Deep convolutional generative adversarial network for generation of computed tomography images of discontinuously carbon fiber reinforced polymer microstructures.

Computed tomography images are of utmost importance when characterizing the heterogeneous and complex microstructure of discontinuously fiber reinforced polymers. However, the devices are expensive and the scans are time- and energy-intensive. Through recent advances in generative adversarial networks, the instantaneous generation of endless numbers of images that are representative of the input images and hold physical significance becomes possible. Hence, this work presents a deep convolutional generative adversarial network trained on approximately 30,000 input images from carbon fiber reinforced polyamide 6 computed tomography scans. The challenge lies in the low contrast between the two constituents caused by the close proximity of the density of polyamide 6 and carbon fibers as well as the small fiber diameter compared to the necessary resolution of the images. In addition, the stochastic, heterogeneous microstructure does not follow any logical or predictable rules exacerbating their generation. The quality of the images generated by the trained network of 256 pixel × 256 pixel was investigated through the Fréchet inception distance and nearest neighbor considerations based on Euclidean distance and structural similarity index measure. Additional visual qualitative assessment ensured the realistic depiction of the complex mixed single fiber and fiber bundle structure alongside flow-related physically feasible positioning of the fibers in the polymer. The authors foresee additionally huge potential in creating three-dimensional representative volume elements typically used in composites homogenization.

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.

The Impact of Draping Effects on the Stiffness and Failure Behavior of Unidirectional Non-Crimp Fabric Fiber Reinforced Composites.

Unidirectional non-crimp fabrics (UD-NCF) are often used to exploit the lightweight potential of continuous fiber reinforced plastics (CoFRP). During the draping process, the UD-NCF fabric can undergo large deformations that alter the local fiber orientation, the local fiber volume content (FVC) and create local fiber waviness. Especially the FVC is affected and has a large impact on the mechanical properties. This impact, resulting from different deformation modes during draping, is in general not considered in composite design processes. To analyze the impact of different draping effects on the mechanical properties and the failure behavior of UD-NCF composites, experimental results of reference laminates are compared to the results of laminates with specifically induced draping effects, such as non-constant FVC and fiber waviness. Furthermore, an analytical model to predict the failure strengths of UD laminates with in-plane waviness is introduced. The resulting stiffness and strength values for different FVC or amplitude to wavelength configurations are presented and discussed. In addition, failure envelopes based on the PUCKmathsizesmall failure criterion for each draping effect are derived, which show a clear specific impact on the mechanical properties. The findings suggest that each draping effect leads to a "new fabric" type. Additionally, analytical models are introduced and the experimental results are compared to the predictions. Results indicate that the models provide reliable predictions for each draping effect. Recommendations regarding necessary tests to consider each draping effect are presented. As a further prospect the resulting stiffness and strength values for each draping effect can be used for a more accurate prediction of the structural performance of CoFRP parts.

Accurate Cure Modeling for Isothermal Processing of Fast Curing Epoxy Resins.

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.