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 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.

These ESAFORM 2024 conference proceedings cover a wide range of topics: Additive manufacturing; Composites forming processes; Extrusion and drawing; Forging and rolling; Formability of metallic materials; Friction and wear in metal forming; Incremental and sheet metal forming; Innovative joining by forming technologies; Optimization and inverse analysis in forming; Machining, Cutting and severe plastic deformation processes; Material behavior modelling; New and advanced numerical strategies for material forming; Non-conventional processes; Polymer processing and thermomechanical properties; Sustainability on material forming. Keywords: WAAM Technology, Fused deposition Modeling (FDM), Fiber Composite Printers, Ultrasonic Powder Atomization, Finite Element Modeling (FEM), Laser Powder Bed Fusion (L-PBF), Rapid Prototyping in Additive Manufacturing, Directed Energy Deposition (DED), GTAW Droplet Deposition, Deep Learning, Thermoplastic Pultrusion, Textile Reinforcements, Thermoforming Simulation, New Sustainable Materials, Non-Crimp Fabrics, CFRP Scraps, PEEK Composites, Thermoplastic Sheets, Flax/PP Composites.

In this work, an extension of the federated averaging algorithm, FedAvg-Gaussian, is applied to train probabilistic neural networks. The performance advantage of probabilistic prediction models is demonstrated and it is shown that federated learning can improve driving range prediction. Using probabilistic predictions, routing and charge planning based on destination attainability can be applied. Furthermore, it is shown that probabilistic predictions lead to reduced travel time.

This work describes a method for weighted least squares approximation of an unbounded number of data points using a B-spline function. The method can shift the bounded B-spline function definition range during run-time. The approximation method is used for optimizing velocity trajectories for an electric vehicle with respect to travel time, comfort and energy consumption. The trajectory optimization method is extended to a driver assistance system for automated vehicle longitudinal control.

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.

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.

These proceedings present papers on Additive Manufacturing, Composites Forming Processes, Extrusion and Drawing, Forging and Rolling, Formability of Metallic Materials, Friction and Wear in Metal Forming, Incremental and Sheet Metal Forming, Innovative Joining by Forming Technologies, Lionel Fourment MS on Optimization and Inverse Analysis in Forming, Machining and Cutting, Material Behavior Modelling, New and Advanced Numerical Strategies for Material Forming, Non-Conventional Processes, Polymer Processing and Thermomechanical Properties, Sustainability on Material Forming, and Property-Controlled Forming.

In the last few decades, natural fibers have received growing attention as an alternative to the synthetic fibers used in the reinforcement of polymeric composites, thanks to their specific properties, low price, health advantages, renewability, and recyclability. Furthermore, natural fibers have a CO2-neutral life cycle, in contrast to their synthetic counterparts. As is widely known, natural fibers also possess some drawbacks, e.g., a hydrophilic nature, low and variable mechanical properties, poor adhesion to polymeric matrices, high susceptibility to moisture absorption, low aging resistance, etc. This implies that their applications are limited to non-structural interior products. To overcome this problem, the hybridization of natural fibers with synthetic ones (i.e., glass, carbon, and basalt) or different natural fibers can be a solution. For this reason, extensive research concerning natural–synthetic and natural–natural hybrid composites has been done in the last years. In this context, this book aims to collect some interesting papers concerning the use of natural fibers together with synthetic ones with the aim of obtaining hybrid structures with good compromise between high properties (e.g., mechanical performances, thermal behavior, aging tolerance in humid or aggressive environments, and so on) and environment care.