Research on Boundary Extraction of STL Models based on Genetic Algorithm

Jing Bin Hao; Zhong Bin Wang; Hai Feng Yang; Zhong Kai Li

doi:10.4028/www.scientific.net/AMR.566.78

Paper Titles

Theoretical and Experimental Study on the Secondary Piezoelectric Effect of a Piezoelectric Cantilever Energy Harvester
p.57

Welded Joint Fatigue Cumulative Damage MMM Characteristic
p.61

Surface Crack Growth from Small Indentations in a Silicon Nitride Square Bar under Cyclic Reversed Torsion
p.65

Computational Issues in Rate-Dependent Plasticity Models
p.70

Research on Boundary Extraction of STL Models based on Genetic Algorithm
p.78

A Study of Transverse Shrinkage and Vertical Displacement in Induction Triangle Heating
p.82

A Knowledge Based CAD/CAE Integrated Multidisciplinary Design System
p.88

Determination and Analysis of the Order of Stress in a Homogeneous Field
p.92

Multivariate Time Series Prediction Based on a Simple RBF Network
p.97

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Research on Boundary Extraction of STL Models based on Genetic Algorithm

Abstract:

To efficiently decompose a large complex STL model, an improved boundary extraction method is proposed based on genetic algorithm. Three curvature parameters (dihedral angle, perimeter ration and convexity) were used to estimate the surface curvature information. Genetic Algorithm (GA) is used to determinate the threshold of feature edge. The discrete feature edges are grouped and filtered using the best-fit plane (BFP), which is calculated by Least Square Method (LSM). Several experimental results demonstrate that the amount of feature edges is about half of the preset threshold method, and useful feature edges were reserved. The extracted feature boundaries can be directly used to decompose large complex models.