In this book, a new three-dimensional approach for the process simulation of SMC is developed. This approach takes into account both, the core layer that is dominated by the extensional viscosity and the thin lubrication layer. In order to transfer the information from the process to the structure simulation, a CAE chain is further developed. In addition, a new rheological tool is developed to analyze flow behavior experimentally and to provide the required material parameters.
Sheet Molding Compounds (SMC) are discontinuous fiber reinforced composites that are widely applied due to their ability to realize composite parts with long fibers at low cost. A novel Direct Bundle Simulation (DBS) method is proposed in this work to enable a direct simulation at component scale utilizing the observation that fiber bundles often remain in a bundled configuration during SMC compression molding.
Ihrer Arbeit in der Originalsprache: This work aims at identifying relevant road surface characteristics to mitigate tire-road noise of free-rolling tires using a systematic approach. As using open porous roads is already known as an efficient measure to reduce tire rolling noise, this study will focus on compact road surfaces which have a low acoustic absorption. Measurements on standardized ISO 10844 test tracks and on public roads are used to study the norm's representativity and its completeness.
Interdisciplinary development approaches for system-efficient lightweight design unite a comprehensive understanding of materials, processes and methods. This applies particularly to continuous fibre-reinforced plastics (CoFRPs), which offer high weight-specific material properties and enable load path-optimised designs. This thesis is dedicated to understanding and modelling Wet Compression Moulding (WCM) to facilitate large-volume production of CoFRP structural components.
This thesis deals with the fibre impregnation of a carbon fibre reinforcement by a Sheet Moulding Compound (SMC). In the beginning, the carbon fibre reinforcement has no impregnation. Instead, the impregnation of the carbon fibre is performed by the resin within the SMC material during compression moulding. The combination leads to a Hybrid SMC composite, which is characterized by a high design freedom, good mechanical properties, and high production rates at the same time. The main objective of this study is the development of an analytical impregnation model for Hybrid SMC composites. The impregnation model predicts the final void content with regard to the properties of the semi-finished products and the process implementation. The fibre impregnation is influenced by the viscosity of the SMC material, the processing compression, the permeability, and the thickness of the carbon fibre reinforcement. Among all these parameters, the viscosity is an essential factor for the fibre impregnation, because it is dependent on the temperature and the time. The final impregnation model is developed by an approach of fluid dynamics to track the flow front particles within the SMC material during compression moulding. At the same time, experiments are realized and the void content is determined by using microscopic analysis of the Hybrid SMC composites. The evaluated void contents of the experiments are used to compare the results with the impregnation model. All in all, the investigations have led to an analytical impregnation model with a high accuracy. A deviation of 5% for more than 82% of the specimens was achieved.
This work presents novel simulation techniques for injection molding of fiber reinforced polymers. These include approaches for anisotropic flow modeling, hydrodynamic forces from fluid on fibers, contact forces between fibers, a novel fiber breakage modeling approach and anisotropic warpage analysis. Due to the coupling of fiber breakage and anisotropic flow modeling, the fiber breakage directly influences the modeled cavity pressure, which is validated with experimental data.
In this work, contributes to the optimization of local continuous fiber reinforcement patches, under consideration of manufacturing constraints. This approach requires specific optimization strategies. Therefore, an multi-objective optimization strategy for the placement of local reinforcement patches, under consideration of manufacturing constraints, has been developed. During the multi objective optimization, structural and process related objectives are considered.
This work aims at improving the energy consumption forecast of electric vehicles by enhancing the prediction with a notion of uncertainty. The algorithm itself learns from driver and traffic data in a training set to generate accurate, driver-individual energy consumption forecasts.
Fiber-reinforced materials offer a huge potential for lightweight design of load-bearing structures. However, high-volume production of such parts is still a challenge in terms of cost efficiency and competitiveness. Numerical process simulation can be used to analyze underlying mechanisms and to find a suitable process design. In this study, the curing process of the resin is investigated with regard to its influence on RTM mold filling and process-induced distortion.
With the further development of automated driving, the functional performance increases resulting in the need for new and comprehensive testing concepts. This doctoral work aims to enable the transition from quantitative mileage to qualitative test coverage by aggregating the results of both knowledge-based and data-driven test platforms. The validity of the test domain can be extended cost-effectively throughout the software development process to achieve meaningful test termination criteria.
