Victoria Miles victoria.s.miles@durham.ac.uk
PGR Student Doctor of Philosophy
Approaching STEP file analysis as a language processing task: A robust and scale-invariant solution for machining feature recognition
Miles, Victoria; Giani, Stefano; Vogt, Oliver
Authors
Dr Stefano Giani stefano.giani@durham.ac.uk
Associate Professor
Dr Oliver Vogt oliver.vogt@durham.ac.uk
Assistant Professor
Abstract
Machining feature recognition is a key task in the intelligent analysis of 3D CAD models as it represents a bridge between a part design and the manufacturing processes required for manufacture and can, therefore, increase automation in the manufacturing process. As 3D model files do not naturally conform to the fixed size necessary as the input to most varieties of neural network, most existing solutions for machining feature recognition rely on either transforming CAD models into a fixed shape representation, accepting some loss of information in the process, or employ rigid rules-based feature extraction techniques prior to applying any learning-based algorithm, resulting in solutions which may display high performance for specific applications but which lack in the flexibility provided by a purely learning-based approach. In this paper, we present a novel machining feature recognition model, which is capable of interpreting the data present in a STEP (standard for the exchange of product data) file using purely learning-based algorithms, with no need for human input. Our model builds on the basic framework for feature extraction from STEP file data proposed in Miles et al. (2022), with the design of a decoder network capable of using extracted features to perform the complex task of machining feature recognition. Model performance is evaluated based on accuracy at the task of identifying 24 classes of machining feature in CAD models containing between two and ten intersecting features. Results demonstrate that our solution achieves comparable performance with existing solutions when given data similar to that used during training and significantly increased robustness when compared to existing solutions when presented with CAD models which vary from those seen during training and contain small features.
Citation
Miles, V., Giani, S., & Vogt, O. (2023). Approaching STEP file analysis as a language processing task: A robust and scale-invariant solution for machining feature recognition. Journal of Computational and Applied Mathematics, 427, Article 115166. https://doi.org/10.1016/j.cam.2023.115166
Journal Article Type | Article |
---|---|
Acceptance Date | Feb 17, 2023 |
Online Publication Date | Feb 27, 2023 |
Publication Date | Aug 1, 2023 |
Deposit Date | Mar 8, 2023 |
Publicly Available Date | Mar 8, 2023 |
Journal | Journal of Computational and Applied Mathematics |
Print ISSN | 0377-0427 |
Electronic ISSN | 1879-1778 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 427 |
Article Number | 115166 |
DOI | https://doi.org/10.1016/j.cam.2023.115166 |
Public URL | https://durham-repository.worktribe.com/output/1177426 |
Files
Published Journal Article
(2.1 Mb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Copyright Statement
© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/
licenses/by/4.0/).
You might also like
Recursive autoencoder network for prediction of CAD model parameters from STEP files
(2024)
Presentation / Conference Contribution
Recursive Encoder Network for the Automatic Analysis of STEP Files
(2022)
Journal Article
Enhancing lecture capture with deep learning
(2024)
Journal Article
UKACM Proceedings 2024
(2024)
Presentation / Conference Contribution
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search