A Flexible Approach for Retrieving Geometrically Similar Finite Element Models Using Point Cloud Autoencoders

Sonja Schlenz, Sonja Schlenz, Simon Mößner, Carl Ek, Fabian Duddeck

2023

Abstract

For the development of complex products like vehicle components, knowledge about previous solutions is a key factor. Complete solutions or parts thereof can often be reused if a similar previous model can be identified. To gain independence from the individual experience of single engineers about previous models and a tedious search process, identifying and retrieving the most similar models from large databases offers great potential. Accordingly, this paper introduces a method to achieve this kind of shape retrieval based on engineering data. 3D geometries are represented as point clouds and reduced to one single vector with an autoencoder to identify similarities in the latent space. The method can be used in a flexible way to identify global or local similarities as well as to emphasize different parts of the structure in the similarity search. The method is evaluated on an industrial dataset containing real-world engineering data.

Paper Citation

in Harvard Style

Schlenz S., Mößner S., Ek C. and Duddeck F. (2023). A Flexible Approach for Retrieving Geometrically Similar Finite Element Models Using Point Cloud Autoencoders. In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN 978-989-758-671-2, SciTePress, pages 188-195. DOI: 10.5220/0012158700003598

in Bibtex Style

@conference{kdir23,
author={Sonja Schlenz and Simon Mößner and Carl Ek and Fabian Duddeck},
title={A Flexible Approach for Retrieving Geometrically Similar Finite Element Models Using Point Cloud Autoencoders},
booktitle={Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2023},
pages={188-195},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012158700003598},
isbn={978-989-758-671-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - A Flexible Approach for Retrieving Geometrically Similar Finite Element Models Using Point Cloud Autoencoders
SN - 978-989-758-671-2
AU - Schlenz S.
AU - Mößner S.
AU - Ek C.
AU - Duddeck F.
PY - 2023
SP - 188
EP - 195
DO - 10.5220/0012158700003598
PB - SciTePress