Development Approach of Automated Recognition for Isolated and Intersection (Complex) Manufacturing Features for Prismatic Mechanical Parts

The intersection between part features caused ambiguousness in featurerepresentation because the topology in this case will be change. To overcome this no unique and ambiguousness by combine the topology and characteristics to extract and recognize the intersection features. This paper introduces ne...

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Bibliographic Details
Main Author: Hussein. S. Ketan
Format: Article
Language:English
Published: Unviversity of Technology- Iraq 2010-10-01
Series:Engineering and Technology Journal
Subjects:
Online Access:https://etj.uotechnology.edu.iq/article_41087_0cf914c70358f0a658f682212932211c.pdf
Description
Summary:The intersection between part features caused ambiguousness in featurerepresentation because the topology in this case will be change. To overcome this no unique and ambiguousness by combine the topology and characteristics to extract and recognize the intersection features. This paper introduces new general algorithm to: (1) construct developed Attributes Adjacency Matrix (DAAM) and (2) analysis the DAAM to extract and recognize the isolated and intersection (complex) manufacturing features. This algorithm consists of two stages, preprocessorstage which performs extraction of information from Design ExchangeFormat (DXF) file, and post-processor stage which recognizes part’s features depending on the relations included in DAAM. The parts are represented in AutoCAD using Constructive Solid Geometry (CSG) technique and the algorithm built in visual basic. Several parts tested and succeed to recognize several types of intersection (complex) manufacturing features. The main contributions of this research include building the adjacent relations between faces of the part bygeometrical characteristics in form of matrix, and use clustering operation to extract the exact faces of feature and use different classes of geometrical characteristics to assisting to recognize interacting depression prismatic features.
ISSN:1681-6900
2412-0758