In this work a holistic approach for the characterization and mathematical modeling of the reaction kinetics of a fast epoxy resin is shown. Major composite manufacturing processes like resin transfer molding involve isothermal curing at temperatures far below the ultimate glass transition temperature. Hence, premature vitrification occurs during curing and consequently has to be taken into account by the kinetic model. In order to show the benefit of using a complex kinetic model, the Kamal-Malkin kinetic model is compared to the Grindling kinetic model in terms of prediction quality for isothermal processing. From the selected models, only the Grindling kinetic is capable of taking into account vitrification. Non-isothermal, isothermal and combined differential scanning calorimetry (DSC) measurements are conducted and processed for subsequent use for model parametrization. In order to demonstrate which DSC measurements are vital for proper cure modeling, both models are fitted to varying sets of measurements. Special attention is given to the evaluation of isothermal DSC measurements which are subject to deviations arising from unrecorded cross-linking prior to the beginning of the measurement as well as from physical aging effects. It is found that isothermal measurements are vital for accurate modeling of isothermal cure and cannot be neglected. Accurate cure predictions are achieved using the Grindling kinetic model.

The reactive process of reinforced thermoset injection molding significantly influences the mechanical properties of the final composite structure. Therefore, reliable process simulation is crucial to predict the process behavior and relevant process effects. Virtual process design is thus highly important for the composite manufacturing industry for creating high quality parts. Although thermoset injection molding shows a more complex flow behavior, state of the art molding simulation software typically focusses on thermoplastic injection molding. To overcome this gap in virtual process prediction, the present work proposes a finite volume (FV) based simulation method, which models the multiphase flow with phase-dependent boundary conditions. Compared to state-of-the-art Finite-Element-based approaches, Finite-Volume-Method (FVM) provides more adequate multiphase flow modeling by calculating the flow at the cell surfaces with an Eulerian approach. The new method also enables the description of a flow region with partial wall contact. Furthermore, fiber orientation, curing and viscosity models are used to simulate the reinforced reactive injection molding process. The open source Computational-Fluid-Dynamics (CFD) toolbox OpenFOAM is used for implementation. The solver is validated with experimental pressure data recorded during mold filling. Additionally, the simulation results are compared to commercial Finite-Element-Method software. The simulation results of the new FV-based CFD method fit well with the experimental data, showing that FVM has a high potential for modeling reinforced reactive injection molding.

Process-induced distortion of composite structures often leads to a violation of tolerances, making the assembly of components difficult and expensive. It therefore can inhibit a cost-effective mass production of high-performance composite structures. Process-induced distortion is often introduced by curved regions of a part due to spring-in. Main drivers are chemical shrinkage of the resin and thermal expansion of both fiber and resin during cooling after demolding. Both contribute to residual strains and consequently lead to distortion of the manufactured part. The spring-in phenomenon has been already addressed in many studies. However, variations in manufacturing and specimen properties inhibit a detailed comparison of the results. Hence, it is difficult to isolate major influencing parameters. Here we show spring-in results of specimens that were manufactured using the very same experimental setup and laminate configuration but different resin and fiber types. It is therefore possible to identify the interaction of the curing temperature and the maximum achievable glass transition temperature of the individual resins as a major influencing factor. Furthermore, it is shown that the properties of the investigated resins do not differ largely in terms of thermal expansion and chemical shrinkage. Moreover, the latter was measured using two different techniques to enable a comparison. Numerical spring-in prediction revealed good accuracy throughout the investigated specimen configurations. Limitations found are the influence of the sewing of fiber textiles and the sensitivity of the model to gradual changes of the layup. Moreover, different homogenization techniques are compared with regard to spring-in prediction accuracy.