Region segmentation and shape characterisation for tessellated CAD models

Abstract

Tessellated computer aided design (CAD) models are extensively used in CAD/computer aided manufacturing applications. One challenge in manipulating tessellated CAD models is to segment the facets of a tessellated model into different groups that carry distinct geometrical properties. In this article a method is proposed that can automatically detect convex and concave regions on a tessellated CAD model and further to characterise their shapes. The method starts at clustering triangular facets based on local convexity of each facet with its neighbourhood. It classifies triangular facets on an STL file into local convex group, local concave group and mixed group, based on the relation of each facet normal with its neighbouring facets. Subsequently, shape recognition on the detected regions is conducted using Gaussian Image formation and Point Distribution distance algorithm. Examples are shown to demonstrate the capability of the algorithm in characterising basic shapes out of those segmented facet clusters.

Keywords:

• feature recognition
• STL file
• Gaussian image
• shape characterisation

Disclosure statement

No potential conflict of interest was reported by the authors.