AI-assisted study of auxetic structures

Grednev, Sergej; Steude, Henrik Sebastian; Bronder, Stefan; Niggemann, Oliver; Jung, Anne

doi:10.14311/app.2023.42.0032

AI-assisted study of auxetic structures

Publication date

2023-10-12

Document type

Konferenzbeitrag

Author

Grednev, Sergej

Steude, Henrik Sebastian

Bronder, Stefan

Niggemann, Oliver

Jung, Anne

Organisational unit

Schutzsysteme

Informatik im Maschinenbau

DOI

10.14311/app.2023.42.0032

URI

https://openhsu.ub.hsu-hh.de/handle/10.24405/22679

Conference

18th Youth Symposium on Experimental Solid Mechanics (YSESM 2023) ; Telč, Czech Republic ; June 11–14, 2023

Publisher

Czech Technical University

Series or journal

Acta Polytechnica = CTU Proceedings

ISSN

2336-5382

Periodical volume

42

First page

32

Last page

36

Peer-reviewed

✅

Part of the university bibliography

✅

Language

English

Abstract

In this study, the viability of using machine learning models to predict stress-strain curves of auxetic structures based on geometry-describing parameters is explored. Given the computational cost and time associated with generating these curves through numerical simulations, a machine learning-based approach promises a more efficient alternative. A range of machine learning models, including Artificial Neural Networks, k-Nearest Neighbors Regression, Support Vector Regression, and XGBoost, is implemented and compared regarding the aptitude to predict stress-strain curves under quasi-static compressive loading. Training data is generated using validated finite element simulations. The performance of these models is rigorously tested on data not seen during training. The Feed-Forward Artificial Neural Network emerged as the most proficient model, achieving a Mean Absolute Percentage Error of 0.367 ± 0.230.

Description

Licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)

Version

Published version

Access right on openHSU

Metadata only access