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An integrated convolutional neural network-based surrogate model for crashworthiness performance prediction of hot-stamped vehicle panel components
File(s)
matecconf_icmr2024_03013.pdf (1.92 MB)
Published version
Author(s)
Li, Haoran
Zhou, Haosu
Li, Nan
Type
Journal Article
Abstract
During the structural design of vehicle components, Finite Element (FE) modelling has been extensively used for simulations of physical experiments. A typical design optimisation task requires iterative simulations to identify the optimum design, where FE simulations can be too time-consuming. Surrogate models have been developed to approximate complex simulations, which can reduce computational time and improve the efficiency of the design cycle. This paper presents a novel application of convolutional neural network (CNN) on rapid predictions of crashworthiness performance of vehicle panel components considering manufacturability. The dataset for training the model was generated based on the FE results of hot-stamped ultra-high strength steel (UHSS) B-pillar components. The formed components were analysed with a simplified lateral crash test to evaluate the deformation under impact. The trained model can instantly predict the deformation of the designed component with high accuracy compared to the FE results. Due to its high computational efficiency and precision, the surrogate model enables faster and more extensive design evaluations.
Editor(s)
Qin, Y
Zhou, X
Yang, S
Zhao, J
Chen, B
Al-Ahmad, M
Date Issued
2024-08-27
Date Acceptance
2024-08-01
Citation
MATEC Web of Conferences, 2024, 401
URI
URL
DOI
ISSN
2261-236X
Publisher
EDP Sciences
Journal / Book Title
MATEC Web of Conferences
Volume
401
Copyright Statement
© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative
Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
http://dx.doi.org/10.1051/matecconf/202440103013
Publication Status
Published
Article Number
03013
Date Publish Online
2024-08-27