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Supervised learning approach to parametric computer-aided design geometry repair

Supervised learning approach to parametric computer-aided design geometry repair
Supervised learning approach to parametric computer-aided design geometry repair
Multidisciplinary optimization systems rely increasingly on parametric CAD engines to supply the geometries required by their analysis components. Such parametric geometry models usually result from an uneasy compromise between high flexibility, that is, the ability to morph into a wide variety of topologies and shapes, and robustness, the ability to produce feasible, sensible topologies and shapes throughout most of the design space.
It is argued that a possible means of achieving both objectives is via a supervised learning system attached to the CAD model. It is shown that such a model can capture some of the engineering and geometrical judgment of the designer and can thereafter be used to repair design variable sets that lead to infeasible CAD models.
0001-1452
282-289
Sobester, A.
096857b0-cad6-45ae-9ae6-e66b8cc5d81b
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
Sobester, A.
096857b0-cad6-45ae-9ae6-e66b8cc5d81b
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def

Sobester, A. and Keane, A.J. (2006) Supervised learning approach to parametric computer-aided design geometry repair. AIAA Journal, 44 (2), 282-289.

Record type: Article

Abstract

Multidisciplinary optimization systems rely increasingly on parametric CAD engines to supply the geometries required by their analysis components. Such parametric geometry models usually result from an uneasy compromise between high flexibility, that is, the ability to morph into a wide variety of topologies and shapes, and robustness, the ability to produce feasible, sensible topologies and shapes throughout most of the design space.
It is argued that a possible means of achieving both objectives is via a supervised learning system attached to the CAD model. It is shown that such a model can capture some of the engineering and geometrical judgment of the designer and can thereafter be used to repair design variable sets that lead to infeasible CAD models.

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Published date: 2006

Identifiers

Local EPrints ID: 23906
URI: http://eprints.soton.ac.uk/id/eprint/23906
ISSN: 0001-1452
PURE UUID: c124a4cd-a2cc-444a-aad5-b38bde3ced67
ORCID for A. Sobester: ORCID iD orcid.org/0000-0002-8997-4375
ORCID for A.J. Keane: ORCID iD orcid.org/0000-0001-7993-1569

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Date deposited: 16 Mar 2006
Last modified: 16 Mar 2024 03:26

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