A knowledge-based geometry repair system for robust parametric CAD models
A knowledge-based geometry repair system for robust parametric CAD models
In modern multi-objective design optimization (MDO) an effective geometry engine is
becoming an essential tool and its performance has a significant impact on the entire MDO
process. Building a parametric geometry requires difficult compromises between the conflicting
goals of robustness and flexibility. This article presents a method of improving the
robustness of parametric geometry models by capturing and modeling engineering knowledge
with a support vector regression surrogate, and deploying it automatically for the
search of a more robust design alternative while trying to maintain the original design
intent. Design engineers are given the opportunity to choose from a range of optimized
designs that balance the ‘health’ of the repaired geometry and the original design intent.
The prototype system is tested on a 2D intake design repair example and shows the potential
to reduce the reliance on human design experts in the conceptual design phase and
improve the stability of the optimization cycle. It also helps speed up the design process
by reducing the time and computational power that could be wasted on flawed geometries
or frequent human interventions
1-17
Li, D.
eaa10cac-8eee-4069-a3e4-649088cf9a16
Sobester, A.
096857b0-cad6-45ae-9ae6-e66b8cc5d81b
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
4 January 2010
Li, D.
eaa10cac-8eee-4069-a3e4-649088cf9a16
Sobester, A.
096857b0-cad6-45ae-9ae6-e66b8cc5d81b
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
Li, D., Sobester, A. and Keane, A.J.
(2010)
A knowledge-based geometry repair system for robust parametric CAD models.
48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, Orlando, Florida.
04 - 07 Jan 2010.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
In modern multi-objective design optimization (MDO) an effective geometry engine is
becoming an essential tool and its performance has a significant impact on the entire MDO
process. Building a parametric geometry requires difficult compromises between the conflicting
goals of robustness and flexibility. This article presents a method of improving the
robustness of parametric geometry models by capturing and modeling engineering knowledge
with a support vector regression surrogate, and deploying it automatically for the
search of a more robust design alternative while trying to maintain the original design
intent. Design engineers are given the opportunity to choose from a range of optimized
designs that balance the ‘health’ of the repaired geometry and the original design intent.
The prototype system is tested on a 2D intake design repair example and shows the potential
to reduce the reliance on human design experts in the conceptual design phase and
improve the stability of the optimization cycle. It also helps speed up the design process
by reducing the time and computational power that could be wasted on flawed geometries
or frequent human interventions
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Li_10.pdf
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Published date: 4 January 2010
Venue - Dates:
48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, Orlando, Florida, 2010-01-04 - 2010-01-07
Identifiers
Local EPrints ID: 72013
URI: http://eprints.soton.ac.uk/id/eprint/72013
PURE UUID: 5527cbca-3736-427b-93c3-708bab35178c
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Date deposited: 14 Jan 2010
Last modified: 14 Mar 2024 02:47
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Author:
D. Li
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