Recursive autoencoder network for prediction of CAD model parameters from STEP files

Miles, Victoria; Giani, Stefano; Vogt, Oliver; Kafieh, Raheleh

Abstract

Databases of 3D CAD (computer aided design) models are often large and lacking in meaningful organisation. Effective tools for automatically searching for, categorising and comparing CAD models, therefore, have many potential applications in improving efficiency within design processes. This paper presents a novel asymmetric autoencoder model, consisting of a recursive encoder network and fully-connected decoder network, for the reproduction of CAD models through prediction of the parameters necessary to generate a 3D part design. Inputs to the autoencoder are STEP (standard for the exchange of product data) files, an ISO standard CAD model format, compatible with all major CAD software. A complete 3D model can be accurately reproduced using a STEP file, meaning that all geometric information can be used to contribute to the final encoded vector, with no loss of small detail. In a CAD model of overall size 10 × 10 × 10 units, for 90% of models, the class of an added feature is estimated with maximum error of 0.6 units, feature size with maximum error of 0.4 units and coordinate values representing position with maximum error of 0.3 units. These results demonstrate the successful encoding of complex geometric information, beyond merely the shape of the 3D object, with potential application in the design of search engine functionality.

Citation

Miles, V., Giani, S., Vogt, O., & Kafieh, R. Recursive autoencoder network for prediction of CAD model parameters from STEP files

• Presentation Conference Type: Conference Paper (published)
• Acceptance Date: Jun 12, 2022
• Online Publication Date: Mar 20, 2024
• Publication Date: 2024
• Deposit Date: May 23, 2024
• Publicly Available Date: May 23, 2024
• Journal: Procedia Computer Science
• Print ISSN: 1877-0509
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 232
• Pages: 3239-3246
• DOI: https://doi.org/10.1016/j.procs.2024.02.139
• Public URL: https://durham-repository.worktribe.com/output/2361873