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Eingeladener Vortrag
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Grain boundary (GB) chemical and structural variations can significantly influence materials performance. The former is generally ascribed to the structural gradient between the grain and GB. While GB segregation may be accompanied by chemical and structural variations, clear insights about the GB’s thermodynamic phase behaviour upon coupling between its chemistry and structure is lacking. Using the CALPHAD integrated density-based phase field model, we study the co-evolution of GB’s structure and segregation in Fe-Mn alloys. We found that the GB segregation transition is amplified if its structure can respond to chemical variation. Additionally, the coupling between GB structural and segregation evolution was found to enable co-existence of the spinodally formed low- and high-Mn phases within the GB. In the light of atomistic simulations, we expand on investigating the correlation between the parameters that characterise the GB density map with GB properties.
Recent advances in Deep Learning (DL) have significantly impacted the field of materials modelling. DL tools have been recently considered as promising tools to address the complex relationships among processing, microstructure and property of materials. The thermal stability of polycrystalline materials is a highly interesting and complex problem that could be addressed using DL techniques. The Multi-Phase-Field (MPF) method has emerged as a powerful tool for addressing grain growth phenomena from multiple perspectives. Unlike sharp-interface based methods, the MPF approach bypasses the need for detailed information on individual grains. In this work, we use DL to address issues related to MPF simulations of grain growth including numerical efficiency, computing speed, and resource consumption. This presents specific challenges for high-performance computing (HPC) due to the large datasets and complex computations required by both MPF and DL methods. We study various 3D microstructure settings with the goal of accelerating the simulation process while exploring different physical effects. In particular, the impact of grain boundary and triple junction energies on grain growth are to be investigated. The results will be presented in terms of the evolving size and shape distribution of the grains.
Manipulating ceramic powder compacts and ceramic suspensions (slurries) within their volume with light requires a minimum transparency of the materials. Compared to polymers and metals, ceramic materials are unique as they offer a wide electronic band gap and thus a wide optical window of transparency. The optical window typically ranges from below 0.3 µm up to 5µm wavelength. Hence, to penetrate with laser light into the volume of a ceramic powder compound its light scattering properties need to be investigated and tailored. In the present study we introduce the physical background and material development strategies to apply two-photon-polymerization (2PP) for the additive manufacture of filigree structures within the volume of ceramic slurries.
We report about a joint project aiming at the digitalization of a lab course in materials testing. The undergraduate students were asked to prepare samples of a precipitation hardened aluminum alloy and characterize them using hardness and tensile tests. In a first step, we developed the frames for the digital labor notebook using eLabFTW. The primary data and the relevant metadata of each run were saved in a central database and made available for analysis and report issues. The whole set of results produced in a course was made available in the database. This database can be improved and serve as an open repository for data on this specific alloy.
The logical frame for the joint project was provided by the PMD Core Ontology (PMDco), a mid-level ontology that enables the representation and description of processes and process chains in an MSE-specific manner, ensuring full traceability of generated data. For the digitalization of this lab course, the tensile test ontology (TTO) was applied which is designed as a module of the PMDco using strongly related semantic concepts.
Das hochaktuelle Thema der Integration und Wiederverwendung von Wissen und Daten aus Herstellung, Bearbeitung und Charakterisierung von Materialien wird im Zuge der digitalen Transformation in der Materialwissenschaft und Werkstofftechnologie in verschiedenen Projekten adressiert. Dabei stehen die Interoperabilität von nach FAIR-Prinzipien erstellten und veröffentlichten Daten und Anwendungen im Vordergrund. Zur Umsetzung eines komplexen Datenmanagements sowie der Digitalisierung im Bereich der Materialwissenschaften etablieren sich Ontologien zunehmend als belastbares Werkzeug. Sie ermöglichen sowohl menschenlesbare als auch maschinenverständliche und -interpretierbare Wissensrepräsentationen durch semantische Konzeptualisierungen.
Im Rahmen des Projektes Plattform MaterialDigital (PMD, materialdigital.de) werden Ontologien verschiedener Ebenen entwickelt (verbindende mid-level sowie Domänen-Ontologien). Die PMD-Kernontologie (PMD Core Ontology - PMDco) ist eine Ontologie der mittleren Ebene), die Verbindungen zwischen spezifischeren MSE-Anwendungsontologien und domänenneutralen Konzepten herstellt, die in bereits etablierten Ontologien höherer Ebenen (top-level Ontology) verwendet werden. Sie stellt somit einen umfassenden Satz von durch Konsensbildung in der Gemeinschaft (geteiltes Vokabular) entstanden Bausteinen grundlegender Konzepte aus der Materialwissenschaft und Werkstofftechnik (MSE) dar. Das primäre Ziel des PMDco-Designs ist es, die Interoperabilität zwischen verschiedenen MSE-bezogenen und anderen Ontologien zu ermöglichen. Die PMDco dient als umfassend ausgelegte und erweiterbare semantische Zwischenschicht, die gemeinsame MSE-Konzepte durch semantisches Mapping auf andere Darstellungen vereinheitlicht, was sie zu einem effizienten Werkzeug zur Strukturierung von MSE-Wissen macht. In dieser Präsentation wird die Version 2.0 der PMDco vorgestellt.
Weiterhin wird eine auf die PMDco bezogene normenkonforme ontologische Repräsentation zur Speicherung und Weiterverarbeitung von Zugversuchsdaten präsentiert, die in enger Zusammenarbeit mit Standardisierungsgremien erstellt wurde. Dies umfasst den Weg von der Entwicklung einer Ontologie nach Norm, der Konvertierung von Daten aus Standardtests in das interoperable RDF-Format bis hin zur Verbindung von Ontologie und Daten. Letztendlich können die entsprechenden Daten in einem Triple Store abgelegt und abgefragt werden. Auf Basis dieser Zugversuchsontologie wurde im Folgenden unter Verwendung eines elektronischen Laborbuches (electonic lab notebook – ELN) zur Datenaufnahme eine Praktikumsreihe von Universitätsstudierenden digitalisiert. Dadurch wurde eine vollständig digital integrierte Versuchsführung ermöglicht, die auf andere Versuchsreihen und Experimente übertragbar ist und ebenfalls vorgestellt werden soll. Neben einer erleichterten Aufnahme, Analyse und (Wieder-)Verwendbarkeit von Daten wird damit eine Sensibilisierung von Studierenden für Datenstrukturierung sowie semantische Technologien im Sinne der Aus- und Weiterbildung erreicht.
Suitable material solutions are of key importance in designing and producing components for engineering systems – either for functional or structural applications. Materials data are generated, transferred, and introduced at each step along the complete life cycle of a component. A reliable materials data space is therefore crucial in the digital transformation of an industrial branch.
A great challenge in establishing a materials data space lies in the complexity and diversity of materials science and engineering. It must be able to handle data from different knowledge areas over several magnitudes of length scale.
The Platform MaterialDigital (PMD) is expected to network a large number of repositories of materials data, allowing the direct contact of different stakeholders as materials producers, testing labs, designers and end users. Following the FAIR principles, it will promote the semantic interoperability across the frontiers of materials classes. In the frame of a large joint initiative, PMD
works intensively together with currently near 20 research consortia in promoting this exchange (www.material-digital.de).
In this presentation we will describe the status of our Platform MaterialDigital. We will also present in more detail the activities of GlasDigital, one of the joint projects mentioned above dealing with the digitalization of glass design and manufacturing.
(https://www.bam.de/Content/EN/Projects/GlasDigital/glasdigital.html)
Nickel-based alloys have been widely used for gas turbine blades owing to their excellent mechanical properties and corrosion resistance at high temperatures. The operating temperatures of modern gas turbines have been increased in pursuit of increased thermal efficiency. Turbine blades are exposed to these high temperatures combined with mechanical stresses, resulting in material damage through creep, fatigue, and other mechanisms. These turbine blades must be regularly inspected and replaced as needed, to prevent the loss of efficiency, breakdown, and catastrophic failure. Repair of the damaged turbine blades is often a more practical and cost-effective option than replacement, as replacement is associated with high costs and loss of material resources. To this end, state-of-the-art repair technologies including different additive manufacturing and brazing processes are considered to ensure efficient repair and optimum properties of repaired components.
In any repaired part, materials property-mismatches and/or inner defects may facilitate the crack initiation and propagation and thus reduce the number of load cycles to failure. Therefore, a fundamental understanding of the fatigue crack growth and fracture mechanisms in joining zones is required to enable the prediction of the remaining life of repaired components and to further improve and adapt the repair technologies.
Fatigue crack growth experiments have been conducted on SEN (Single Edge Notch) specimens joined via brazing, and pre-sintered Preform (PSP) and multi-materials (casted/printed) specimens layered via additive manufacturing (AM). The experiments were performed at 950 °C and various stress ratios. The crack growth was measured using DCPD (Direct Current Potential Drop) method. The stress intensity factors for joined SEN specimens were calculated using the finite element method and then used to derive the fatigue crack growth curves. Metallographic and fractographic analyses were conducted to get insight into the fracture mechanism.
Results show that the experimental technique for fatigue crack growth was successfully adapted and applied for testing joined specimens. Furthermore, the initial tests indicate that the investigated braze filler material provides a lower resistance to crack growth, and bonding defects cause a crack to deviate to the interface of the base material and joining zone. In AM-sandwich specimens, the crack growth rates are significantly reduced when the crack reaches the interface of printed material and casted material. The obtained crack growth data can be used to calibrate a crack growth model, which will further be utilized to predict the remaining life of repaired components.
Multiscale modeling of heterogeneous structures based on a localized model order reduction approach
(2023)
Many of today’s problems in engineering demand reliable and accurate prediction of failure mechanisms of mechanical structures. Thus, it is necessary to take into account the heterogeneous structure on the smaller scale, to capture the underlying physical phenomena. However, this poses a great challenge to the numerical solution since the computational cost is significantly increased by resolving the smaller scale in the model. Moreover, in applications where scale separation as the basis of classical homogenization schemes does not hold, the influence of the smaller scale on the larger scale has to be modelled directly. This work aims to develop an efficient concurrent methodology to model heterogeneous structures combining the variational multiscale method (VMM) [1] and model order reduction techniques (e. g. [2]). First, the influence of the smaller scale on the larger scale can be taken into account following the additive split of the displacement field as in the VMM. Here, also a decomposition of the global domain into subdomains, each containing a fine grid discretization of the smaller scale, is introduced. Second, local reduced approximation spaces for the smaller scale solution are constructed by exploring possible solutions for each subdomain based on the concept of oversampling [3]. The associated transfer operator is approximated by random sampling [4]. Herein, we propose to incorporate the actual physical behaviour of the structure of interest in the training data by drawing random samples from a multivariate normal distribution with the solution of a reduced global problem as mean. The local reduced spaces are designed such that local contributions of each subdomain can be coupled in a conforming way. Thus, the resulting global system is sparse and reduced in size compared to the direct numerical simulation, leading to a faster solution of the problem.
Additive manufacturing (AM) has revolutionized the manufacturing industry, offering a new paradigm to produce complex geometries and parts with customized properties. Among the different AM techniques, the wire arc additive manufacturing (WAAM) process has gained significant attention due to its high deposition rate and low equipment cost. However, the process is characterized by a complex thermal history, dynamic metallurgy, and mechanical behaviour that make it challenging to simulate it in real-time for online process control and optimization.
In this context, a reduced order model (ROM) using the proper generalized decomposition (PGD) method is proposed as a powerful tool to overcome the limitations of conventional numerical methods and enable the real-time simulation of the temperature field of WAAM processes. Though, the simulation of a moving heat source leads to a hardly separable parametric problem, which is handled by applying a novel mapping approach. Using this procedure, it is possible to create a simple separated representation of the model, also allowing to simulate multiple layers.
In this contribution, a PGD model is derived for the WAAM procedure simulating the temperature field. A good agreement with a standard finite element method is shown. The reduced model is further used in a stochastic model parameter estimation using Bayesian inference, speeding up calibrations and ultimately leading to a calibrated real-time simulation.
In recent years, the use of simulation-based digital twins for monitoring and assessment of complex mechanical systems has greatly expanded. Their potential to increase the information obtained from limited data makes them an invaluable tool for a broad range of real-world applications. Nonetheless, there usually exists a discrepancy between the predicted response and the measurements of the system once built. One of the main contributors to this difference in addition to miscalibrated model parameters is the model error. Quantifying this socalled model bias (as well as proper values for the model parameters) is critical for the reliable performance of digital twins. Model bias identification is ultimately an inverse problem where information from measurements is used to update the original model. Bayesian formulations can tackle this task. Including the model bias as a parameter to be inferred enables the use of a Bayesian framework to obtain a probability distribution that represents the uncertainty between the measurements and the model. Simultaneously, this procedure can be combined with a classic parameter updating scheme to account for the trainable parameters in the original model. This study evaluates the effectiveness of different model bias identification approaches based on Bayesian inference methods. This includes more classical approaches such as direct parameter estimation using MCMC in a Bayesian setup, as well as more recent proposals such as stat- FEM or orthogonal Gaussian Processes. Their potential use in digital twins, generalization capabilities, and computational cost is extensively analyzed.
Additive manufacturing (AM) has revolutionized the manufacturing industry, offering a new paradigm to produce complex geometries and parts with customized properties. Among the different AM techniques, the wire arc additive manufacturing (WAAM) process has gained significant attention due to its high deposition rate and low equipment cost. However, the process is characterized by a complex thermal history making it challenging to simulate it in real-time for online process control and optimization.
In this context, a reduced order model (ROM) using the proper generalized decomposition (PGD) method [1] is proposed as a powerful tool to overcome the limitations of conventional numerical methods and enable the real-time simulation of the temperature field of WAAM processes. These simulations use a moving heat source leading to a hardly separable parametric problem, which is handled by applying a novel mapping approach [2]. This procedure makes it possible to create a simple separated representation of the model, which allows to simulate multiple layers.
In this contribution, a PGD model is derived for the temperature field simulation of the WAAM process. A good agreement with a standard finite element method is shown. The reduced model is further used in a stochastic model parameter estimation using Bayesian inference, speeding up calibrations and ultimately leading to a calibrated real-time simulation.
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 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.
The overview of the activity of Federal Institute for Material Research and Testing (BAM, Belin, Germany) in the field of additively manufacturing material characterization will be presented. The research of our group is focused on the 3D imaging of AM materials by means of X-ray Computed Tomography at the lab and at synchrotron, and the residual stress characterization by diffraction (nondestructive technique). Also, two successful research project in collaboration with CAM2, Sweden are presented.
Die Kombination aus hoher Korrosionsbeständigkeit und guten mechanischen Eigenschaften von Duplexstählen (DSS) ist auf ihre chemische Zusammensetzung und das ausgewogene Phasenverhältnis von Ferrit (α) und Austenit (γ) zurückzuführen.
Viele industrielle Anwendungen erfordern eine stoffschlüssige Verbindung von DSS. Das Wolfram-Inertgas-Schweißen (WIG) ist relativ einfach zu handhaben, benötigt nur wenig Platz und ermöglicht ein automatisiertes Schweißen, mit sehr hoher Reproduzierbarkeit und ist daher hervorragend zum Schweißen von DSS.
Während der Erstarrung dieser Dualphasenstähle kann es zu kritischen Phasenverhältnissen von α und γ kommen, was zu Erstarrungsrissen, Korrosionsanfälligkeit, geringerer Duktilität und kritischen Festigkeitswerten führt. Um die gewünschten Werkstoffeigenschaften zu erhalten, muss daher die α/γ-Verteilung zuverlässig vorhergesagt werden. Dies geschieht in der Regel mit Hilfe des WRC1992-Diagramms. Die Vorhersagegenauigkeit des Ferritgehalts in diesem Diagramm ist jedoch meist nicht genau genug und muss daher optimiert werden. Daher ist es notwendig, selbst kleinste Veränderungen in der chemischen Zusammensetzung des Schweißguts idealerweise während des Schweißens zu überwachen. Dies wird in diesen Experimenten mit Hilfe der laser-induzierten Plasmaspektroskopie (LIBS) durchgeführt. Ein großer Vorteil dieser Technik ist die hochgenaue zeit- und ortsaufgelöste Messung der chemischen Zusammensetzung während des Schweißens. In vorherigen Arbeiten wurde bereits die chemische Zusammensetzung im Schweißgut und der WEZ quantifiziert. In der präsentierten Untersuchung wird der Einfluss einzelner Elemente, wie Nb und Cu, auf das resultierende Schweißmikrogefüge untersucht.
The talk gives a short overview and explains basic principles of research ethics for PhD students. We examine the most important principles in the codes of conduct for good scientific practice on EU and national level. Based on these general rules, we give some hands-on practical tips for daily scientific work and discuss in detail one of the fields most susceptible to conflict, the topic of authorship in scientific publications.
Since the function of materials is controlled by properties and processes on the atomic scale, ab initio based high-throughput methods are valuable strategies in materials design. For computational efficiency, they are however often restriction to T=0K calculations, while many technologically relevant materials properties and thermodynamic stabilities change when going from low to high temperatures. On the other hand, the constantly increasing performance of digital tools for simulation and data-driven science enables more targeted material development including these kinds of finite-temperature effects. A flexible infrastructure, including data management and workflow solutions is required to make this symbiosis user-friendly efficient. Within this presentation, examples from ab initio thermodynamics for the design of phase stabilities in hard-magnetic alloys and defect-phases in advanced high-strength steels will be demonstrated. We will discuss physical concepts with a focus at the impact of magnetic excitations. At the same time, the examples will be used to derive requirements and present solutions for a digital infrastructure. An outlook will be given to current strategies with the NDFI initiative NFDI-MatWerk.
In vielen Anwendungen werden aufgrund zunehmender Anforderungen an den Widerstand gegenüber hohen, kombinierten mechanischen, tribologischen, thermischen und korrosiven Beanspruchungen Hochleistungsle¬gierungen eingesetzt. Den vielfach komplexen Bauteilstrukturen, herausfordernden Fertigungseigenschaften und der Erhöhung der Ressourceneffizienz lässt sich heute mit der additiven Fertigung (AM) begegnen, dem oftmals noch unzureichende Kenntnisse über die resultierende Homogenität und Anisotropie der speziellen Gefüge und den damit verbundenen Eigenschaften entgegensteht. Viele Anwendungen benötigen zudem definierte Oberflächen mit hohen Anforderungen an die Oberflächenintegrität, dem durch komplementären Einsatz von additiven und abtragenden Fertigungsschritten Rechnung getragen wird. Hochleistungslegierungen auf der Basis von Nickel oder Kobalt sind infolge der niedrigen Wärmeleitfähigkeit verbunden mit hoher Festigkeit und Zähigkeit äußerst herausfordernd mit Blick auf eine wirtschaftliche Zerspanung mit geometrisch bestimmter Schneide. Ein Vorhaben (IGF-Nr. 20.979 N/DVS-Nr. 1.3211) der BAM und TU Clausthal/ISAF befasst sich mit der Lösung dieser Problemstellung durch gezielte Beeinflussung des Gefüges mittels Modifikation der Schweißzusatzwerkstoffe und den Einsatz des ultraschallunterstützten Fräsens (US), bei gleichzeitiger Sicherstellung der geforderten Eigenschaften der untersuchten Werkstoffe. Es werden die Ergebnisse der Mikrostruktur und der Zerspanbarkeit der Modifikationen der MSG-Schweißung der FeNi36-Referenzlegierung mit 0,4 Gew.-% Zr und der Modifikationen der MSG-Schweißung der CoCr-Referenzlegierung mit 1 Gew.-% Zr sowie Hf vorgestellt. Abschließend werden aus den Ergebnissen Handlungsempfehlungen abgeleitet.
Structural alloys are largely employed in key industrial sectors and their demand is predicted to rise rapidly for the next decades. Most of these materials require a large amount of energy for extraction and manufacturing, which causes the emission of greenhouse gases and other pollutants. Therefore, strategies for improving the sustainability of structural metallic alloys are urgently needed.
Additive Manufacturing (AM), in particular Laser Powder Bed Fusion (PBF-LB/M), aims to be a sustainable manufacturing process, as it allows the build-up of complex geometry in near net-shape from 3D models, while minimizing material waste and the energy required for the process and post-process treatments.
Nevertheless, the application of additively manufactured parts in structural safety-relevant applications is still hindered by the poor fatigue performance. The cause of this has been mainly attributed to the presence of manufacturing defects and surface roughness. Therefore, a huge effort has been made to optimize the process parameters and to introduce post-process treatments to minimize the defect content. However, material flaws cannot be fully eliminated, but these can be considered in a damage tolerance framework for the prediction of the fatigue performance of additively manufactured metallic materials, which is essential for part design and qualification.
This work aims at presenting different modelling strategies for the prediction of the fatigue limit of AM metals. Simple empirical models and more complex models based on fatigue short crack propagation are proposed. The investigated material is an AlSi10Mg alloy fabricated by PBF-LB/M and subjected to two different low-temperature heat-treatments (265°C for 1 h and 300°C for 2h). The results show that the models can provide good approximation of the fatigue limits and help in the interpretation of the scatter of fatigue data.
The effect of the oscillating metal vapor plume on the keyhole and molten pool behavior during the laser beam welding of AlMg3 aluminum alloys is investigated by experimental and numerical methods. The real-time height of the metal vapor plume is measured by high-speed camera observation. The obtained experimental results are used to evaluate the additional heating source and laser beam attenuation caused by the scattering and absorption based on the Beer–Lambert theory. Furthermore, the dynamic behavior of the metal vapor plume is incorporated into a 3D transient heat transfer and fluid flow model, coupled with the ray tracing method, for the laser beam welding of the AlMg3 alloy. It is found that additional heating resulting from the scattered and absorbed laser beam energy by the metal vapor plume significantly expands the shape of the molten pool on the top region. Moreover, the oscillating metal vapor plume caused the fluctuation of the high-temperature region in the molten pool. The probability of keyhole collapse at the bottom increases 17% due to the oscillating laser power induced by the laser beam attenuation. The internal interplay between the metal vapor plume, molten pool shape, and keyhole collapse is obtained. The developed model has been validated by experiments, which shows a good agreement.
Magnetohydrodynamic technology is increasingly recognized as a promising approach for enhancing the quality of deep penetration laser beam welding. This study employs experimental and numerical methods to investigate the effects of an auxiliary oscillating magnetic field on reducing porosity in the laser beam welding of 5754 aluminum alloy. The experimental results clearly demonstrate a significant reduction in the porosity ratio, thereby validating the efficacy of applying MHD technology in mitigating porosity during the laser beam welding process. In addition, a transient 3D multi-physical model has been developed, integrating the magnetohydrodynamic and metal vapor plume, to gain a more comprehensive understanding of the porosity suppression mechanism. The introduction of an oscillating magnetic field generates a time-averaged downward Lorentz force. This Lorentz force, in turn, induces an electromagnetic expulsive force, which effectively increases the upward escape velocity of bubbles in the molten pool. Furthermore, the molten pool shape is significantly enlarged, which further facilitate the escape of bubbles. The simulation results agree well with the experimental results.
Der Absorptionsgrad metallischer Werkstoffe spielt bei Fügeprozessen mit einer Strahlungsquelle wie dem Hochleistungslaserstrahltiefschweißen eine bedeutende Rolle. Dieser beeinflusst die Menge der absorbierten Laserenergie, welche zum Aufschmelzen sowie zur lokalen Verdampfung des zu verbindenden Materials führt. Eine der wichtigsten Eigenschaften der Laserstrahlabsorption ist die Temperaturabhängigkeit. Dennoch wird sie in vielen Simulationsarbeiten häufig ignoriert und stattdessen ein angepasster Parameter zur Kalibrierung der Ergebnisse implementiert. Diese Vorgehensweise wirkt sich negativ auf die Zuverlässigkeit sowie die Genauigkeit des numerischen Modells sowie auf die Vorhersagbarkeit der Simulationsergebnisse aus. In der vorliegenden Arbeit wird die Temperaturabhängigkeit der Laserabsorption in ein selbstkonsistentes zweiphasiges Modell unter Berücksichtigung der gekoppelten 3D-Fluidströmung und Wärmeübertragung einbezogen. Die berechnete Laserstrahlabsorption wird durch die temperaturabhängigen Materialeigenschaften, die Lasercharakteristik und den Einfallswinkel des Laserstrahls bestimmt. Die freie Oberfläche jeder Phase wird mithilfe der volume-of-fluid Methode, kurz VOF, berechnet und die Laser-Material-Wechselwirkung wird durch ein neulich implementiertes Ray-Tracing-Verfahren modelliert, die auf einem lokalisierten Level-Set-Algorithmus basiert. Anschließend wird die transiente Wärmeeinbringung und die Geometrie der Schweißnaht analysiert und mit dem temperaturunabhängigen Absorptionsmodell verglichen. Es wurde festgestellt, dass die Temperaturabhängigkeit der Laserabsorption entscheidend für die genaue Bestimmung der Wärmeeinbringung und der Schmelzbadgeometrie ist. Das entwickelte Modell wurde anhand der experimentellen Untersuchungen validiert und die Bedeutung der Temperaturabhängigkeit bei der Laserstrahlabsorption wurde für die Simulation des Laserstrahltiefschweißens aufgeklärt.
The progress of the VAMAS interlaboratory comparison Project P13 "Lateral size of graphene oxide flakes by SEM" within the Technical Working Area 41 "Graphene and Related 2D Materials" is presented. The challenges at sample preparation on substrates for accurate measurement and image analysis as well as two different analysis approaches, containing exact guidance how to measure the main descriptors for the lateral size measurement of the imaged graphene oxide flakes with Scanning Electron Microscopy are highlighted. The implementation of the results into the corresponding ISO technical specification AWI/TS 23879 is also discussed and planned, in relation with the AFM part.
Fracture mechanics-based methods can be applied to the fatigue assessment of mechanical components containing defects or cracks subjected to cyclic loading. The knowledge of the crack growth behavior for short and long cracks enables the determination of both finite and infinite life regimes. Nevertheless, a first problem arises from the empirical determination of the fatigue data, which vary according to the test conditions, e.g., the test method, frequency and the stress ratio R. This is due to crack closure and further environmental phenomena affecting crack propagation behavior. A second issue regards the lack of standard experimental procedures for the determination of the fatigue crack propagation threshold ΔKth dependency on the crack size. This is problematic, since ΔKth increases with the build-up of the closure effects in the short crack regime and its inaccurate experimental determination leads to large deviations in the assessment and, even more critical, to non-conservative predictions. Aiming at providing more reliable and safer assessment procedures, the present work proposes a method based on effective crack growth data obtained at high stress ratios (R ≈ 0.8). A short-crack model based on the cyclic R-curve and CPLR data is employed for describing the development of the closure effects. The predictions are compared to established methods and validated by experimental tests.
Thermodynamic bulk phase diagrams have become the roadmap used by researchers to identify alloy compositions and process conditions that result in novel materials with tailored microstructures. On the other hand, recent progress in experimental atomic-scale characterization techniques allows one to study the local chemical composition at individual defects such as interfaces, grain boundaries, dislocations and surfaces. They show that changes in the alloy composition can drive not only transitions in the bulk phases present in a material, but also in the concentration and type of defects they contain. Defect phase diagrams, using chemical potentials as thermodynamics variables, provide a natural route to study these chemically driven defects. Our results show, however, that the direct application of thermodynamic approaches can fail to reproduce the experimentally observed defect formation.
Therefore, we extend the concept to metastable defect phase diagrams to account for kinetic limitations that prevent the system from reaching equilibrium. We use ab initio calculations based on density functional theory to quantify the extension of regions where defect formation is expected. We successfully applied this concept to explain the formation of large concentrations of planar defects in supersaturated Fe2Nb Laves phases. In addition, we identify in a joint study with experiments conditions and structures in Mg-Al-Ca alloys for defect phase occurrence. The concept offers new avenues for designing materials with tailored defect structures.
Understanding segregation to grain boundaries, and eventually their defect phase diagrams, is critical to enable rational grain boundary engineering for alloys design. Here, we discuss the progress and challenges one may face when generating such a database of defect-solute interactions, in the context of studying of grain boundary segregation of solutes and impurities in Fe GBs. To compute the large number of relevant defect-solute interactions we have performed high-throughput ab initio calculations using efficient and highly automated workflows using pyiron. We study has been performed across a representative set of coincident-site-lattice (CSL) type tilt GBs. Based on the large ab-initio datasets, we extract and analyse the chemical and structural trends observed in the solute segregation behaviour across the periodic table. The features which are most important in evaluating site segregation are presented and discussed.
The Kitagawa-Takahashi (KT) diagram is a well-established tool in the fatigue community for the prediction of the fatigue limit of metallic materials in presence of defects. Nevertheless, its determination is associated with a large number of fatigue tests carried out on specimens with artificial defects (notches) to describe the fatigue limit-defect size relationship in the short crack regime. The preparation of the specimens and the execution of the tests is time expensive. Therefore, few phenomenological models have been introduced in the past to provide a first approximation of the KT diagram based solely on the fatigue limit for smooth specimen and the fatigue crack propagation threshold for long cracks. Despite the use of such models is widely spread, these suffer from problems related to the uncertainties of the material parameters. Furthermore, the mechanics of short cracks, which is the physical mechanism behind the concept of the fatigue limit, is not considered. This work discusses the advantages and drawbacks of using short fatigue crack propagation models for predicting the fatigue limit of flawed metallic materials. The KT diagrams for a EA4T railway axle steel in as-received and full-hardened condition are approximated by means of the cyclic R-curve method.
Inspired by the chemistry of mussel adhesive proteins, polydopamine (PDA has been shown as one of the most versatile platforms for altering the properties and incorporating new functionalities to nearby any material surface despite its nature. Rich chemistry of PDA enables broad variety of surface modification and diverse secondary reactions that makes it extremely interesting for a wide range of application including biomedical field, e.g., drug delivery, adhesives, cell adhesion, biosensing. Despite high potential of polydopamine, the lack of deposition control and precision in existed methods limits their applications in microdevices and miniaturized functional systems like, for example, MEMS, microfluidic and sensorics.
Herein, we demonstrate a novel maskless approach for surface micropatterning with polydopamine based on Multiphoton Lithography that overcomes present limitations. Neither strong oxidants, metal ions nor adjustment of pH to alkaline is required by this technique. The spatial resolution down to 0.8 µm has been achieved which is at least an order of magnitude smaller than shown by other existed methods. We are able to control the morphology and thickness of the micropattern by altering fabrication parameters allowing structure gradient.
Apart from the glass substrate, we achieved PDA patterning at surfaces of different nature such as polychlorotrifluoroethylene, polydimethylsiloxane, polyethylene terephthalate, silicon wafers, and fluorinated glass coverslips. Post-modification of polydopamine micropatterns with protein enzyme like trypsin is demonstrated to highlight its sensing potential.
Presented in this work microfabrication technique empowers advanced applications of mussel-inspired materials in single-molecule bioassays, sensors and other complex microdevices.
Thermodynamic bulk phase diagrams have become the roadmap used by researchers to identify alloy compositions and process conditions that result in novel materials with tailored microstructures. On the other hand, recent progress in experimental atomic-scale characterization techniques allows one to study the local chemical composition at individual defects such as interfaces, grain boundaries, dislocations and surfaces. They show that changes in the alloy composition can drive not only transitions in the bulk phases present in a material, but also in the concentration and type of defects they contain. Defect phase diagrams, using chemical potentials as thermodynamics variables, provide a natural route to study these chemically driven defects. Our results show, however, that the direct application of thermodynamic approaches can fail to reproduce the experimentally observed defect formation.
Therefore, we extend the concept to metastable defect phase diagrams to account for kinetic limitations that prevent the system from reaching equilibrium. We use ab initio calculations based on density functional theory to quantify the extension of regions where defect formation is expected. We successfully applied this concept to explain the formation of large concentrations of planar defects in supersaturated Fe2Nb Laves phases. In addition, we identify in a joint study with experiments conditions and structures in Mg-Al-Ca alloys for defect phase occurrence. The concept offers new avenues for designing materials with tailored defect structures.
Suitable material solutions are of key importance in designing and producing components for engineering systems – either for functional or structural applications. Materials data are generated, transferred, and introduced at each step along the complete life cycle of a component. A reliable materials data space is therefore crucial in the digital transformation of an industrial branch.
Therefore, the “Innovation Platform MaterialDigital (PMD) funded by the German Federal Ministry of Education and Research (BMBF), aims to develop a sustainable infrastructure for the standardized digital representation of materials science and materials engineering. With its partners (KIT, Fraunhofer IWM, FIZ, Leibnitz IWT, BAM, MPIE), the PMD is committed to build up a materials science data space. To achieve this the PMD provides a prototypical infrastructure for the digitalization of materials implemented by decentralized data servers, standardized data schemas and digital workflows. Following the FAIR principles, it will promote the semantic interoperability across the frontiers of materials classes.
Standards, methods, and tools developed within the platform are deployed and consolidated within the context of currently near 20 BMBF-funded academic and industrial research consortia and made available to the material science community in general. In this context scientific workflows represent a major focus area, represented within the platform by the workflow frameworks pyiron and SimStack. In consequence, the platform is building up a digital library in form of a workflow store along with common standards for the definition and representation of digital workflows.
In this presentation we will describe the status of our Platform MaterialDigital with a focus on the workflow activities. The current status and the vision for dissemination of the solutions developed in the PMD within the community are provided.
Material science problems have intrinsically multiscale and multiphysics characters, and require us to employ a combination of methods on different time and length scales to resolve critical features. Normally creating workflows that connect data in multiple scales and various methods is a cumbersome task. Pyiron, an integrated development environment (IDE) for material science, contains modules for the atomistic as well as continuum scale that make a seamless connection possible. To this end, it provides a high-level coherent language in a unified workflow platform to study materials, for example, with density functional theory (DFT) simulations in the same framework as with the finite element method (FEM). In this work, in addition to the introduction of different features in pyiron and its continuum module, a couple of exemplary workflows are demonstrated, bridging the scales in pyiron. One such workflow is analyzing the elastoplastic response of metallic alloys, where the elastic properties of the system are dependent on the atomistic composition and structure of the alloy. We use pyiron atomistics to model the dependence of elastic moduli on the atomistic composition of Al-Mg alloys. Benefiting from the integration of DAMASK in the continuum module of pyiron, the model can be used as an input to simulate the elastoplastic response of the alloy under various loadings. In addition, finite element simulations using the FEniCS package are currently integrated into the continuum module and will be demonstrated in the presentation.
Since the function of materials is controlled by properties and processes on the atomic scale, ab initio based high-throughput methods are valuable strategies in materials design. For computational efficiency, they are however often restriction to T=0K calculations, while many technologically relevant materials properties and thermodynamic stabilities change when going from low to high temperatures. On the other hand, the constantly increasing performance of digital tools for simulation and data-driven science enables more targeted material development including these kinds of finite-temperature effects. A flexible infrastructure, including data management and workflow solutions is required to make this symbiosis user-friendly efficient. Within this presentation, examples from ab initio thermodynamics for the design of phase stabilities in hard-magnetic alloys and defect-phases in advanced high-strength steels will be demonstrated. We will discuss physical concepts with a focus at the impact of magnetic excitations. At the same time, the examples will be used to derive requirements and present solutions for a digital infrastructure. An outlook will be given to current strategies with the NDFI initiative NFDI-MatWerk.
Thermodynamic bulk phase diagrams have become the roadmap used by researchers to identify alloy compositions and process conditions that result in novel materials with tailored microstructures. Recent experimental studies show that changes in the alloy composition can drive not only transitions in the bulk phases present in a material, but also in the concentration and type of defects they contain. Defect phase diagrams in combination with density functional theory provide a natural route to study these chemically driven defects. Our results show, however, that direct application of thermodynamic approaches can fail to reproduce the experimentally observed defect formation. Therefore, we extend the concept to metastable defect phase diagrams to account for kinetic limitations that prevent the system from reaching equilibrium. We successfully applied this concept to explain the formation of large concentrations of planar defects in supersaturated Fe-Nb solid solutions and to identify in a joint study with experiments conditions in Mg-Al-Ca alloys for defect phase occurrence. The concept offers new avenues for designing materials with tailored defect structures.
Suitable material solutions are of key importance in designing and producing components for engineering systems – either for functional or structural applications. Materials data are generated, transferred, and introduced at each step along the complete life cycle of a component. A reliable materials data space is therefore crucial in the digital transformation, both in academia and industry.
Therefore, the consortium NFDI MatWerk aims to develop a sustainable infrastructure for the standardized digital representation of materials science and engineering (MatWerk). The goal is to seamlessly integrate decentralized data and metadata, experimental and computational workflows, and a materials ontology to maximize interoperability and reproducibility of materials data processing. To this end, data use profiles of participant projects from different sub-disciplines are analyzed to identify the most relevant scientific scenarios within MatWerk.
Similarly, the Plattform MaterialDigital (PMD) is committed to provide a prototypical infrastructure for the digitalization of materials in an industrial context implemented by decentralized data servers, semantic data schemas and digital workflows. The standards, methods, and tools developed within the PMD are deployed and consolidated within the context of currently more than 20 BMBF-funded academic and industrial research consortia. Scientific workflow environments represent a major focus area, including efforts to improve the definition and representation of digital workflows, as well as their distribution in form of a workflow store.
In this presentation we will describe the overarching visions behind these initiatives, their status, and progress of dissemination with a focus on the workflow activities. Following the philosophy of both consortia, specific examples will be used to demonstrate innovative and pragmatic solutions.
Suitable material solutions are of key importance in designing and producing components for engineering systems – either for functional or structural applications. Materials data are generated, transferred, and introduced at each step along the complete life cycle of a component. A reliable materials data space is therefore crucial in the digital transformation and an important prerequisite for machine learning in materials science.
Therefore, the consortium NFDI MatWerk aims to develop a sustainable infrastructure for the standardized digital representation of materials science and engineering (MatWerk). The goal is to seamlessly integrate decentralized data and metadata, experimental and computational workflows, and a materials ontology to maximize interoperability and reproducibility of materials data processing. To this end, data use profiles of participant projects from different sub-disciplines are analyzed to identify the most relevant scientific scenarios within MatWerk.
Similarly, the Plattform MaterialDigital (PMD) is committed to provide a prototypical infrastructure for the digitalization of materials in an industrial context implemented by decentralized data servers, semantic data schemas and digital workflows. The standards, methods, and tools developed within the PMD are deployed and consolidated within the context of currently more than 20 BMBF-funded academic and industrial research consortia. Scientific workflow environments represent a major focus area, including efforts to improve the definition and representation of digital workflows, as well as their distribution in form of a workflow store.
In this presentation we will describe the overarching visions behind these initiatives, their status, and progress of dissemination with a focus on the workflow activities and the connection to machine learning applications. Following the philosophy of both consortia, specific examples will be used to demonstrate innovative and pragmatic solutions.
Thermodynamic bulk phase diagrams have become the roadmap used by researchers to identify alloy compositions and process conditions that result in novel materials with tailored microstructures. On the other hand, recent progress in experimental atomic-scale characterization techniques allows one to study the local chemical composition at individual defects such as interfaces, grain boundaries, dislocations and surfaces. They show that changes in the alloy composition can drive not only transitions in the bulk phases present in a material, but also in the concentration and type of defects they contain. Defect phase diagrams, using chemical potentials as thermodynamics variables, provide a natural route to study these chemically driven defects. Our results show, however, that the direct application of thermodynamic approaches can fail to reproduce the experimentally observed defect formation.
Therefore, we extend the concept to metastable defect phase diagrams to account for kinetic limitations that prevent the system from reaching equilibrium. We use ab initio calculations based on density functional theory to quantify the extension of regions where defect formation is expected. We successfully applied this concept to explain the formation of large concentrations of planar defects in supersaturated Fe2Nb Laves phases. In addition, we identify in a joint study with experiments conditions and structures in Mg-Al-Ca alloys for defect phase occurrence. The concept offers new avenues for designing materials with tailored defect structures.
Der Vortrag gibt einen kurzen Überblick über die Bedeutung der Fügetechnik in Wasserstofftechnologien und über zukünftige FuE-Bereiche. Fügetechnologien haben dabei wesentliche Bedeutung für die Umsetzung von technischen Komponenten der Wasserstofftechnologien. Forschungsschwerpunkte ergeben sich insbesondere für die Erzeugung und den Transport des Wasserstoffs.
Das Master Curve (MC) Konzept nach ASTM E1921 wird momentan u.a. in der Kerntechnik für die bruchmechanische Sicherheitsbewertung ferritischer Stähle eingesetzt. Für ferritisches Gusseisen mit Kugelgraphit (GJS) fehlt jedoch eine Validierung dieser Methode und ggf. eine systematische Überprüfung möglicher Modifikationen grundlegender Ansätze des MC-Konzepts. Aus diesem Grund soll in einem kürzlich gestarteten BMUV-Verbundprojekt zwischen der MPA Stuttgart und der BAM Berlin die Anwendbarkeit des MC-Konzepts auf GJS bei dynamischer Beanspruchung untersucht werden. Ziel ist es, eine effektive und effiziente Methodik bereitzustellen, mit der mittels einer vergleichsweise kleinen Stichprobe bruchmechanischer Kleinproben eine Referenztemperatur T0 zur werkstoffspezifischen Temperaturjustierung der MC ermittelt werden kann.
Hydrogenated nitrile butadiene rubber (HNBR) elastomer was thermo-oxidatively aged at different temperatures up to 150°C. Fourier transform infrared spectroscopy (FTIR), compression stress relaxation (CSR) and IRHD microhardness were used to characterise the chemo-mechanical changes of HNBR O-rings during thermo-oxidative ageing. FTIR shows the development of carbonyl, methyl and ester groups but the nitrile content was not affected by ageing. The effect of sample geometry during CSR was investigated. CSR data were converted through integrated kinetic laws. The conversion has proven its sensibility to detect heterogeneous ageing. This was confirmed by the IRHD measurements across the section of O-rings. Oxidation profiles were determined by applying the model of Wise et al. to the IRHD experimental results.
Inorganic nanocrystals with linear and nonlinear luminescence in the ultraviolet, visible, near infrared and short-wave infrared like semiconductor quantum dots and spectrally shifting lanthanide-based nanophosphors have meanwhile found applications in the life and material sciences ranging from optical reporters for bioimaging and sensing over security barcodes to solid state lighting and photovoltaics. These nanomaterials commonly have increasingly sophisticated core/shell particle architectures with shells of different chemical composition and thickness to minimize radiationless deactivation at the particle surface that is usually the main energy loss mechanism [1]. For lanthanide-based spectral shifters, particularly for very small nanoparticles, also surface coatings are needed which protect near-surface lanthanide ions from luminescence quenching by high energy vibrators like O-H groups and prevent the disintegration of these nanoparticles under high dilution conditions. [2,3,4]. The identification of optimum particle structures requires quantitative spectroscopic studies focusing on the key performance parameter photoluminescence quantum yield [5,6], ideally flanked by single particle studies to assess spectroscopic inhomogeneities on a particle-to-particle level for typical preparation methods [7,8], Moreover, in the case of upconversion nanoparticles with a multi-photonic and hence, excitation power density (P)-dependent luminescence, quantitative luminescence studies over a broad P range are required to identify particle architectures that are best suited for applications in fluorescence assays up to fluorescence microscopy. Here, we present methods to quantify the photoluminescence of these different types of emitters in the vis/NIR/SWIR and as function of P and demonstrate the importance of such measurements for a profound mechanistic understanding of the nonradiative deactivation pathways in semiconductor and upconversion nanocrystals of different size and particle architecture in different environments.
Als elektrische Durchschlagfestigkeit bezeichnet man die elektrische Feldstärke, bei der es zur Entladung durch ein isolierendes Medium kommt. Im Falle von Festkörpern ist dies ein zerstörender Prozess. Bei der messtechnischen Bestimmung der Durchschlagfestigkeit haben neben den verwendeten Messgeräten auch die eingesetzten Elektroden, die Form des Prüfkörpers und das Isoliermedium, in dem die Prüfung stattfindet, einen signifikanten Einfluss auf die ermittelten Messwerte. Im Vortrag werden die Einflüsse erläutert und anhand von Messreihen aus der Literatur und der eigenen Forschung quantifiziert. Aufgrund der vorgestellten Effekte wird klar, dass es sich bei elektrischer Durchschlagfestigkeit nicht um absolute Materialkennwerte handelt, sondern vielmehr um systemabhängige Größen.
The surface chemistry of nanomaterials controls their interaction with the environment and biological species and their fate and is hence also relevant for their potential toxicity. This has meanwhile led to an increasing interest in validated and preferably standardized methods for the determination and quantification of surface functionalities on nanomaterials and initiated different standardization projects within ISO/TC 229 and IEC/TC 113 as well as interlaboratory comparisons (ILCs) of different analytical methods for the quantification of surface coatings by OECD. Here we present the results of a first ILC on the quantification of the amount of amino functionalities on differently sized inorganic nanoparticles done by division Biophotonics and the National Research Council of Canada (NRC) and the PWI 19257 on the Characterization and Quantification of Surface Functional Groups and Coatings on Nanoobjects approved by ISO/TC 229 (WG2) in fall 2022 that will result in a VAMAS study on this topic organized by division Biophotonics. Key words: nanoparticles, surface analysis, surface functional groups, quantification, optical assay, qNMR, VAMAS, standardization, ICL, quality assurance, reference material.
The overview of the activity of Federal Institute for Material Research and Testing (BAM, Belin, Germany) in the field of additively manufacturing material characterization will be presented. The research of our group is focused on the 3D imaging of AM materials by means of X-ray Computed Tomography at the lab and at synchrotron, and the residual stress characterization by diffraction (nondestructive technique).
Chemical bonding and coordination environments are crucial descriptors of material properties. They have previously been applied to creating chemical design guidelines and chemical heuristics. They are currently being used as features in machine learning more and more frequently. I will discuss implementations and algorithms (ChemEnv and LobsterEnv) for identifying these coordination environments based on geometrical characteristics and chemical bond quantum chemical analysis. I will demonstrate how these techniques helped in testing chemical heuristics like the Pauling rule and thereby improved our understanding of chemistry. I will also show how these tools can be used to create new design guidelines and a new understanding of chemistry. To use quantum-chemical bonding analysis on a large-scale and for machine-learning approaches, fully automatic workflows and analysis tools have been developed. After presenting the capabilities of these tools, I will also point out how these developments relate to the general trend towards automation in the field of density functional based materials science.