TY - CHAP A1 - Englhard, Markus A1 - Weber, Tobias A1 - Arent, Jan-Christoph T1 - Efficiency enhancement for CFRP-Prepregautoclave manufacturing by means of simulation-assisted loading optimization T2 - Proceedings of SAMPE Europe Conference 2021 N2 - A new method for improved autoclave loading within the restrictive framework of helicopter manufacturing is proposed. It is derived from experimental and numerical studies of the curing process and aims at optimizing tooling positions in the autoclave for fast and homogeneous heat-up. The mold positioning is based on two sets of information. The thermal properties of the molds, which can be determined via semi-empirical thermal simulation. The second information is a previously determined distribution of heat transfer coefficients inside the autoclave. Finally, an experimental proof of concept is performed to show a cycle time reduction of up to 31% using the proposed methodology. Y1 - 2021 ER - TY - CHAP A1 - Nikolovski, Gjorgji A1 - Reke, Michael A1 - Elsen, Ingo A1 - Schiffer, Stefan T1 - Machine learning based 3D object detection for navigation in unstructured environments T2 - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) N2 - In this paper we investigate the use of deep neural networks for 3D object detection in uncommon, unstructured environments such as in an open-pit mine. While neural nets are frequently used for object detection in regular autonomous driving applications, more unusual driving scenarios aside street traffic pose additional challenges. For one, the collection of appropriate data sets to train the networks is an issue. For another, testing the performance of trained networks often requires tailored integration with the particular domain as well. While there exist different solutions for these problems in regular autonomous driving, there are only very few approaches that work for special domains just as well. We address both the challenges above in this work. First, we discuss two possible ways of acquiring data for training and evaluation. That is, we evaluate a semi-automated annotation of recorded LIDAR data and we examine synthetic data generation. Using these datasets we train and test different deep neural network for the task of object detection. Second, we propose a possible integration of a ROS2 detector module for an autonomous driving platform. Finally, we present the performance of three state-of-the-art deep neural networks in the domain of 3D object detection on a synthetic dataset and a smaller one containing a characteristic object from an open-pit mine. KW - 3D object detection KW - LiDAR KW - autonomous driving KW - Deep learning KW - Three-dimensional displays Y1 - 2021 SN - 978-1-6654-7921-9 U6 - http://dx.doi.org/10.1109/IVWorkshops54471.2021.9669218 N1 - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops), 11-17 July 2021, Nagoya, Japan. SP - 236 EP - 242 PB - IEEE ER - TY - CHAP A1 - Ritschel, Konstantin A1 - Stenzel, Adina A1 - Czarnecki, Christian A1 - Hong, Chin-Gi ED - Gesellschaft für Informatik e.V. (GI), T1 - Realizing robotic process automation potentials: an architectural perspective on a real-life implementation case T2 - GI Edition Proceedings Band 314 "INFORMATIK 2021" Computer Science & Sustainability N2 - The initial idea of Robotic Process Automation (RPA) is the automation of business processes through a simple emulation of user input and output by software robots. Hence, it can be assumed that no changes of the used software systems and existing Enterprise Architecture (EA) is required. In this short, practical paper we discuss this assumption based on a real-life implementation project. We show that a successful RPA implementation might require architectural work during analysis, implementation, and migration. As practical paper we focus on exemplary lessons-learned and new questions related to RPA and EA. KW - Robotic Process Automation KW - Enterprise Architecture KW - Implementation Case Y1 - 2021 SN - 9783885797081 SN - 1617-5468 N1 - INFORMATIK 2021 – 51. Jahrestagung der Gesellschaft für Informatik, 27. September – 01. Oktober 2021 / Virtuell, Berlin. https://informatik2021.gi.de/ SP - 1303 EP - 1311 PB - Köllen CY - Bonn ER - TY - CHAP A1 - El Moussaoui, Noureddine A1 - Kassmi, Khalil A1 - Alexopoulos, Spiros A1 - Schwarzer, Klemens A1 - Chayeb, Hamid A1 - Bachiri, Najib T1 - Simulation studies on a new innovative design of a hybrid solar distiller MSDH alimented with a thermal and photovoltaic energy T2 - Materialstoday: Proceedings Y1 - 2021 U6 - http://dx.doi.org/10.1016/j.matpr.2021.03.115 SN - 2214-7853 ER - TY - CHAP A1 - Ferrein, Alexander A1 - Meeßen, Marcus A1 - Limpert, Nicolas A1 - Schiffer, Stefan ED - Lepuschitz, Wilfried T1 - Compiling ROS Schooling Curricula via Contentual Taxonomies T2 - Robotics in Education Y1 - 2021 SN - 978-3-030-67411-3 U6 - http://dx.doi.org/10.1007/978-3-030-67411-3_5 N1 - RiE: International Conference on Robotics in Education (RiE); Advances in Intelligent Systems and Computing book series (AISC, volume 1316) SP - 49 EP - 60 PB - Springer CY - Cham ER - TY - CHAP A1 - Heuermann, Holger A1 - Harzheim, Thomas A1 - Mühmel, Marc T1 - A maritime harmonic radar search and rescue system using passive and active tags T2 - 2020 17th European Radar Conference (EuRAD) KW - Harmonic Radar KW - Rescue System KW - Frequency Doubler KW - Transponder KW - Tag Y1 - 2021 SN - 978-2-87487-061-3 U6 - http://dx.doi.org/10.1109/EuRAD48048.2021.00030 N1 - Proceedings of the 17th European Radar Conference, 13th - 15th January 2021, Utrecht, Netherlands SP - 73 EP - 76 PB - IEEE ER - TY - CHAP A1 - Funke, Harald A1 - Beckmann, Nils A1 - Keinz, Jan A1 - Horikawa, Atsushi T1 - 30 years of dry low NOx micromix combustor research for hydrogen-rich fuels: an overview of past and present activities T2 - Proceedings of the ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition, September 21–25, 2020, Virtual, Online. Vol.: 4B: Combustion, Fuels, and Emissions KW - Micromix KW - Hydrogen KW - Fuel-flexibility KW - NOx KW - Emissions Y1 - 2021 SN - 978-0-7918-8413-3 U6 - http://dx.doi.org/10.1115/GT2020-16328 N1 - Paper No. GT2020-16328, V04BT04A069 PB - American Society of Mechanical Engineers (ASME) ER - TY - CHAP A1 - Schulte, Maximilian A1 - Eggert, Mathias T1 - Predicting hourly bitcoin prices based on long short-term memory neural networks T2 - Proceedings of the International Conference on Wirtschaftsinformatik (WI) 2021 N2 - Bitcoin is a cryptocurrency and is considered a high-risk asset class whose price changes are difficult to predict. Current research focusses on daily price movements with a limited number of predictors. The paper at hand aims at identifying measurable indicators for Bitcoin price movement s and the development of a suitable forecasting model for hourly changes. The paper provides three research contributions. First, a set of significant indicators for predicting the Bitcoin price is identified. Second, the results of a trained Long Short-term Memory (LSTM) neural network that predicts price changes on an hourly basis is presented and compared with other algorithms. Third, the results foster discussions of the applicability of neural nets for stock price predictions. In total, 47 input features for a period of over 10 months could be retrieved to train a neural net that predicts the Bitcoin price movements with an error rate of 3.52 %. Y1 - 2021 N1 - 16th International Conference on Wirtschaftsinformatik, March 2021, Essen, Germany ER - TY - CHAP A1 - Schmidt, Thomas A1 - Kasch, Susanne A1 - Eichler, Fabian A1 - Thurn, Laura T1 - Process strategies on laser-based melting of glass powder T2 - Lasers in Manufacturing Conference 2021 N2 - This paper presents the laser-based powder bed fusion (L-PBF) using various glass powders (borosilicate and quartz glass). Compared to metals, these require adapted process strategies. First, the glass powders were characterized with regard to their material properties and their processability in the powder bed. This was followed by investigations of the melting behavior of the glass powders with different laser wavelengths (10.6 µm, 1070 nm). In particular, the experimental setup of a CO2 laser was adapted for the processing of glass powder. An experimental setup with integrated coaxial temperature measurement/control and an inductively heatable build platform was created. This allowed the L-PBF process to be carried out at the transformation temperature of the glasses. Furthermore, the component’s material quality was analyzed on three-dimensional test specimen with regard to porosity, roughness, density and geometrical accuracy in order to evaluate the developed L-PBF parameters and to open up possible applications. KW - 3D-printing KW - glass KW - additive manufactureing KW - laser based powder fusion Y1 - 2021 ER - TY - CHAP A1 - Kroniger, Daniel A1 - Horikawa, Atsushi A1 - Funke, Harald A1 - Pfäffle, Franziska A1 - Kishimoto, Tsuyoshi A1 - Okada, Koichi T1 - Experimental and numerical investigation on the effect of pressure on micromix hydrogen combustion T2 - ASME Turbo Expo 2021: Turbomachinery Technical Conference and Exposition // Volume 3A: Combustion, Fuels, and Emissions N2 - The micromix (MMX) combustion concept is a DLN gas turbine combustion technology designed for high hydrogen content fuels. Multiple non-premixed miniaturized flames based on jet in cross-flow (JICF) are inherently safe against flashback and ensure a stable operation in various operative conditions. The objective of this paper is to investigate the influence of pressure on the micromix flame with focus on the flame initiation point and the NOx emissions. A numerical model based on a steady RANS approach and the Complex Chemistry model with relevant reactions of the GRI 3.0 mechanism is used to predict the reactive flow and NOx emissions at various pressure conditions. Regarding the turbulence-chemical interaction, the Laminar Flame Concept (LFC) and the Eddy Dissipation Concept (EDC) are compared. The numerical results are validated against experimental results that have been acquired at a high pressure test facility for industrial can-type gas turbine combustors with regard to flame initiation and NOx emissions. The numerical approach is adequate to predict the flame initiation point and NOx emission trends. Interestingly, the flame shifts its initiation point during the pressure increase in upstream direction, whereby the flame attachment shifts from anchoring behind a downstream located bluff body towards anchoring directly at the hydrogen jet. The LFC predicts this change and the NOx emissions more accurately than the EDC. The resulting NOx correlation regarding the pressure is similar to a non-premixed type combustion configuration. KW - NOx emissions KW - hydrogen KW - combustor KW - gas turbine Y1 - 2021 U6 - http://dx.doi.org/10.1115/GT2021-58926 N1 - ASME Turbo Expo 2021: Turbomachinery Technical Conference and Exposition. June 7–11, 2021. Virtual, Online. Paper No: GT2021-58926, V03AT04A025 ER -