Recognizing 21/2D shape features using a neural network and heuristics

Nezis, K; Vosniakos, G

dc.contributor.author	Nezis, K	en
dc.contributor.author	Vosniakos, G	en
dc.date.accessioned	2014-03-01T01:13:18Z
dc.date.available	2014-03-01T01:13:18Z
dc.date.issued	1997	en
dc.identifier.issn	0010-4485	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/12413
dc.subject	Boundary models	en
dc.subject	Connectedness	en
dc.subject	Feature recognition	en
dc.subject	Heuristic algorithms	en
dc.subject	Neural nets	en
dc.subject.classification	Computer Science, Software Engineering	en
dc.subject.other	FEATURE-RECOGNITION	en
dc.subject.other	SYSTEM	en
dc.subject.other	CAD	en
dc.title	Recognizing 21/2D shape features using a neural network and heuristics	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S0010-4485(97)00003-1	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S0010-4485(97)00003-1	en
heal.language	English	en
heal.publicationDate	1997	en
heal.abstract	This work presents a Feature Recognition system developed using a previously trained Artificial Neural Network. The part description is taken from a B-rep solid modeller's data base. This description refers only to topological information about the faces in the part in the form of an Attributed Adjacency Graph. A set of heuristics is used for breaking down this compound feature graph into subgraphs, that correspond to simple features. Special representation patterns are then constructed for each of these subgraphs. These patterns are presented to a Neural Network which classifies them into feature classes: pockets, slots, passages, protrusions, steps, blind slots, corner pockets, and holes. The scope of instances/variations of these features that can be recognised is very wide. A commercially available neural network modelling tool was used for training. The user interface to the neural network recogniser has been written in Pascal. The program can handle parts with up to 200 planar or curved faces. The performance of the recogniser in terms of speed is far better than that of any other rule-based system due to the Neural Network approach employed. The basic limitation is that of the heuristics used to break down compound features into simple ones which are fed to the ANN, but this is still a step ahead compared to other approaches. (C) 1997 Elsevier Science Ltd.	en
heal.publisher	ELSEVIER SCI LTD	en
heal.journalName	CAD Computer Aided Design	en
dc.identifier.doi	10.1016/S0010-4485(97)00003-1	en
dc.identifier.isi	ISI:A1997XA33500005	en
dc.identifier.volume	29	en
dc.identifier.issue	7	en
dc.identifier.spage	523	en
dc.identifier.epage	539	en