@techreport{SternerThemaEckertetal., author = {Sterner, Michael and Thema, Martin and Eckert, Fabian and Moser, Albert and Sch{\"a}fer, Andreas and Drees, Tim and Christian Rehtanz, and Ulf H{\"a}ger, and Kays, Jan and Seack, Andr{\´e} and Dirk Uwe Sauer, and Matthias Leuthold, and Philipp St{\"o}cker,}, title = {Stromspeicher in der Energiewende - Untersuchung zum Bedarf an neuen Stromspeichern in Deutschland f{\"u}r den Erzeugungsausgleich, Systemdienstleistungen und im Verteilnetz}, publisher = {Agora Energiewende}, address = {Berlin}, doi = {10.13140/RG.2.2.31804.56964}, abstract = {Wie groß ist der Speicherbedarf in Deutschland in der weiteren Umsetzung der Energiewende? Welche Rolle spielen Batteriespeicher, Pumpspeicher, Power-to-Gas etc. im Kontext anderer Flexibilit{\"a}tsoptionen auf den verschiedenen Netzebenen? Wie entwickelt sich der Markt f{\"u}r Batterien und Wasserstoff? In unserer Agora-Speicherstudie haben wir auch erstmalig den Begriff Power-to-X definiert und damit die bis dato entstandenen Begriffe Power-to-Gas, Power-to-Liquids, Power-to-Products, Power-to-Chemicals etc. zusammengefasst.}, language = {de} } @misc{LehrerStockerDuddecketal., author = {Lehrer, Tobias and Stocker, Philipp and Duddeck, Fabian and Wagner, Marcus}, title = {Comparison of Low- vs. High-Dimensional Machine Learning Approaches for Sheet Metal Drawability Assessment}, series = {Third International Conference on Computational Science and AI in Industry (CSAI 2023), Trondheim, Norway, 28-30 August 2023}, journal = {Third International Conference on Computational Science and AI in Industry (CSAI 2023), Trondheim, Norway, 28-30 August 2023}, publisher = {International Centre for Numerical Methods in Engineering}, doi = {10.35096/othr/pub-6477}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-64772}, abstract = {Developing new deep-drawn sheet metal parts is a challenging task due to conflicting demands for low costs, durability, and crash properties. Ensuring manufacturability throughout geometrical changes adds to the complexity, leading engineers to rely on experience-driven iterative design changes that compromise requirements and lack reproducibility. Finite Element (FE) simulation models are employed to ensure manufacturability, albeit at the expense of high computational costs and delays in part development. To improve efficiency, a Machine learning (ML)-centered approach was proposed to ensure manufacturability. However, the limited availability of data raises uncertainty about whether a low- or high-dimensional ML approach is most suitable for drawability assessment. This work compares the accuracy of a low-dimensional, feature-based Linear Support Vector surrogate and an adapted high-dimensional PointNet model under different dataset sizes. The dataset is composed of parametrically generated, U-shaped structural sheet metal parts. We use a one-step simulation scheme and evaluate results with a Forming Limit Diagram (FLD) to label drawability. Results show the point of transition to be at about 500 training samples, from which onwards Deep learning is advantageous. Moreover, the generalizability of these models is tested on a second dataset with topologically similar components. This is to assess the potential for a geometrically more comprehensive evaluation. We discuss several influences on model performances and outline future potentials.}, language = {en} }