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Many types of heterogeneous CAD models created by previous product development activities are stored in enterprise databases. Searching reusable CAD models for engineer design requirements is still very difficult. In this paper, we propose an ontology-based semantic retrieval approach for heterogeneous 3D CAD models by using semantic web theory and information retrieval theory. By exploiting layered feature ontology and ontology mapping, a uniform description for heterogeneous CAD models is generated as semantic descriptor. Meanwhile, to improve the representative ability, several heuristic rules are constructed with the aid of SWRL language and ontology reasoning is acted on the semantic descriptors, which makes our retrieval system achieve better performance. Furthermore, multi-mode similarity measurement is carried out to meet engineers’ diversified requirements. We demonstrate the feasibility and effectiveness of our approach through experiments on heterogeneous 3D CAD model library. [ABSTRACT FROM AUTHOR]

Performing 3-dimensional computer-aided design (3D-CAD) model classification, retrieval, and reuse is of vital importance in industrial manufacturing, as it considerably shortens the engineering development cycle and reduces development costs. Although existing 3D model classification and retrieval methods achieve satisfactory performance when operating on meshes or point clouds, they cannot be applied directly to 3D-CAD models, which are generally represented by the boundary representation (B-rep). To address this issue, and to fully exploit the topology of B-rep, a graph structure descriptor called B-rep graph is proposed to pre-process B-rep data, and to extract the precise topological and geometric features from 3D-CAD models. Meanwhile, a novel efficient neural network called FuS-GCN, based on graph convolutional networks (GCNs), is designed to handle this graph data. To better extract the graph features and to improve the effect of pooling, the self-attention mechanism and feature fusion are incorporated into the pooling layer, yielding the proposed fusion self-attention graph pooling (FuSPool) algorithm. Finally, we demonstrate the effectiveness of FuS-GCN on 3D-CAD model data, while outperforming alternative 3D shape descriptors such as point clouds, voxels, and meshes. [ABSTRACT FROM AUTHOR]

Design pattern is widely used in the software engineering field, which enables designers to reuse existing mature designs from a high level perspective. Inspired by this idea, a novel approach is proposed to extract design patterns in the CAD field. First, the characteristics for a good design pattern are analyzed and the model for representing design patterns is elaborated. Then, given a set of 3D feature-based CAD models, the corresponding extraction approach is proposed, which includes three important phases: (1) extracting reusable regions with high cohesion, low coupling and moderate complexity so as to form a relative integrated function; (2) constructing candidate design patterns by clustering reusable regions using a graph-oriented agglomerative hierarchical clustering algorithm; (3) determining the final design patterns by choosing those candidate design patterns with high frequency and sufficient information. Finally, a design pattern extraction prototype system is developed, and the experimental results are presented to demonstrate the effectiveness of the approach. [ABSTRACT FROM AUTHOR]