D. K. , Sehgal and K. , Pathak (2010) Gradientless shape optimization using artificial neural networks. Struct Multidisc Optim,, 41. pp. 699-709.

Full text not available from this repository. (Request a copy)

Abstract

In this paper a new zero order method of structural shape optimization, in which material shrinks or grows perpendicular to the design boundary, has been proposed in order to satisfy fully stressed design criteria. To avoid mesh distortion that results in undesirable shape, design element concept and for nodal movement and convergence checking, fuzzy set theory have been used. To accelerate the convergence, artificial neural networks are employed. The proposed approach, named as GSN technique, has been incorporated in a FORTRAN software GSOANN. Using this software shape optimization of four structures are carried out. It is demonstrated that proposed technique overcomes most of the shortcomings of mundane zero order methods.

Item Type:	Article
Subjects:	Lightweight Materials
Divisions:	UNSPECIFIED
Depositing User:	Mr. B.K. Prasad
Date Deposited:	19 Aug 2013 11:11
Last Modified:	19 Aug 2013 11:11
URI:	http://ampri.csircentral.net/id/eprint/406

Actions (login required)

View Item