A new method for secondary features segmentation, performed on high-density tessellated geometric models, is proposed. Four types of secondary features are considered: fillets, rounds and grooves. Sharp edges are also recognised. The method is based on an algorithm that analyses the principal curvatures. The nodes, potentially attributable to a fillet of given geometry, are those with a certain value for the maximum principal curvature. Since the deterministic application of this simple working principle shows several problems, due to the uncertainties in the curvature estimation, a fuzzy approach is proposed. In order to segment the nodes of a tessellated model belonging to secondary features of a given radius, an appropriate set of membership functions is defined and evaluated based on some parameters, which affect the quality of the curvature estimation. A region-growing algorithm connects the nodes pertaining to a same secondary feature so that, for a given radius, one or more secondary features may be recognized. The method is applied and verified in some test cases.

Secondary features segmentation from high-density tessellated surfaces

Di Angelo, L.;Di Stefano, P.;
2018-01-01

Abstract

A new method for secondary features segmentation, performed on high-density tessellated geometric models, is proposed. Four types of secondary features are considered: fillets, rounds and grooves. Sharp edges are also recognised. The method is based on an algorithm that analyses the principal curvatures. The nodes, potentially attributable to a fillet of given geometry, are those with a certain value for the maximum principal curvature. Since the deterministic application of this simple working principle shows several problems, due to the uncertainties in the curvature estimation, a fuzzy approach is proposed. In order to segment the nodes of a tessellated model belonging to secondary features of a given radius, an appropriate set of membership functions is defined and evaluated based on some parameters, which affect the quality of the curvature estimation. A region-growing algorithm connects the nodes pertaining to a same secondary feature so that, for a given radius, one or more secondary features may be recognized. The method is applied and verified in some test cases.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/126669
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