Identification of crashworthiness indicators of column energy absorbers
with triggers in the form of cylindrical embossing on the lateral edges
using artificial neural networks
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Online publication date: 2022-11-03
Publication date: 2022-11-03
Eksploatacja i Niezawodność – Maintenance and Reliability 2022;24(4):805-821
HIGHLIGHTS
- The possibility of using artificial neural networks to identify the most advantageous variants of column energy is shown
- The considered design variants differ in geometric parameters and the position of the trigger
- The research was carried out with the use of FEM, and the models were validated by the experiment
- It has been shown that the use of neural networks to predict the properties of the energy absorber is possible with a slight error in relation to the timeconsuming multi-variant FEM analyzes.
KEYWORDS
ABSTRACT
The paper presents the possibility of neural network application in order to identify the
most advantageous design variants of column energy absorbers in terms of the achieved energy absorption indicators. Design variants of the column energy absorber made of standard
thin-walled square aluminium profile with triggers in the form of four identical cylindrical
embossments on the lateral edges were considered. These variants differ in the diameter
of the trigger, its depth and position. The geometrical parameters of the trigger are crucial
for the energy absorption performance of the energy absorber. The following indicators are
studied: PCF (Peak Crushing Force), MCF (Mean Crushing Force), CLE (Crash Load Efficiency), SE (Stroke Efficiency) and TE (Total Efficiency). On the basis of numerical studies
validated by experimentation, a neural network has been created with the aim of predicting
the above-mentioned indices with an acceptable error for an energy absorber with the trigger
of specified geometrical parameters and position. The paper demonstrates that the use of an
effective multilayer perceptron can successfully speed up the design process, saving time on
multivariate time-consuming analyses.
CITATIONS (4):
1.
Staggered compensation of a multi-tube load curve with height difference and variable induced ring distribution
Zhejun Feng, Suchao Xie, Shichen Yang, Kunkun Jing, Hao Wang, Hui Zhou
Thin-Walled Structures
2.
The coordinated design and optimisation of combined energy-absorbing structure with multi-level thin-walled tapered square tubes
Ping Xu, Jiaxing He, Jie Xing, Shuguang Yao, Qi Huang, Xin Zheng
International Journal of Crashworthiness
3.
Long short-term memory based modeling of heat treatment and trigger mechanism effect on thin-walled aluminum 6063 T5 for crashworthiness
Moises Jimenez-Martinez, Jorge Jiménez Armendáriz, Isaac Chairez, Mariel Alfaro-Ponce
International Journal of Sustainable Engineering
4.
Application of neural networks specific forms for estimation of crushing signal parameters of multilevel structural absorbers implemented in passive safety research
Jakub Gajewski, Michał Rogala, David Vališ
Measurement