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- 2023 (31) (entfernen)
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- Beitrag zu einem Tagungsband (15)
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- Laser beam welding (14)
- Numerical modeling (5)
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- Deep penetration laser beam welding (3)
- Grain refinement (3)
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- Periodic solidification pattern (3)
- AM (2)
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- 9 Komponentensicherheit (31) (entfernen)
Eingeladener Vortrag
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It is proposed to model the experimentally observed weld pool boundary with superellipses (Lamé curves) and to find the unknown parameters of the curves using optimization methods. It has been shown experimentally that during laser welding of austenitic stainless steel with a thickness of 2 mm at a speed of 20 mm/s, the rear weld pool part has a shape close to triangular which can be accurately approximated by a superellipse. Analytical dependences of the trajectory and growth rate of the crystal and its cross-sectional area on the geometry of the rear weld pool part are obtained.
Die Additive Fertigung gewinnt zunehmend an Bedeutung für die Verarbeitung von Kupferwerkstoffen im industriellen Umfeld. Hierbei wird verstärkt auf drahtförmige Ausgangswerkstoffe gesetzt, da diese Vorteile im Handling bieten, bereits aus der Schweißtechnik bekannt sind und sich zumeist durch geringere Beschaffungskosten auszeichnen. In den letzten Jahren entwickelte sich unter den drahtbasierten Verfahren der Directed-Energy-Deposition (DED) eine Prozessvariante unter Nutzung des Elektronenstrahls zur industriellen Marktreife. Dabei zeigt die Technologie Wire Electron Beam Additive Manufacturing (DED-EB) besondere Vorteile gegenüber anderen DED-Prozessen für die Anwendung an Kupfer. Um das Verfahren einem breiten Anwenderkreis in der Industrie zugänglich zu machen, fehlen jedoch Daten zu Leistungsfähigkeit, Prozessgrenzen und Anwendungsmöglichkeit- en. Die vorliegende Untersuchung beschäftigt sich mit dieser Problemstellung am Beispiel der Legierung CuSn1MnSi. Über mehrstufige Testschweißungen werden die physikalisch möglichen Prozessgrenzen ermittelt und Rückschlüsse über die Eignung der Parameter zum additiven Aufbau gezogen. An verschiedenen additiv gefertigten Probekörpern werden anschließend Kennwerte für Aufbaurate, Härte, Mikrostruktur, Oberflächenqualität sowie mechanische Festigkeitswerte ermittelt. Es zeigt sich, dass das die durch DED-EB hergestellten Proben, trotz des groben Gefüges sowie der thermischen Belastung im Aufbauprozess, in ihren Eigenschaften gut mit den Spezifikationen des Ausgangsmaterials übereinstimmen.
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.
Abstract
In the post-processing of large maritime components, a considerable amount of waste in the form of milling and grinding chips is produced. At the same time, additive manufacturing technologies have shown great potential in producing high-volume parts for maritime applications, allowing novel design approaches and short lead times. In this context, this study presents a sustainable approach to recycle and use aluminium bronze waste material, generated during post-processing of large cast ship propellers, as feedstock for laser-powder directed energy deposition. The recycling technology used to produce powder batches is inductive re-melting in combination with ultrasonic atomization. The derived metal powders are characterized using digital image analysis, powder flowability tests, scanning electron microscopy as well as energy dispersive X-ray spectroscopy. Compared to conventional metal powders produced by gas atomization, the recycled material shows excellent sphericity and a powder size distribution with a higher content of finer and coarser particles. Metallographic sections of deposited additively produced specimens show an increased hardness and reduced ductility, but also competitive densities and higher yield and ultimate tensile strength compared to cast material. The process chain shows high potential for the maritime sector to enable circular and sustainable manufacturing.
Liquid metal embrittlement (LME) cracking is a phenomenon observed during resistance spot welding (RSW) of zinccoated advanced highstrength steels (AHSS) in automotive manufacturing. In this study, severe cracks are observed at the edge of the sheet under reduced flange widths. These cracks, traversing the AHSS sheet, culminate at the edge with a width of approximately 1.2 mm.
Through combined numerical and experimental investigations, and material testing, these cracks are identified and validated as a new type of LME crack. The mechanism behind this crack formation is attributed to unique geometric conditions that, when compared to center welding, amplify radial material flow by ninefold to 0.87 mm. The resultant tangential tensile stresses approximate 760 MPa, which exceed the yield strength of the examined advanced highstrength steel (AHSS) under heightened temperature conditions, and when combined with liquid zinc, promote the formation of this new type of LME crack.
The present work deals with the recently confirmed widening of the weld pool interface, known as a bulging effect, and its relevance in high power laser beam welding. A combined experimental and numerical approach is utilized to study the influence of the bulge on the hot cracking formation and the transport of alloying elements in the molten pool. A technique using a quartz glass, a direct-diode laser illumination, a high-speed camera, and two thermal imaging cameras is applied to visualize the weld pool geometry in the longitudinal section. The study examines the relevance of the bulging effect on both, partial and complete penetration, as well as for different sheet thicknesses ranging from 8 mm to 25 mm. The numerical analysis shows that the formation of a bulge region is highly dependent on the penetration depth and occurs above 10 mm penetration depth. The location of the bulge correlates strongly with the cracking location. The obtained experimental and numerical results reveal that the bulging effect increases the hot cracking susceptibility and limits the transfer of alloying elements from the top of the weld pool to the weld root.
The strain field can reflect the initiation time of solidification cracks during the welding process. The traditional strain measurement is to first obtain the displacement field through digital image correlation (DIC) or optical flow and then calculate the strain field. The main disadvantage is that the calculation takes a long time, limiting its suitability to real-time applications. Recently, convolutional neural networks (CNNs) have made impressive achievements in computer vision.
To build a good prediction model, the network structure and dataset are two key factors. In this paper, we first create the training and test sets containing welding cracks using the controlled tensile weldability (CTW) test and obtain the real strain fields through the Lucas–Kanade algorithm. Then, two new networks using ResNet and DenseNet as encoders are developed for strain prediction, called StrainNetR and StrainNetD. The results show that the average endpoint error (AEE) of the two networks on our test set is about 0.04, close to the real strain value. The computation time could be reduced to the millisecond level, which would greatly improve efficiency.
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.
Numerical study on the formation of a bulging region in partial penetration laser beam welding
(2023)
A transient three-dimensional thermo-fluid dynamics numerical model was developed to study the formation of a bulging region in partial penetration laser beam welding. The model accounts for the coupling between the fluid flow, the heat transfer, and the keyhole dynamics by considering the effects of multiple reflections and Fresnel absorption of the laser beam in the keyhole, the phase transitions during melting and evaporating, the thermo-capillary convection, the natural convection, and the phase-specific and temperature-dependent material properties up to the evaporation temperature. The validity of the model was backed up by experimentally obtained data, including the drilling time, the weld pool length, the local temperature history outside the weld pool, the process efficiency, and a range of metallographic crosssections.
The model was applied for the cases of partial penetration laser beam welding of 8 mm and 12 mm thick unalloyed steel sheets. The obtained experimental and numerical results reveal that the bulging region forms transiently depending on the penetration depth of the weld, showing a tendency to transition from a slight bulging to a fully developed bulging region between penetration depths of 6 mm and 9 mm, respectively.
Numerical analysis of ultrasonic vibration enhanced friction stir welding of dissimilar Al/Mg alloys
(2023)
The ultrasonic vibration enhanced friction stir welding (UVeFSW) process has unique advantages in joining dissimilar Al/Mg alloys. While there are complex coupling mechanisms of multi-fields in the process, it is of great significance to model this process, to reveal the influence mechanism of ultrasonic vibration on the formation of Al/Mg joints.
In this study, the acoustic-plastic constitutive equation was established by considering the influence of both ultrasonic softening and residual hardening on the flow stress at different temperatures and strain rates. And the ultrasonic induced friction reduction (UiFR) effect on friction coefficient in different relative directions at the FSW tool-workpiece interface was quantitatively calculated and analyzed.
The Al/Mg UVeFSW process model was developed through introducing the above acoustic effects into the model of Al/Mg friction stir welding (FSW). The ultrasonic energy is stronger on the aluminum alloy side. In the stirred zone, there is the pattern distribution of ultrasonic sound pressure and energy. The heat generation at the tool-workpiece contact interface and viscous dissipation were reduced after applying ultrasonic vibra-tion. Due to the UiFR effect, the projection of friction coefficient and heat flux distributions at the tool-workpiece interface present a "deformed" butterfly shape. The calculated results show that ultrasonic vibra-tion enhanced the material flow and promoted the mixing of dissimilar materials.