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Manufacturing-constrained multi-objective optimization of local patch reinforcements for discontinuous fiber reinforced composite parts
In this work, contributes to the optimization of local continuous fiber reinforcement patches, under consideration of manufacturing constraints. This approach requires specific optimization strategies. Therefore, an multi-objective optimization strategy for the placement of local reinforcement patches, under consideration of manufacturing constraints, has been developed. During the multi objective optimization, structural and process related objectives are considered.
Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles with Federated Learning
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.
AI and IoT Meet Mobile Machines: Towards a Smart Working Site
Infrastructure construction is society's cornerstone and economics' catalyst. Therefore, improving mobile machinery's efficiency and reducing their cost of use have enormous economic benefits in the vast and growing construction market. In this thesis, I envision a novel concept smart working site to increase productivity through fleet management from multiple aspects and with Artificial Intelligence (AI) and Internet of Things (IoT).
Discontinuous Fiber Composites
This book is a printed edition of the Special Issue "Discontinuous Fiber Composites" that was published in J. Compos. Sci.
Experimental investigation and process simulation of the compression molding process of Sheet Molding Compound (SMC) with local reinforcements
In this book, a new three-dimensional approach for the process simulation of SMC is developed. This approach takes into account both, the core layer that is dominated by the extensional viscosity and the thin lubrication layer. In order to transfer the information from the process to the structure simulation, a CAE chain is further developed. In addition, a new rheological tool is developed to analyze flow behavior experimentally and to provide the required material parameters.
Process simulation of wet compression moulding for continuous fibre-reinforced polymers
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.
Development of a CO2e quantification method and of solutions for reducing the greenhouse gas emissions of construction machines
This work focuses on the development of a quantification method for GHG (CO2e) emissions from construction machines. The method considers CO2e reduction potentials in the time past-present–future, through influencing factors from six pillars: Machine efficiency, process efficiency, energy source, operating efficiency, material efficiency and CCS. In addition, transformation solutions are proposed to reduce GHG emissions from construction machines like liquid methane, fuel cell drive or CCS.
Trajectory optimization based on recursive B-spline approximation for automated longitudinal control of a battery electric vehicle
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.
Stochastic Range Estimation Algorithms for Electric Vehicles using Data-Driven Learning Models
This work aims at improving the energy consumption forecast of electric vehicles by enhancing the prediction with a notion of uncertainty. The algorithm itself learns from driver and traffic data in a training set to generate accurate, driver-individual energy consumption forecasts.
Numerical prediction of curing and process-induced distortion of composite structures
Fiber-reinforced materials offer a huge potential for lightweight design of load-bearing structures. However, high-volume production of such parts is still a challenge in terms of cost efficiency and competitiveness. Numerical process simulation can be used to analyze underlying mechanisms and to find a suitable process design. In this study, the curing process of the resin is investigated with regard to its influence on RTM mold filling and process-induced distortion.
