基于设计草图的零配件检索算法

Retrieval algorithm for parts and accessories based on design sketch

在机械制造智能化进程中不可避免地产生了海量零配件模型信息,给数据的高效检索带来了巨大的挑战。考虑到设计草图具备用户友好且轻量级的特性,方法通过构造深度跨域表征模型进行基于设计草图的机械零配件模型检索。针对草图和三维模型的跨模态信息关联问题,提出特征联合学习方法,旨在控制检索对象类内及类间差异的过程中,使特征描述符习得单一域特征的同时融合跨域信息,建立跨模态数据在共嵌空间下的一致性关联表征。最后,利用哈希编码构建索引表实现海量数据的快速检索。在零部件数据上的实验结果表明,所提出的基于设计草图的零配件检索方法在同期方法中既能实现最准确的检索结果,也具备较高的检索效率。方法在提升跨模态零配件信息检索准确性的同时提高了数据管理效率,从而间接提升了产品设计的效率和便捷性,相关系统已经在部分企业落地应用且获得良好反馈。

In the intelligent transformation of the mechanical manufacturing, a large amount of spare parts models is inevitably generated, which brings huge challenges to the efficient retrieval of data.Considering the user-friendly and lightweight characteristics of design sketches, this paper conducted model retrieval of mechanical parts based on design sketches by constructing a deep cross-domain representation model.Aiming at the problem of cross-modal information association between sketches and 3D models, this paper proposed a feature joint learning method, which controlled the differences within and between classes of retrieved models.By this method, feature descriptors could acquire single-domain features as well as fuse the cross-domain information, and thus, the method established a consistent association representation of cross-modal data in a co-embedding space.Finally, it constructed the index table by using hash coding to realize fast retrieval of massive data.The experimental results on the parts data show that the proposed retrieval method based on the design sketch can not only achieve the most accurate retrieval results, but also have higher retrieval efficiency among the comparison methods.The method not only improves the accuracy of cross-modal spare parts information retrieval, but also improves the efficiency of data management, thereby indirectly improving the efficiency and convenience of product design.The related systems have been applied in some enterprises and have received good feedback.