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Simplification of CAD Models by Automatic Recognition and Suppression of Blend Chains

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Abstract

This paper presents a CAD model simplification procedure that consists in recognizing and suppressing blend chains of certain types. The proposed method involves Euler operators (KEV, KEF, and KFMV) developed on top of an open-source geometric modeling kernel. The simplification process consists of two stages: recognition and suppression. The suppression stage ensures the geometric and topological validity of the simplification result. The proposed approach is targeted for use in batch mode, which poses strict requirements to the robustness of the suppression algorithm. The essential properties of the approach are its sustainability, predictability of the result, and extensible architecture, which allows new topological cases to be added without modifying the algorithm’s core. At the recognition stage, the algorithm constructs an attributed adjacency graph, which is then enriched with the information about types of edges, their properties, and assumed kinds of blend faces. At the suppression stage, the algorithm iterates through the adjacency graph and composes candidate blend faces into chains. For each face in a chain, local topology analysis is carried out to determine the corresponding sequence of Euler operators that are supposed to eliminate that face. The algorithm can be extended by adding descriptors of new topological cases into the processing. Upon applying the Euler operators, the affected edges are reconstructed to obtain a geometrically correct boundary representation of the model.

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Authors and Affiliations

  1. Lobachevsky State University of Nizhny Novgorod, pr. Gagarina 23, 603950, Nizhny Novgorod, Russia

    S. E. Slyadnev & V. E. Turlapov

Authors
  1. S. E. Slyadnev
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  2. V. E. Turlapov
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Correspondence to S. E. Slyadnev or V. E. Turlapov.

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Translated by Yu. Kornienko

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Slyadnev, S.E., Turlapov, V.E. Simplification of CAD Models by Automatic Recognition and Suppression of Blend Chains. Program Comput Soft 46, 233–243 (2020). https://doi.org/10.1134/S0361768820030081

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  • DOI: https://doi.org/10.1134/S0361768820030081

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