A Novel Shape Retrieval Method for 3D Mechanical Components Based on Object Projection, Pre-Trained Deep Learning Models and Autoencoder
Sebastian Bickel, Benjamin Schleich, Sandro Wartzack
Abstract
The reuse of existing design models offers great potential in saving resources and generating an efficient workflow. In order to fully benefit from these advantages, it is necessary to develop methods that are able to retrieve mechanical engineering geometry from a query input. This paper aims to address this problem by presenting a method that focuses on the needs of product development to retrieve similar components by comparing the geometrical similarity of existing parts. Therefore, a method is described, which first converts surface meshes into point clouds, rotates them, and then transforms the results into matrices. These are subsequently passed to a pre-trained Deep Learning network to extract the feature vector. A similarity between different geometries is calculated and evaluated based on this vector. The procedure employs a new type of part alignment, especially developed for mechanical engineering geometries. The method is presented in detail and several parameters affecting the accuracy of the retrieval are discussed. This is followed by a critical comparison with other shape retrieval approaches through a mechanical engineering benchmark data set.