An Intelligent Identification Approach of Assembly Interface for CAD Models  

作　　者：Yigang Wang Hong Li Wanbin Pan Weijuan Cao Jie Miao Xiaofei Ai Enya Shen 

机构地区：[1]School of Media and Design,Hangzhou Dianzi University,Hangzhou,310001,China [2]School of Software,Tsinghua University,Beijing,100084,China

出　　处：《Computer Modeling in Engineering & Sciences》2023年第10期859-878,共20页工程与科学中的计算机建模（英文）

基　　金：supported by the National Natural Science Foundation of China[61702147];the Zhejiang Provincial Science and Technology Program in China[2021C03137].

摘　　要：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 abovementioned 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.

关 键 词：Assembly interface identification kinematic semantics reconstruction attributed adjacency graph graph neural network 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]