This Dagstuhl Seminar will focus on the topic of shape and topology representation and optimization, in particular in the context of additive manufacturing (AM), and tackling the generation and use of 3D content for digital twins with controlled accuracy and desired properties at different scales.

Central to the ability to perform design optimization is the ability to capture, process and optimize shapes and topologies, allowing to work on a digital twin for design, or capture with geometry, topology and attached property during manufacturing or later. Topology optimization and the use of lattice structures helps make new designs that better exploit the potential of AM than traditional methods. New modeling tools and techniques are desperately needed to support the specialized needs of geometric modeling in such a computationally intensive and highly automated environment in order to leverage industry 4.0 at scale. We shall also consider the broader context of industry 5.0 aiming at developing sustainable solutions.

CAD based on B-rep targets subtractive manufacturing technologies and assumes that the material properties are isotropic and uniform throughout an object. This is contrary to objects produced by AM, where an object is built layer by layer until the final shape is reached. AM allows variable material combinations, lattice structures and voids but objects will in general exhibit anisotropic material behaviors that are dependent on the actual printing parameter used. Consequently, in AM there is a need for also representing and simulating the interior of an object both at macro, meso and micro scale to support anisotropic properties and complex internal geometric shapes.

Analysis-based design brings performance insights forward into the design phase. This Dagstuhl Seminar will explore recent developments in shape design and simulation for discrete or continuous shapes. In the continuous setting, the combination of the locally refined splines and topology structures for isogeometric analysis (IgA) constitutes a new foundation for CAD-representation, where the interior of volumes is described mathematically (Volume-representation, or V-rep). The seminar will address the interplay of IgA and V-rep and their link to AM. We will also consider some recent approaches using implicit or volumetric models for lattice structures and internal voids, which can provide extremely lightweight representations but may suffer from limitations of accuracy. The seminar will present industrial design challenges occurring from the AM processes, both for smooth shapes and shape with pattern, so that scientific ideas and approaches may emanate.

Decades-old research challenges in merging optimization methods, geometric design methods, and engineering analysis methods remain open, and are increasing in relevance as AM becomes a manufacturing mainstay. This relates not only to assembling multidisciplinary analysis and optimization capabilities, but also in integrating new analysis tools and rapidly adjusting computing tool streams to account for new design challenges. Making effective use of these new modeling algorithms and techniques requires us to continue to advance the state of the art in geometry processing, and data management, analysis, optimization and learning for these large volumes of 3D geometric data.

Creative Commons BY 4.0

Tor Dokken, Xiaohong Jia, Géraldine Morin, and Elissa Ross

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• Massimo Carraturo (University of Pavia, IT) [dblp]
• Falai Chen (Univ. of Science & Technology of China - Anhui, CN) [dblp]
• Tor Dokken (SINTEF - Oslo, NO) [dblp]
• Gershon Elber (Technion - Haifa, IL) [dblp]
• Konstantinos Gavriil (SINTEF - Oslo, NO) [dblp]
• Carlotta Giannelli (University of Firenze, IT) [dblp]
• Ron Goldman (Rice University - Houston, US) [dblp]
• Hans Hagen (RPTU Kaiserslautern-Landau, DE) [dblp]
• Stefanie Hahmann (INRIA Grenoble Rhône-Alpes, FR) [dblp]
• Kai Hormann (University of Lugano, CH) [dblp]
• Xiaohong Jia (Chinese Academy of Sciences, CN) [dblp]
• Bert Jüttler (Johannes Kepler Universität Linz, AT) [dblp]
• Panagiotis Kaklis (The University of Strathclyde - Glasgow, GB) [dblp]
• Shahroz Khan (BAR Technologies - Portsmouth, GB) [dblp]
• Myung-Soo Kim (Seoul National University, KR) [dblp]
• Tae-wan Kim (Seoul National University, KR) [dblp]
• Stefan Kollmannsberger (Bauhaus-Universität Weimar, DE) [dblp]
• Jiri Kosinka (University of Groningen, NL) [dblp]
• Tom Lyche (University of Oslo, NO) [dblp]
• Zoë Marschner (Carnegie Mellon University - Pittsburgh, US) [dblp]
• Dominik Mokriš (MTU Aero Engines - München, DE) [dblp]
• Géraldine Morin (IRIT - University of Toulouse, FR) [dblp]
• Caitlin Mueller (MIT - Cambridge, US) [dblp]
• Georg Muntingh (SINTEF - Oslo, NO) [dblp]
• Suraj R. Musuvathy (nTopology - New York, US) [dblp]
• Baldwin Nsonga (Universität Leipzig, DE) [dblp]
• Daniele Panozzo (New York University, US) [dblp]
• Jörg Peters (University of Florida - Gainesville, US) [dblp]
• Jeff Poskin (The Boeing Company - Seattle, US) [dblp]
• Helmut Pottmann (TU Wien, AT) [dblp]
• David Reeves (Metafold 3D - Toronto, CA)
• Ulrich Reif (TU Darmstadt, DE) [dblp]
• Elissa Ross (Metafold 3D - Toronto, CA) [dblp]
• Péter Salvi (Budapest University of Technology and Economics, HU) [dblp]
• Maria Lucia Sampoli (University of Siena, IT) [dblp]
• Espen Sande (EPFL - Lausanne, CH) [dblp]
• Scott Schaefer (Texas A&M University - College Station, US) [dblp]
• Felix Scholz (Johannes Kepler Universität Linz, AT)
• Gunnar Schulze (ttrinckle 3D - Berlin, DE)
• Yongjie Jessica Zhang (Carnegie Mellon University - Pittsburgh, US) [dblp]
• Jianmin Zheng (Nanyang TU - Singapore, SG)
• Eric Zimmermann (FU Berlin, DE) [dblp]