The individualization of patient-specific ankle joint orthoses is becoming increasingly important and can be ideally realized by means of additive manufacturing. However, currently, there are no functional additively manufactured fiber-reinforced products that are used in the field of orthopedic treatment. In this paper, an approach as to how additively manufactured orthopedic products can be designed and produced quickly and flexibly in the future is presented. This is demonstrated using the example of a solid ankle–foot orthosis. For this purpose, test results on PETG-CF15, which were determined in a previous work, were integrated into a material map for an FEA simulation. Therewith, the question can be answered as to whether production parameters that were determined at the test specimen level can also be adapted to real, usable components. Furthermore, gait recordings were used as loading conditions to obtain exact results for the final product. In order to perfectly adapt the design of the splint to the user, a 3D scan of a foot was performed to obtain a perfect design space for topology optimization. This resulted in a patient-specific and stiffness-optimized product. Subsequently, it was demonstrated that the orthosis could be manufactured using fused layer modelling. Finally, a comparison between the conventional design and the consideration of AM-specific properties was made. On this basis, it can be stated that the wearing comfort of the patient-specific design is very good, but the tightening of the splint still needs to be improved.

It is undeniable that mechanical properties, such as the stiffness or residual strength of fibre-reinforced thermoplastics, are adversely affected by fatigue damage caused by cyclic loading. In order to quantify and predict this damage influence, a calculation approach was developed in the past for the subgroup of short-fibre-reinforced thermoplastics. In order to test and expand the applicability of this approach to the field of long-fibre-reinforced thermoplastics, the decrease in mechanical properties is investigated experimentally in this paper using PP LGF30, propylene reinforced with long glass fibres, as an example. The paper describes both the fatigue behaviour and the residual strength of the material after fatigue damage. A decrease in the residual strength of up to about 35% could be recorded. The paper also presents a modelling approach that predicts the orientation-dependent fatigue strength of the material, and furthermore allows for the calculation of its residual strength as a function of fatigue damage. The novelty of the contribution lies in the continuous modelling of fatigue behaviour for arbitrary oriented samples of long-fibre-reinforced thermoplastics and also in the prediction of its residual strength depending on previously induced fatigue damage.

Additively manufactured parts play an increasingly important role in structural applications. Fused Layer Modeling (FLM) has gained popularity due to its cost-efficiency and broad choice of materials, among them, short fibre reinforced filaments with high specific stiffness and strength. To design functional FLM parts, adequate material models for simulations are crucial, as these allow for reliable simulation within virtual product development. In this contribution, a new approach to derive FLM material models for short fibre reinforced parts is presented; it is based on simultaneous fitting of the nine orthotropic constants of a linear elastic material model using six specifically conceived tensile specimen geometries with varying build direction and different extrusion path patterns. The approach is applied to a 15 wt.% short carbon-fibre reinforced PETG filament with own experiments, conducted on a Zwick HTM 5020 servo-hydraulic high-speed testing machine. For validation, the displacement behavior of a geometrically more intricate demonstrator part, printed upright, under bending is predicted using simulation and compared to experimental data. The workflow proves stable and functional in calibration and validation. Open research questions are outlined.

Only by using accurate material data can precise simulation results be achieved. This principle also and especially applies in the field of crash simulation. However, in the simulation of short-fibre reinforced thermoplastics, material parameters are usually used that originate from the material testing of as-new samples. In order to get closer to the condition on the roads, where not only new vehicles are driving, the influence of service loads on the crashworthiness has to be investigated. This paper reports on studies of PBT GF30, a polybutylene terephthalate reinforced with 30% glass fibres, in which fatigue damage was induced in the material by cyclic loading. The residual strength was then determined in a high-speed experiment and compared with the strength of virgin samples. In order to enable the usability of the findings in the simulation, a modified failure criterion was implemented that takes the previous fatigue damage into account. The prediction quality of the simulation model was compared with the experimental findings and it can be concluded that there is good agreement.

This work presents a novel infill method for additive manufacturing, specifically designed to optimize material use and enhance stiffness in fused filament fabrication (FFF) parts through a geometry-aware, corrugated design inspired by sandwich structures. Unlike standard infill patterns, which typically employ uniform, space-filling grids that often disregard load-specific requirements, this method generates a cavity inside the component to be printed and fill the space between inner and outer contours with continuous, adaptable extrusion paths. This design enables consistent support and improved load distribution, making it particularly effective for parts under bending stresses, as it enhances structural resilience without requiring additional material. Simulations performed on a 10 cm³ test part using this method showed potential reductions in material consumption by up to 77% and a decrease in print time by 78%, while maintaining stiffness comparable to parts using conventional 100% grid infill. Additionally, simulations demonstrated that the new corrugated infill pattern provides near-isotropic stiffness, addressing the anisotropic limitations often seen in traditional infill designs that are sensitive to load orientation. This geometry-aware infill strategy thus contributes to balanced stiffness across complex geometries, enhancing reliability under mechanical loads. By integrating directly with slicer software, this approach simplifies advanced stiffness optimization without the necessity of finite element analysis-based topology optimization.

Fatigue life testing is a complex and costly matter, especially in the case of fibre-reinforced thermoplastics, where other parameters in addition to force alone must be taken into account. The number of tests required therefore increases significantly, especially if the influence of different fibre orientations is to be taken into account. It is therefore important to gain the greatest possible amount of knowledge from the limited number of available tests. In order to achieve this, this study aims to utilise adaptive sampling, which is used in numerous areas of computational engineering, for the design of experiments on fatigue life testing. Artificial neural networks (ANNs) are therefore trained on data for the short-fibre-reinforced material PBT GF30, and their areas of greatest model uncertainty are queried. This was undertaken with ANNs from various numbers of hidden layers, which were analysed for their performance. The ideal case turned out to be four hidden layers, for which a squared error as small as 1 × 10⁻³ was recorded. Locally resolved, the ANN was used to identify the region of greatest uncertainty for samples of vertical orientation and small numbers of cycles. With information such as this, additional data can be obtained in such uncertain regions in order to improve the model prediction—almost halving the recorded error to only 0.55 × 10⁻³. In this way, a model of comparable value can be found with less experimental effort, or a model of better quality can be set up with the same experimental effort.