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Paper des Monats
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A three-dimensional numerical model was developed to accurately predict the steady-state weld pool shape in full penetration laser beam welding. The model accounts for the coupling between the heat transfer and the fluid dynamics by considering the effects of solid/liquid phase transition, thermo-capillary convection, natural convection, and phase-specific and temperature-dependent material properties up to the evaporation temperature. A fixed right circular cone was utilized as a keyhole geometry to consider the heat absorbed from the laser beam. The model was used to analyze the influence of the thermodynamic activity of solutes and turbulence on the weld pool shape. A mesh sensitivity analysis was performed on a hybrid mesh combining hexahedral and tetrahedral elements. For the case of full penetration laser beam welding of 8 mm thick unalloyed steel sheets, the dependence of the weld pool shape on the surface-active element sulfur was found to be negligible. The analysis of the results showed that a laminar formulation is sufficient for accurately predicting the weld pool shape since the turbulence has a minor impact on the flow dynamics in the weld pool. The validity of the numerical results was backed up by experimental measurements and observations, including weld pool length, local temperature history, and a range of metallographic crosssections.
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.
This paper presents an automatic damage imaging technique by employing a signal processing approach based on applying hierarchically clustered filters across different domains. The technique involves time-frequency-wavenumber filter banks which are applied sequentially to ultrasonic guided wave (UGW) data. The study is conducted for a single lap joint composite specimen with a special focus on small voids which were formed due to manual adhesive component mixing. UGW data are acquired with a 3D Scanning Laser Doppler Vibrometer (LDV) over the scan area of the bonded plate. UGWs are excited at the central frequency of 100 kHz by a single piezoelectric transducer mounted on the surface of the single plate. Within each domain of time, frequency, and wavenumber, four filters are designed which results in 64 distinct filtered wavefields. From each filtered wavefield, an image is obtained by using root-mean-square (RMS) calculation of the signals. The obtained results are then combined to create a final, improved-resolution image of the scan area. The final image is compared to the image obtained through RMS calculation of full wavefield with interpolation through Delaunay triangulation and the image obtained by X-ray radiography. The results show that the smallest void that could be detected has a diameter of 2.14 mm.
Gas source localization (GSL) helps mitigate the impact of industrial accidents and natural disasters. While GSL may be dangerous and time-consuming when performed by humans, swarms of agile and inexpensive nano aerial robots may increase the safety and efficiency of gas source localizations. Since the small payloads of nano aerial robots limit the sensing and computing resources, strategies adapted from biological swarms, such as colonies of social insects, are used to coordinate robot swarms. Most swarm GSL strategies are based on the assumption that the maxima of gas concentrations are sufficiently close to the gas sources. However, prior studies have indicated that the occurrence of “bouts”, a metric for the intermittency of gas distributions, may advantageously be used as a more accurate gas source proximity indicator. This paper presents a swarm GSL strategy employing bouts as source proximity indicators and a bio-inspired pheromone system for communication. Nano aerial robots, deployed in this study, act as agents and emit pheromone markers in an artificial environment upon detecting bouts. Leveraging the concept of artificial potential fields, the agents switch between exploiting the knowledge of the swarm by following pheromone gradients and exploring the search space by targeting a random point. The agents are repelled by each other and by walls to avoid collisions. The swarm GSL strategy is implemented into three nano aerial robots and validated in a real-world experiment in an indoor environment with a single gas source. The results indicate that the the swarm GSL strategy presented in this paper is capable of GSL in indoor environments and that the intermittency of gas distributions is a better source proximity indicator than the mean concentration.
Liquefied Hydrogen (LH2) or Liquefied Natural Gas (LNG) establish themselves as important energy carriers in the transport sector. Its storage requires tanks with Thermal Super Insulations (TSI) to keep the transported fluid cold. TSI has proven itself in various applications over a long time, but not in the land transport sector, where accidents involving collisions, fires, and their combination are to be expected. The focus of this study is to investigate the behavior of different types of TSI when exposed to a heat source that represents a fire. Therefore, a High-Temperature Thermal Vacuum Chamber (HTTVC) was used that allows the thermal loading of thermal insulation material in a vacuum and measuring the heat flow through the TSI in parallel. Within this study, 5 samples were tested regarding 3 different types of MLI, rock wool, and perlites. The thermal exposure caused different effects on the samples. In practice, this can be connected to the rapid release of flammable gases as well as a Boiling Liquid Expanding Vapour Explosion (BLEVE). These results are relevant for the evaluation of accident scenarios, the improvement of TSI, and the development of emergency measures.
The spatial laser energy absorption inside the keyhole is decisive for the dynamic molten pool behaviors and the resultant weld properties in high-power laser beam welding (LBW). In this paper, a numerical simulation of the LBW process, considering the 3D transient heat transfer, fluid flow, and keyhole dynamics, is implemented, in which the free surface is tracked by the volume-of-fluid algorithm. The underlying laser-material interactions i.e., the multiple reflections and Fresnel absorption, are considered by an advanced ray-tracing method based on a localized Level-Set strategy and a temperature-dependent absorption coefficient. The laser energy absorption is analyzed from a time-averaged point of view for a better statistical representation. It is found for the first time that a noticeable drop of the time-averaged laser energy absorption occurs at the focus position of the laser beam, and the rest region of the keyhole has relatively homogenous absorbed energy. This unique absorption pattern may lead to a certain keyhole instability and have a strong correlation with the detrimental bulging and narrowing phenomena in the molten pool. The influence of the different focus positions of the laser beam on the keyhole dynamics and molten pool profile is also analyzed and compared. The obtained numerical results are compared with experimental measurements to assure the validity of the proposed model.
The RILEM TC 281–CCC "Carbonation of concrete with supplementary cementitious materials" is studying the effect of supplementary cementitious materials (SCMs) on natural and accelerated carbonation of blended cements. In this context a large database with carbonation depths over time has been set-up with currently 910 concrete and mortar mixes, which have been tested at different curing conditions. The collected data includes information on chemical composition and physical properties of the raw materials, on mix-design, compressive strength, curing and carbonation testing conditions. Data for a large variety of binders (with up to 94 % SCMs) and mix designs (with w/b ratios ranging from 0.26 to 0.75) were collected from the literature and unpublished material testing projects. Natural carbonation was recorded for several years in many cases, and indoor as well as outdoor results are included. However, in this contribution only lab conditions are considered. Carbonation kinetics are investigated considering the binder composition and mix design. Furthermore, the accuracy of accelerated carbonation testing is evaluated. As described in literature, the w/CaOreactive-ratio is a decisive factor for carbonation resistance but curing and exposure conditions influence the results as well. Even with identical test conditions, the scatter is very large.
Aschen und Schlacken aus der thermischen Abfallbehandlung enthalten eine Vielzahl von Schwermetallverbindungen. Für Antimon, Chrom, Kupfer, Molybdän und Vanadium werden in der neuen Ersatzbaustoffverordnung (Bundesregierung, 2021) Eluatgrenzwerte für die Verwertung festgelegt. Aschen und Schlacken werden aus der Abfallverbrennungsanlage nass ausgetragen und vor einer Verwertung in der Regel mehrere Wochen gelagert. Dabei finden hydraulische Reaktionen statt und enthaltenes CaO wird zu einem Großteil in Calciumcarbonat umgewandelt. Während wässrige Eluate von frischen Aschen pH-Werte von 12 und höher aufweisen, sind die Eluate nach einigen Wochen weniger stark alkalisch. Die gemessenen Schwermetallkonzentrationen sind daher dann auch deutlich niedriger, mit Ausnahme von Antimon (Sb) und Vanadium (V). Hier steigen die Konzentration mit der Lagerungsdauer. Grund sind sinkende Konzentrationen von Ca-Ionen, die sonst zu einer Ausfällung von Antimonaten und Vanadaten führen. Zu hohe Eluatwerte von Sb und V können jedoch durch Sorption an Eisenoxidverbindungen gemindert werden. Dazu wurden Sorptionsexperimente mit verschiedenen Eisenoxidverbindungen durchgeführt.
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.