MeshPointNet: 3D Surface Classification Using Graph Neural Networks and Conformal Predictions on Mesh-Based Representations
Name
md_146_5_051712.pdf
Description
Published version
Size
1.53 MB
Format
Adobe PDF
Checksum (MD5)
99bad0449fcf8b405284a160fcafbda8
Author(s)
Heyrani Nobari, Amin
Rey, Justin
Kodali, Suhas
Jones, Matthew
Ahmed, Faez
Date Issued
March 18, 2024
Journal
Journal of Mechanical Design
Publisher
ASME International
Citation
Heyrani Nobari, A., Rey, J., Kodali, S., Jones, M., and Ahmed, F. (March 18, 2024). "MeshPointNet: 3D Surface Classification Using Graph Neural Networks and Conformal Predictions on Mesh-Based Representations." ASME. J. Mech. Des. May 2024; 146(5): 051712.
Version
Final published version
Abstract
In many design automation applications, accurate segmentation and classification of 3D surfaces and extraction of geometric insight from 3D models can be pivotal. This paper primarily introduces a machine learning-based scheme that leverages graph neural networks for handling 3D geometries, specifically for surface classification. Our model demonstrates superior performance against two state-of-the-art models, PointNet + + and PointMLP, in terms of surface classification accuracy, beating both models. Central to our contribution is the novel incorporation of conformal predictions, a method that offers robust uncertainty quantification and handling with marginal statistical guarantees. Unlike traditional approaches, conformal predictions enable our model to ensure precision, especially in challenging scenarios where mistakes can be highly costly. This robustness proves invaluable in design applications, and as a case in point, we showcase its utility in automating the computational fluid dynamics meshing process for aircraft models based on expert guidance. Our results reveal that our automatically generated mesh, guided by the proposed rules by experts enabled through the segmentation model, is not only efficient but matches the quality of expert-generated meshes, leading to accurate simulations.
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1115/1.4064673
