A knowledge-based geometry repair system for robust parametric CAD models

Li, D., Sobester, A. and Keane, A.J. (2010) A knowledge-based geometry repair system for robust parametric CAD models. In, 48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, Orlando, Florida, 04 - 07 Jan 2010. 17pp, 1-17.


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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

Item Type: Conference or Workshop Item (Paper)
Related URLs:
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions : University Structure - Pre August 2011 > School of Engineering Sciences > Computational Engineering and Design
ePrint ID: 72013
Accepted Date and Publication Date:
4 January 2010Published
Date Deposited: 14 Jan 2010
Last Modified: 31 Mar 2016 13:03
URI: http://eprints.soton.ac.uk/id/eprint/72013

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