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Efficient detailed design optimization of topology optimization concepts by using support vector machines and metamodels
Örebro universitet, Institutionen för naturvetenskap och teknik. Department of Mechanical Engineering.ORCID-id: 0000-0001-6821-5727
2019 (engelsk)Inngår i: Engineering optimization (Print), ISSN 0305-215X, E-ISSN 1029-0273Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

In this article, an approach for metamodel-based design optimization (MBDO) of topology optimization (TO) concepts is proposed by using support vector machines (SVMs) as geometric models of the concepts instead of traditional parametric computer aided design (CAD) models. In such a manner, an efficient approach for the MBDO-driven design of TO-based concepts is obtained. An implicit hypersurface representing the TO-based concept is generated by classifying the TO-solutions of zeros and ones by using the 1-norm SVM of Mangasarian. The implicit SVM-based hypersurfaces are then utilized to set up designs of experiments of nonlinear finite element analyses by morphing the TO-based concepts by using Boolean and blending operations. Finally, MBDO is performed by using an ensemble of metamodels consisting of quadratic regression, Kriging, radial basis function networks, polynomial chaos expansion and support vector regression models. The proposed MBDO framework is demonstrated by minimizing the mass of a three-dimensional design domain with a constraint on the plastic limit load. The performance of the approach is most promising.

sted, utgiver, år, opplag, sider
Taylor & Francis, 2019.
Emneord [en]
Support vector machines, topology optimization, metamodels
HSV kategori
Identifikatorer
URN: urn:nbn:se:oru:diva-75953DOI: 10.1080/0305215X.2019.1646258ISI: 000481217300001OAI: oai:DiVA.org:oru-75953DiVA, id: diva2:1347102
Tilgjengelig fra: 2019-08-30 Laget: 2019-08-30 Sist oppdatert: 2019-08-30bibliografisk kontrollert

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