Feature patterns in recognizing non-interacting and interacting primitive, circular and slanting features using a neural network

A. H. Zulkifli, S. Meeran

Research output: Contribution to journalArticle

24 Citations (Scopus)

Abstract

Many varied techniques have long been suggested for the recognition of features from solid modellers, and the systems which have incorporated these techniques have achieved a moderate success. However the problem of recognition of the wide variety of features, e.g. interacting and non-interacting primitive, circular and slanting features, that any real life component may have, requires complex systems which are inflexible and hence limited in their use. Here, we present a simple and flexible system in which the features are defined as patterns of edges and vertices to deal with all the above types of features. The system starts by searching a B-rep solid model, using a cross-sectional layer method, for volumes which can be considered to represent features. Once the volumes are detected, their edges and vertices are processed and arranged into feature patterns which are used as input for a neural network to recognize the features. Simple conventions used in this work enable the creation of feature patterns for primitive, circular and slanting features. Learning, generalizing and tolerating incomplete data are some of the neural network's attributes exploited in this work to deal with interacting and non-interacting features.

Original languageEnglish
Pages (from-to)3063-3100
Number of pages38
JournalInternational Journal of Production Research
Volume37
Issue number13
DOIs
Publication statusPublished - 1 Jan 1999

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Neural networks
Large scale systems
Complex systems
Incomplete data

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

Cite this

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