Dagstuhl Seminar 24241
Geometric modeling: Challenges for Additive Manufacturing, Design and Analysis
( Jun 09 – Jun 14, 2024 )
Permalink
Please use the following short url to reference this page:
https://www.dagstuhl.de/24241
Organizers
- Tor Dokken (SINTEF - Oslo, NO)
- Xiaohong Jia (Chinese Academy of Sciences, CN)
- Géraldine Morin (IRIT - University of Toulouse, FR)
- Elissa Ross (Metafold 3D - Toronto, CA)
Contact
- Michael Gerke (for scientific matters)
- Susanne Bach-Bernhard (for administrative matters)
Dagstuhl Seminar Wiki
- Dagstuhl Seminar Wiki (Use personal credentials as created in DOOR to log in)
Shared Documents
- Dagstuhl Materials Page (Use personal credentials as created in DOOR to log in)
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
- Massimo Carraturo (University of Pavia, IT)
- 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)
- Carlotta Giannelli (University of Firenze, IT) [dblp]
- Ron Goldman (Rice University - Houston, US) [dblp]
- Hans Hagen (RPTU - Kaiserslautern, DE) [dblp]
- Stefanie Hahmann (INRIA Grenoble Rhône-Alpes, FR) [dblp]
- Kai Hormann (University of Lugano, CH) [dblp]
- Xiaohong Jia (Chinese Academy of Sciences, CN)
- Bert Jüttler (Johannes Kepler Universität Linz, AT) [dblp]
- Myung-Soo Kim (Seoul National University, KR) [dblp]
- Tae-wan Kim (Seoul National University, KR) [dblp]
- Stefan Kollmannsberger (TU München, DE)
- Jiri Kosinka (University of Groningen, NL)
- David I. W. Levin (University of Toronto, CA) [dblp]
- Tom Lyche (University of Oslo, NO) [dblp]
- Zoë Marschner (Carnegie Mellon University - Pittsburgh, US)
- Dominik Mokriš (MTU Aero Engines - München, DE) [dblp]
- Géraldine Morin (IRIT - University of Toulouse, FR) [dblp]
- Caitlin Mueller (MIT - Cambridge, US)
- Georg Muntingh (SINTEF - Oslo, NO) [dblp]
- Suraj R. Musuvathy (nTopology - New York, US) [dblp]
- Saigopal Nelaturi (Carbon Inc. - Redwood City, US)
- Daniele Panozzo (New York University, US) [dblp]
- Jorg Peters (University of Florida - Gainesville, US) [dblp]
- Jeff Poskin (The Boeing Company - Seattle, US)
- 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)
- Maria Lucia Sampoli (University of Siena, IT) [dblp]
- Espen Sande (EPFL - Lausanne, CH)
- Scott Schaefer (Texas A&M University - College Station, US) [dblp]
- Nelly Villamizar (Swansea University, GB) [dblp]
- Wenping Wang (Texas A&M University - College Station, US) [dblp]
- Yongjie Jessica Zhang (Carnegie Mellon University - Pittsburgh, US) [dblp]
- Jianmin Zheng (Nanyang TU - Singapore, SG)
- Eric Zimmermann (FU Berlin, DE)
Related Seminars
- Dagstuhl Seminar 9127: Geometric Modelling (1991-07-01 - 1991-07-05) (Details)
- Dagstuhl Seminar 9326: Geometric Modelling (1993-06-28 - 1993-07-02) (Details)
- Dagstuhl Seminar 9622: Geometric Modelling (1996-05-27 - 1996-05-31) (Details)
- Dagstuhl Seminar 99201: Geometric Modelling (1999-05-16 - 1999-05-21) (Details)
- Dagstuhl Seminar 02201: Geometric Modelling (2002-05-12 - 2002-05-17) (Details)
- Dagstuhl Seminar 05221: Geometric Modeling (2005-05-29 - 2005-06-03) (Details)
- Dagstuhl Seminar 08221: Geometric Modeling (2008-05-25 - 2008-05-30) (Details)
- Dagstuhl Seminar 11211: Geometric Modeling (2011-05-22 - 2011-05-27) (Details)
- Dagstuhl Seminar 14221: Geometric Modeling (2014-05-25 - 2014-05-30) (Details)
- Dagstuhl Seminar 17221: Geometric Modelling, Interoperability and New Challenges (2017-05-28 - 2017-06-02) (Details)
- Dagstuhl Seminar 21471: Geometric Modeling: Interoperability and New Challenges (2021-11-21 - 2021-11-26) (Details)
Classification
- Computational Geometry
- Graphics
- Machine Learning
Keywords
- Geometric Modeling
- Additive Manufacturing
- isogeometric analysis
- machine learning
