Bayesian approach for extracting and identifying features

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

The paper introduces a new uncertainty reasoning based method for the identification and extraction of manufacturing features from solid model descriptions of objects. A major difficulty faced by previously proposed methods for feature extraction has been the interaction between features. In interacting situations, the representation for various primitive features is nonunique, making their recognition very difficult. The paper develops an approach based on generating, propagating, and combining geometric and topological evidence in a hierarchical belief network for identifying and extracting features. The methodology combines and propagates pieces of evidence to determine a set of corect virtual links to be augmented to the cavity graph representing a depression of the object so that the resulting supergraph can be partitioned to obtain the features of the object. The hierarchical belief network is constructed on the basis of the hypotheses for the potential virtual links. The pieces of evidence, which consist of topological and geometric relationships at different abstraction levels, impacts the belief network through its (amount of) support for different hypotheses. The propagation of the impact of different pieces of evidence updates the beliefs in the network in accordance with the Bayesian probabilistic rules.

Original languageEnglish (US)
Pages (from-to)435-454
Number of pages20
JournalComputer-Aided Design
Volume27
Issue number6
DOIs
StatePublished - 1995

Fingerprint

Bayesian networks
Belief Networks
Bayesian Approach
Virtual Link
Hierarchical Networks
Solid Model
Feature extraction
Identification (control systems)
Feature Extraction
Cavity
Reasoning
Manufacturing
Update
Propagation
Uncertainty
Evidence
Methodology
Graph in graph theory
Interaction
Object

Keywords

  • computer-aided process planning
  • evidential reasoning
  • feature recognition

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Industrial and Manufacturing Engineering
  • Geometry and Topology

Cite this

Bayesian approach for extracting and identifying features. / Ji, Qiang; Marefat, Michael Mahmoud.

In: Computer-Aided Design, Vol. 27, No. 6, 1995, p. 435-454.

Research output: Contribution to journalArticle

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