Secondary features, such as fillets, rounds, chamfers and grooves, are simple transitions between primary features, generally introduced in order to remove the sharp edges created by the intersection of primary features. Being able to distinguish secondary from primary features is important in various application contexts, such as reverse engineering, automatic geometric inspection of real scanned objects, and for preparation of models for FEM analysis and CNC tool-path generation. The process for the recognition of secondary features from high-density tessellated models of real work-pieces is intrinsically complex for several reasons. This explains why, currently, there are no methodologies able to recognize automatically secondary features and the investigation on secondary features is mostly focused on B-Rep models. In a previous paper, the authors proposed a method for secondary features recognition from discrete geometric models synthetically generated. Here the methodology is extended to discrete geometric models experimentally acquired, for which the recognition is a very complex process, due to the object discretization, to its non-ideal geometry and to measurement errors.

Automatic Segmentation of Constant Radius Secondary Features from Real Objects

Di Angelo L.
;
Di Stefano P.;Morabito A. E.
2020

Abstract

Secondary features, such as fillets, rounds, chamfers and grooves, are simple transitions between primary features, generally introduced in order to remove the sharp edges created by the intersection of primary features. Being able to distinguish secondary from primary features is important in various application contexts, such as reverse engineering, automatic geometric inspection of real scanned objects, and for preparation of models for FEM analysis and CNC tool-path generation. The process for the recognition of secondary features from high-density tessellated models of real work-pieces is intrinsically complex for several reasons. This explains why, currently, there are no methodologies able to recognize automatically secondary features and the investigation on secondary features is mostly focused on B-Rep models. In a previous paper, the authors proposed a method for secondary features recognition from discrete geometric models synthetically generated. Here the methodology is extended to discrete geometric models experimentally acquired, for which the recognition is a very complex process, due to the object discretization, to its non-ideal geometry and to measurement errors.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/144968
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