metal in the weld pool during high-power laser beam welding. Consequently, a realistic implementation of the effect of laser radiation on the weld metal is crucial to obtain reliable and accurate simulation results. In this paper, we discuss manyfold different improvements on the laser-material interaction, namely, the ray tracing method, in the numerical simulation of the laser beam welding process. The first improvement relates to locating the exact reflection points in the ray tracing method using a so-called cosine condition in the determination algorithm for the intersection of reflected rays and the keyhole surface. A second correction refers to the numerical treatment of the Gaussian distribution of the laser beam, whose beam width is defined by a decay of the laser intensity by a factor of 1/e2, thus ignoring around 14% of the total laser beam energy. In the third step, the changes in the laser radiation distribution in the vertical direction were adapted by using different approximations for the converging and the diverging regions of the laser beam, thus mimicking the beam caustic. Finally, a virtual mesh refinement was adopted in the ray tracing routine. The obtained numerical results were validated with experimental measurements.