Open Access

ARTICLE

An Intelligent Identification Approach of Assembly Interface for CAD Models

by Yigang Wang¹, Hong Li¹, Wanbin Pan^1,*, Weijuan Cao¹, Jie Miao¹, Xiaofei Ai¹, Enya Shen²

1 School of Media and Design, Hangzhou Dianzi University, Hangzhou, 310001, China
2 School of Software, Tsinghua University, Beijing, 100084, China

* Corresponding Author: Wanbin Pan. Email:

(This article belongs to the Special Issue: Integration of Geometric Modeling and Numerical Simulation)

Computer Modeling in Engineering & Sciences 2023, 137(1), 859-878. https://doi.org/10.32604/cmes.2023.027320

Received 26 October 2022; Accepted 12 January 2023; Issue published 23 April 2023

Abstract

Kinematic semantics is often an important content of a CAD model (it refers to a single part/solid model in this work) in many applications, but it is usually not the belonging of the model, especially for the one retrieved from a common database. Especially, the effective and automatic method to reconstruct the above information for a CAD model is still rare. To address this issue, this paper proposes a smart approach to identify each assembly interface on every CAD model since the assembly interface is the fundamental but key element of reconstructing kinematic semantics. First, as the geometry of an assembly interface is formed by one or more adjacent faces on each model, a face-attributed adjacency graph integrated with face structure fingerprint is proposed. This can describe each CAD model as well as its assembly interfaces uniformly. After that, aided by the above descriptor, an improved graph attention network is developed based on a new dual-level anti-interference filtering mechanism, which makes it have the great potential to identify all representative kinds of assembly interface faces with high accuracy that have various geometric shapes but consistent kinematic semantics. Moreover, based on the above-mentioned graph and face-adjacent relationships, each assembly interface on a model can be identified. Finally, experiments on representative CAD models are implemented to verify the effectiveness and characteristics of the proposed approach. The results show that the average assembly-interface-face-identification accuracy of the proposed approach can reach 91.75%, which is about 2%–5% higher than those of the recent-representative graph neural networks. Besides, compared with the state-of-the-art methods, our approach is more suitable to identify the assembly interfaces (with various shapes) for each individual CAD model that has typical kinematic pairs.

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Keywords

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