Author : Bernath, Alexander
Publisher : KIT Scientific Publishing
Page : 294 pages
File Size : 16,50 MB
Release : 2021-10-29
Category : Technology & Engineering
ISBN : 3731510634
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.
Author : Poppe, Christian Timo
Publisher : KIT Scientific Publishing
Page : 332 pages
File Size : 22,29 MB
Release : 2022-07-18
Category : Technology & Engineering
ISBN : 3731511908
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.
Author : Thorgeirsson, Adam Thor
Publisher : KIT Scientific Publishing
Page : 190 pages
File Size : 24,43 MB
Release : 2024-09-03
Category :
ISBN : 3731513714
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.
Author : Meyer, Nils
Publisher : KIT Scientific Publishing
Page : 292 pages
File Size : 21,77 MB
Release : 2022-07-12
Category : Technology & Engineering
ISBN : 3731511738
Sheet Molding Compounds (SMC) are discontinuous fiber reinforced composites that are widely applied due to their ability to realize composite parts with long fibers at low cost. A novel Direct Bundle Simulation (DBS) method is proposed in this work to enable a direct simulation at component scale utilizing the observation that fiber bundles often remain in a bundled configuration during SMC compression molding.
Author : Jauch, Jens
Publisher : KIT Scientific Publishing
Page : 264 pages
File Size : 46,92 MB
Release : 2024-03-01
Category :
ISBN : 3731513323
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.
Author : Pinay, Julien
Publisher : KIT Scientific Publishing
Page : 196 pages
File Size : 16,30 MB
Release : 2024-01-16
Category :
ISBN : 3731513285
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.
Author : Wittemann, Florian
Publisher : KIT Scientific Publishing
Page : 180 pages
File Size : 16,32 MB
Release : 2022-11-18
Category : Technology & Engineering
ISBN : 3731512173
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.
Author : Xiang, Yusheng
Publisher : KIT Scientific Publishing
Page : 294 pages
File Size : 48,58 MB
Release : 2022-06-20
Category : Technology & Engineering
ISBN : 3731511657
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).
Author : Elgharbawy, Mohamed
Publisher : KIT Scientific Publishing
Page : 268 pages
File Size : 41,90 MB
Release : 2023-01-13
Category : Technology & Engineering
ISBN : 3731512548
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.
Author : Scheubner, Stefan
Publisher : KIT Scientific Publishing
Page : 190 pages
File Size : 21,99 MB
Release : 2022-06-03
Category : Technology & Engineering
ISBN : 3731511665
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.
