Recycling carbon fibers (rCF) for reuse is one approach to improve the sustainability of CFRP. However, until now, recycled carbon fiber plastics (rCFRP) had limited composite properties due to the microgeometry of the fibers, which made it difficult to use in load-bearing components. The production of hybrid yarns from rCF and PA6 fibers allows the fibers to be aligned. The geometric properties of the yarn and the individual fibers influence the mechanical properties of the composite. An approach for the modeling and simulation of hybrid yarns consisting of recycled carbon fibers and thermoplastic fibers is presented. The yarn unit cell geometry is modeled in the form of a stochastic fiber network. The fiber trajectory is modeled in form of helical curves using the idealized yarn model of Hearle et al. The variability in the fiber geometry (e.g., length) is included in form of statistical distributions. An additional compaction step ensures a realistic composite geometry. The created model is validated geometrically and by comparison with tensile tests of manufactured composites. With the validated model, multiple parameter studies investigating the influence of fiber and yarn geometry are carried out.

Shell-rib structures made of textile-reinforced composites are used in a wide range of applications to increase bending, buckling and torsional stiffness. Such composites are usually manufactured in differential construction at the preform level by assembling several textile structures or at the component level by the subsequent joining of separately manufactured shells and stiffening structures. Integral preform production is one way to overcome the disadvantages of the forenamed methods, such as high manual effort, failure during assembling or fiber distortion. Weft-knitting technology is excellent for achieving integral preforms for shell-rib components with a strong connection between the shell and the rib, especially while producing biaxial weft-knitted fabrics (BWKF) with reinforcing yarns in the warp and weft direction to improve its mechanical behavior. In this work, the possibilities of the knitting technique are investigated, and a finite element model for comparing different variants is developed and validated. A meso-scale Finite-Element-Method (FEM) model of the BWKF is used. The simulation results with the meso-scale model show a good correlation with experimental data by a description of bending strength and stiffness of different FRP configuration variations. The model can be used in further investigation of fiber-reinforced polymer (FRP) made from BWKF.

Tendons and ligaments are complex tissues that are necessary for human movement. Injuries occur very commonly and treatment quite often requires implants. Such implants must be adapted to the biological and structural composition of human tendons and ligaments. Thus, the objective is to realize graded, biomimetic tendon and ligament implants that are long-term resorbable. First, basic woven fabrics are fabricated from biocompatible silk fibroin yarns. Starting from the basic fabrics, gradient fabrics, with three different weave zones, are then developed and produced. In addition, fabrics with variable width and lateral warp yarn offset are fabricated on the basis of open reed weaving (ORW) technology on a modified shuttle narrow weaving loom. Meso-scale finite element models are developed in order to support the design of the gradient weaves. First, TexGen software is used to create a close to reality fabric geometry. Models are then converted into beam element models using a Python script. Results of real and virtual tensile tests show a clear relationship between the crimp of the warp yarns in the fabric structures and the resulting elongations. The additional ORW yarn system influences the stiffness. The tensile behavior of experiments and simulation agree very well, so the models are suitable for further development of woven implants.

Materials that can change shape in response to external parameters are becoming essential in developing future technologies in robotics, aerospace, and smart systems. This study focuses on the development of fiber-reinforced composites embedded with shape memory alloys (SMA) to enable localized bending and twisting deformations. By carefully designing the internal fiber structure and incorporating SMA wires, the composites were fabricated to deform in specific ways when activated. Multiple design variants were created to study how changes in fiber orientation, number of SMA wires and localized stiffness influence the deformation behavior. A depth-sensing camera setup with synchronized Azure Kinect devices was used to capture and analyze the resulting 3D shape changes. The obtained data facilitated the identification and tracking of specific points on the composite, enabling precise deformation measurement. The results highlight the significant impact of fiber angles and stiff sections on guiding bending and twisting behavior, contributing valuable insights toward the design of adaptive smart materials with controlled, complex deformation capabilities.

The growing use of carbon fibre-reinforced polymers (CFRP) results in an increased amount of CF waste from offcuts or end-of-life components. A promising method to reuse the waste fibre materials in a structural component with excellent mechanical properties is the processing of recycled CF (rCF) and thermoplastic fibres into hybrid yarns. Spinning of friction spun yarns consisting of more than 90% rCF and containing almost no thermoplastic fibres that are suitable for thermoset composites, currently leads to high fibre damage and low yarn quality and is, therefore, addressed in this project. The technology is reported in another paper. One of the limiting factors for drapability of textiles is the stretchability of continuous fibres and draping of the semi-finished textile products for complex geometries is still error-prone. Friction spun yarns exhibit significantly higher yarn elongations due to sliding mechanisms between the fibres. The deformation properties of friction spun yarns are significantly influenced by fibre-fibre interactions and depend on a variety of process and material parameters. In the following, micromechanical finite element models of the spun yarns are created by using beam elements. Monte Carlo method is used to model local variabilities in the yarns. The models are then used to simulate yarn behaviour under deformation and to investigate the influence of various process parameters.

The development of different hybrid yarn structures from recycled carbon fibre (rCF) (rCF content approx. 50% by weight) and thermoplastic fibres for thermoplastic composites has been reported earlier. However, manufacturing of yarns with high rCF content (>90%) required for thermoset composites is still not realizable due to high shortening (≥ 70%) in fibre length of rCF, which occurs during different processing steps of spinning. The reason lies in low shear strength, smooth fibre surface, small diameter and high brittleness of rCF. In addition to this, lack of crimp in rCF leads to drafting error during drawing and spinning process. Therefore, there is a high demand on rCF yarns for thermoset composites, as around 70% of composites are produced based on thermoset matrix. In this paper, yarns consisting of staple rCF with high rCF content (>90 weight%) are developed on DREF-friction spinning and wrap spinning technologies. For the production of yarns, slivers with different rCF content are produced using carding and drawing machine. The effect of different spinning parameters suction air pressure for DREF friction spun yarns and yarn twist for wrap spun yarns is investigated and their effect on tensile properties of yarn is analysed. The results show that the tensile properties of yarns can be adjusted to a wide range varying the yarn structure and spinning parameters.