基于无监督学习的二维工程CAD模型端到端检索算法

End-to-End Retrieval Algorithm of Two-dimensional Engineering CAD Model Based on Unsupervised Learning

针对企业产品制造过程中海量的计算机辅助设计(Computer Aided Design,CAD)模型的高效检索难题,文中研究了一种基于二维CAD模型内容特征的检索算法,构建了一个可用于CAD的DXF格式源文件模型库的检索系统原型。首先,通过对二维CAD模型的DXF文件结构进行分析,来研究模型中的图元规律并进行形状重构;其次,依据图元特点,提出了基于统计直方图、二维形状分布和傅里叶变换共3类内容特征的提取方法;最后,设计了基于无监督学习的多特征融合框架及相似度计算方法,从而提取出了模型的融合特征描述子并实现了二维CAD模型检索。实验结果表明,文中提取的融合特征相较于单一特征包含了更加丰富的内容特征且具有高效的鉴别力。该系统可以直接应用于产品个性化定制、设计重用等方面,有助于企业进一步提升智能制造能力。

Aiming at the problem of efficient retrieval of massive computer aided design(CAD)models in enterprise product manufacturing process,this paper studied a retrieval algorithm based on the content feature of two-dimensionalCAD models,and constructed a retrieval system prototype which can be used for DXF format CAD source file model base.Firstly,through the analysis of the DXF file structure of the two-dimensional CAD model,the rule of the primitive in the model is studied and the shape reconstruction is carried out.Secondly,according to the features of primitive,three kinds of content feature extraction methods are proposed,which are based on statistical histogram,two-dimensional shape distribution and Fourier transform.Finally,a multi-feature fusion framework based on unsupervised learning and similarity calculation method are designed to extract the fusion feature descriptor of the model and realize the retrieval of two-dimensional CAD model.Experiments show that the fusion features extracted in this paper contain more abundant content features and are more effective than single features.The system can be directly used in product customization,product design reuse and other aspects to help enterprises further improve the ability of intelligent manufacturing.