Automatic Detection of Repetitive Components in 3D Mechanical Engineering Models
Author(s) -
Laixiang Wen,
Jinyuan Jia,
Shuang Liang,
Jianhua Zhang
Publication year - 2013
Publication title -
advances in mechanical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.318
H-Index - 40
eISSN - 1687-8140
pISSN - 1687-8132
DOI - 10.1155/2013/607086
Subject(s) - similarity (geometry) , component (thermodynamics) , computer science , matching (statistics) , voxel , genetic algorithm , feature (linguistics) , pattern recognition (psychology) , artificial intelligence , feature extraction , algorithm , mathematics , image (mathematics) , machine learning , linguistics , statistics , physics , philosophy , thermodynamics
We present an intelligent method to automatically detect repetitive components in 3D mechanical engineering models. In our work, a new Voxel-based Shape Descriptor (VSD) is proposed for effective matching, based on which a similarity function is defined. It uses the voxels intersecting with 3D outline of mechanical components as the feature descriptor. Because each mechanical component may have different poses, the alignment before the matching is needed. For the alignment, we adopt the genetic algorithm to search for optimal solution where the maximum global similarity is the objective. Two components are the same if the maximum global similarity is over a certain threshold. Note that the voxelization of component during feature extraction and the genetic algorithm for searching maximum global similarity are entirely implemented on GPU; the efficiency is improved significantly than with CPU. Experimental results show that our method is more effective and efficient than that existing methods for repetitive components detection
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