@misc{FahrionDraegerLakatosetal., author = {Fahrion, Marc-Steffen and Draeger, Susan and Lakatos, Michael and Schmidt, Timo and Nickl, Christoph and Brombacher, Moritz and Bangalore, Deekshitha and Sundermann, Wolfgang}, title = {KLIBAU - Weiterentwicklung und Konkretisierung des Klimaangepassten Bauens : Handlungsempfehlungen f{\"u}r Planer und Architekten}, pages = {69}, language = {de} } @misc{WaltherSchmidtRaethetal., author = {Walther, Dominik and Schmidt, Leander and R{\"a}th, Timo and Schricker, Klaus and Bergmann, Jean Pierre and Sattler, Kai-Uwe and M{\"a}der, Patrick}, title = {Deep learning-driven active sheet positioning using linear actuators in laser beam butt welding of thin steel sheets}, series = {Journal of advanced joining processes}, volume = {11}, journal = {Journal of advanced joining processes}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {2666-3309}, doi = {10.1016/j.jajp.2025.100303}, pages = {1 -- 12}, abstract = {Welding thin steel sheets in industrial applications is difficult because joint gaps occur during the process, which can lead to weld interruptions. Such welds are considered a reject and in order to avoid the weld to interrupt it is crucial to hinder the formation of joint gaps. Especially laser beam welding is affected by the emergence of gaps. Due to the narrow laser spot, product quality is highly dependent on the alignment and positioning of the sheets. This is typically done by clamping devices, which hold the workpieces in place. However, these clamps are suited for a specific workpiece geometry and require manual redesign every time the process changes. Adaptive clamping devices instead are designed to realize a time-dependent workpiece adjustment. Modeling the joint gap behavior to realize a controller for adaptive clamps can be difficult as the influence of heating, melting, and cooling on the joint gap formation is unknown and varies due to temperature dependent physical properties. Instead, the control parameters and actions can be derived using data-driven methods. In this paper, we present a novel data-driven approach how deep learning can be utilized to manipulate the sheet position during the weld with two actuators that apply force. A temporal convolution neural network (TCN) analyzes the change of the joint gap and predicts the required force to adapt the workpiece position. The developed method has been integrated into the welding process and improves the length of the average weld seam by 39.5\% compared to welds without an active adjustment and 1.4\% to welds that have been adapted with a constant force.}, language = {en} } @misc{HeymanProniziusLewisetal., author = {Heyman, Tom and Pronizius, Ekaterina and Lewis, Savannah C. and Acar, Oguz A. and Adamkovič, Mat{\´u}š and Ambrosini, Ettore and Antfolk, Jan and Barzykowski, Krystian and Baskin, Ernest and Batres, Carlota and Boucher, Leanne and Boudesseul, Jordane and Brandst{\"a}tter, Eduard and Collins, W. Matthew and Filipović Ðurđević, Dušica and Egan, Ciara and Era, Vanessa and Ferreira, Paulo and Fini, Chiara and Garrido-V{\´a}squez, Patricia and Godbersen, Hendrik and Gomez, Pablo and Graton, Aurelien and Gurkan, Necdet and He, Zhiran and Johnson, Dave C. and Kačm{\´a}r, Pavol and Koch, Chris and Kowal, Marta and Kratochvil, Tomas and Marelli, Marco and Marmolejo-Ramos, Fernando and Mart{\´i}nez, Mart{\´i}n and Mattiassi, Alan and Maxwell, Nicholas P. and Montefinese, Maria and Morvinski, Coby and Neta, Maital and Nielsen, Yngwie A. and Ocklenburg, Sebastian and Onič, Jaš and Papadatou-Pastou, Marietta and Parker, Adam J. and Paruzel-Czachura, Mariola and Pavlov, Yuri G. and Perea, Manuel and Pfuhl, Gerit and Roembke, Tanja C. and R{\"o}er, Jan P. and Roettger, Timo B and Ruiz-Fernandez, Susana and Schmidt, Kathleen}, title = {Crowdsourcing multiverse analyses to explore the impact of different data-processing and analysis decisions : a tutorial}, series = {Psychological methods}, journal = {Psychological methods}, publisher = {American Psychological Association (APA)}, address = {Washington, DC}, issn = {1939-1463}, doi = {10.1037/met0000770}, abstract = {When processing and analyzing empirical data, researchers regularly face choices that may appear arbitrary (e.g., how to define and handle outliers). If one chooses to exclusively focus on a particular option and conduct a single analysis, its outcome might be of limited utility. That is, one remains agnostic regarding the generalizability of the results, because plausible alternative paths remain unexplored. A multiverse analysis offers a solution to this issue by exploring the various choices pertaining to data-processing and/or model building, and examining their impact on the conclusion of a study. However, even though multiverse analyses are arguably less susceptible to biases compared to the typical single-pathway approach, it is still possible to selectively add or omit pathways. To address this issue, we outline a novel, more principled approach to conducting multiverse analyses through crowdsourcing. The approach is detailed in a step-by-step tutorial to facilitate its implementation. We also provide a worked-out illustration featuring the Semantic Priming Across Many Languages project, thereby demonstrating its feasibility and its ability to increase objectivity and transparency. (PsycInfo Database Record (c) 2026 APA, all rights reserved).}, language = {en} }