Mold-filling Simulation of Resin Transfer Molding with Fluid-Structure Interaction

Das Resin Transfer Molding (RTM) Verfahren ist ein vielversprechender Prozess für die Großserienfertigung von endlosfaserverstärkten Kunststoffen. Um die spezifische Steifigkeit der Strukturbauteile zu erhöhen, können zusätzliche Polymerschaumkerne zwischen den Verstärkungslagen eingebettet werden. Das RTM-Verfahren ermöglicht die intrinsische Herstellung dieser Sandwichbauteile, die sehr hohe spezifische mechanische Eigenschaften aufweisen. Beim Prozessschritt der Formfüllung wird ein flüssiges Polymerharz in eine geschlossene Form injiziert und infiltriert das poröse Halbzeug. Der Druck im Inneren der Form steigt jedoch aufgrund der Widerstandskraft der Fasern gegenüber dem Harzfluss an. Dies führt zu einer Kompression des eingebetteten Schaumkerns. Die Kompression des Schaumkerns führt außerdem zu einer Vergrößerung des Kavitätvolumens und damit zu einem höheren Bauteilgewicht und einem geringeren Faservolumengehalt. Da der Harzfluss stark vom Faservolumengehalt abhängt, führt dies außerdem zu einem veränderten Formfüllungsverhalten. In dieser Arbeit wird eine numerische Methode für die Formfüllung entwickelt, die es ermöglicht, die Wechselbeziehung zwischen Harzfluss und Schaumkernverformung zu analysieren. Die Entwicklungen in dieser Arbeit basieren auf einer Finite-Volumen (FV)-Diskretisierung in Kombination mit einer Volume-of-Fluid (VoF)-Methode für die Zweiphasenströmung bestehend aus flüssigem Harz und Luft, die in der Open-Source-Bibliothek OpenFOAM\textsuperscript{\textregistered} implementiert ist. Zunächst wird eine einseitige Kopplung entwickelt, um den Einfluss einer vorgegebenen Werkzeugverformung auf den Formfüllprozess zu modellieren, wobei die sich verändernde Kavität mit einem dynamischen Netz erfasst wird. Mit diesem ersten Schritt ist es möglich, Compression Resin Transfer Molding (C-RTM) und Pressure-Controlled Resin Transfer Molding (PC-RTM) zu simulieren, bei denen die Höhe der Kavität während der Formfüllung nicht konstant ist. Im nächsten Schritt wird eine Methode zur Beschreibung der porösen Strukturmechanik entwickelt, um die Verformung des Halbzeugs während der Infiltration zu modellieren. Der Ansatz basiert auf dem Terzaghi-Prinzip der effektiven Spannung und verwendet eine inkrementelle FV-Formulierung für große Dehnungen. Die Kopplung zwischen Harzfluss, Halbzeugkompaktierung und Schaumkernkompression wird mit einem partitionierten Fluid-Struktur-Interaktions-Ansatz (FSI) modelliert, der mit der generalisierten Open-Source-Schnittstelle preCICE realisiert wird. Die Simulationsmethoden werden in mehreren Schritten verifiziert und zeigen stets eine sehr gute Übereinstimmung mit analytischen Lösungen. Eine numerische Sensitivitätsstudie auf Bauteilskala zeigt den starken Einfluss eines eingebetteten Schaumkerns auf den Formfülldruck und auf die Ausbreitung der Fließfront, was mit herkömmlichen Formfüllsimulationen nicht vorhergesagt werden kann. Dies bestätigt die Notwendigkeit der Verwendung gekoppelter Simulationen für die Formfüllung bei RTM mit eingebetteten Schaumkernen. Zur Validierung der FSI-Methode werden Sandwichbauteile im RTM-Verfahren hergestellt. Dazu werden experimentelle Messungen durchgeführt, um die an der Validierung beteiligten Materialien zu charakterisieren. Die mechanische Charakterisierung des Polymerschaumkerns wird für verschiedene Schaumdichten und bei unterschiedlichen isothermen Temperaturen durchgeführt. Die Ergebnisse zeigen eine starke Abnahme des Kompressionsmoduls mit abnehmender Dichte und steigender Temperatur. Die Validierungssimulationen zeigen eine gute Übereinstimmung mit dem vorhergesagten Druckniveau. Mit dem validierten Modell ist es möglich, die Formfüllzeit und weitere Parameter wie z.B. die Pressenkraft genauer vorherzusagen. Eine weitere Steigerung der Genauigkeit wird erwartet, wenn die Temperatur innerhalb der Form mit einer erweiterten Methode für den Wärmeübergang innerhalb und zwischen den beteiligten Materialien modelliert wird

Testosterone and Dehydroepiandrosterone Treatment in Ageing Men: Are We All Set?

Although demographic statistics show that populations around the world are rapidly ageing, this rising life expectancy is accompanied by an increase in the number of people living with age-related chronic conditions, such as frailty, cognitive decline, depression, or sexual dysfunction. In men, a progressive decline in androgens occurs with increasing age, and low androgen levels are associated with age-related chronic conditions. However, androgen administration studies are inconclusive, showing differing results according to the androgen used (testosterone [T], dehydroepiandrosterone [DHEA]), the group of men examined (younger vs. older; eugonadal vs. hypogonadal) and the conditions studied (frailty, cognitive decline, depression, sexual dysfunction). In this review, the current state for the use of T and DHEA therapy in men for the age-related conditions is examined. Due to the progressive age-related decline in androgens leading to a higher rate of older men having low androgen levels, the effects of androgen treatment in elderly males will be of particular interest in this review. Dose-response relationships, the role of potential moderators, and the androgen treatment-related risk for adverse events will be discussed. Studies have suggested that T treatment - more so than DHEA treatment - may be an effective therapy against age-related chronic conditions in men with low T levels; especially older men. Such conditions include frailty, depression, or sexual dysfunction. However, T treatment does not emerge as an effective therapy against cognitive decline. Nevertheless, more high-quality, randomised controlled trials using T treatment for age-related chronic conditions are necessary if further conclusions are to be made

Deep neural networks as surrogate models for time-efficient manufacturing process optimisation

Manufacturing process optimisation usually amounts to searching optima in high-dimensional parameter spaces. In industrial practice, this search is most often directed by human-subjective expert judgment and trial-and-error experiments. In contrast, high-fidelity simulation models in combination with general-purpose optimisation algorithms, e.g. finite element models and evolutionary algorithms, enable a methodological, virtual process exploration and optimisation. However, reliable process models generally entail significant computation times, which often renders classical, iterative optimisation impracticable. Thus, efficiency is a key factor in optimisation. One option to increase efficiency is surrogate-based optimisation (SBO): SBO seeks to reduce the overall computational load by constructing a numerically inexpensive, data-driven approximation („surrogate“) of the expensive simulation. Traditionally, classical regression techniques are applied for surrogate construction. However, they typically predict a predefined, scalar performance metric only, which limits the amount of usable information gained from simulations. The advent of machine learning (ML) techniques introduces additional options for surrogates: in this work, a deep neural network (DNN) is trained to predict the full strain field instead of a single scalar during textile forming („draping“). Results reveal an improved predictive accuracy as more process-relevant information from the supplied simulations can be extracted. Application of the DNN in an SBO-framework for blank holder optimisation shows improved convergence compared to classical evolutionary algorithms. Thus, DNNs are a promising option for future surrogates in SBO

Micro-Scale Permeability Characterization of Carbon Fiber Composites Using Micrograph Volume Elements

To manufacture a high-performance structure made of continuous fiber reinforced plastics, Liquid Composite Molding processes are used, where a liquid resin infiltrates the dry fibers. For a good infiltration quality without dry spots, it is important to predict the resin flow correctly. Knowledge of the local permeability is an essential precondition for mold-filling simulations. In our approach, the intra-bundle permeability parallel and transverse to the fibers is characterized via periodic fluid dynamic simulations of micro-scale volume elements (VE). We evaluate and compare two approaches: First, an approach to generate VEs based on a statistical distribution of the fibers and fiber diameters. Second, an approach based on micrograph images of samples manufactured with Tailored Fiber Placement (TFP) using the measured fiber distribution. The micrograph images show a higher heterogeneity of the distribution than the statistically generated VEs, which is characterized by large resin areas. This heterogeneity leads to a significantly different permeability compared to the stochastic approach. In conclusion, a pure stochastic approach needs to contain the large heterogeneity of the fiber distribution to predict correct permeability values

On the Resin Transfer Molding (RTM) Infiltration of Fiber-Reinforced Composites Made by Tailored Fiber Placement

Tailored fiber placement (TFP) is a preform manufacturing process in which rovings made of fibrous material are stitched onto a base material, increasing the freedom for the placement of fibers. Due to the particular kinematics of the process, the infiltration of TFP preforms with resin transfer molding (RTM) is sensitive to multiple processes and material parameters, such as injection pressure, resin viscosity, and fiber architecture. An experimental study is conducted to investigate the influence of TFP manufacturing parameters on the infiltration process. A transparent RTM tool that enables visual tracking of the resin flow front was developed and constructed. Microsection evaluations were produced to observe the thickness of each part of the composite and evaluate the fiber volume content of that part. Qualitative results have shown that the infiltration process in TFP structures is strongly influenced by a top and bottom flow layer. The stitching points and the yarn also create channels for the resin to flow. Furthermore, the stitching creates some eye-like regions, which are resin-rich zones and are normally not taken into account during the infusion of TFP parts

On the Resin Transfer Molding (RTM) Infiltration of Fiber-Reinforced Composites made by Tailored Fiber Placement

Tailored fiber placement (TFP) is a preform manufacturing process in which rovings made of fibrous material are stitched onto a base material, increasing the freedom for the placement of fibers. Due to the particular kinematics of the process, the infiltration of TFP preforms with resin transfer molding (RTM) is sensitive to multiple processes and material parameters, such as injection pressure, resin viscosity, and fiber architecture. An experimental study is conducted to investigate the influence of TFP manufacturing parameters on the infiltration process. A transparent RTM tool that enables visual tracking of the resin flow front was developed and constructed. Microsection evaluations were produced to observe the thickness of each part of the composite and evaluate the fiber volume content of that part. Qualitative results have shown that the infiltration process in TFP structures is strongly influenced by a top and bottom flow layer. The stitching points and the yarn also create channels for the resin to flow. Furthermore, the stitching creates some eye-like regions, which are resin-rich zones and are normally not taken into account during the infusion of TFP parts

Simulating Mold Filling in Compression Resin Transfer Molding (CRTM) Using a Three-Dimensional Finite-Volume Formulation

Light-weight structural components are increasingly made of continuous fiber reinforced plastics (CoFRP), but their mass production is still very expensive. Because of its high automation potential, especially the Compression Resin Transfer Molding (CRTM) process gains more and more attention. Numerical mold filling simulations help to optimize this process and can avoid expensive experimental studies. Here, we present a new method to simulate mold filling in CRTM using a full three-dimensional finite-volume (FV) method. In comparison to known finite-element (FE) methods, it contains a compressible two-phase/Volume-of-Fluid description of the air- and resin-phase. This approach is combined with a moving mesh to account for the change of cavity height during the process, which results in a change of fiber volume fractions and thus permeabilities. We verify the method by comparison to analytic solutions of the Darcy equation and to solutions of state-of-the-art mold filling simulation software. The presented method enables CRTM mold filling simulation of complex parts, which is shown in two application examples. Furthermore, this shows the potential of using FV-based tools to simulate mold filling in RTM process variants containing non-constant cavity geometries

Simulating Mold Filling in Compression Resin Transfer Molding (CRTM) Using a Three-Dimensional Finite-Volume Formulation

Light-weight structural components are increasingly made of continuous fiber reinforced plastics (CoFRP), but their mass production is still very expensive. Because of its high automation potential, especially the Compression Resin Transfer Molding (CRTM) process gains more and more attention. Numerical mold filling simulations help to optimize this process and can avoid expensive experimental studies. Here, we present a new method to simulate mold filling in CRTM using a full three-dimensional finite-volume (FV) method. In comparison to known finite-element (FE) methods, it contains a compressible two-phase/Volume-of-Fluid description of the air- and resin-phase. This approach is combined with a moving mesh to account for the change of cavity height during the process, which results in a change of fiber volume fractions and thus permeabilities. We verify the method by comparison to analytic solutions of the Darcy equation and to solutions of state-of-the-art mold filling simulation software. The presented method enables CRTM mold filling simulation of complex parts, which is shown in two application examples. Furthermore, this shows the potential of using FV-based tools to simulate mold filling in RTM process variants containing non-constant cavity geometries