Journal Article


Crashworthiness behavior assessment and multi-objective optimization of horsetail-inspired sandwich tubes based on artificial neural network

Abstract

The crashworthiness behavior of horsetail-inspired sandwich tubes was analyzed in this study. Multilayer perceptron (MLP) algorithms with the Levenberg-Marquardt training algorithm (LMA) were used to predict force-displacement curve and optimize the geometrical parameters according to minimum peak crushing force and specific energy absorption. Based on the non-dominated sorting genetic algorithm II (NSGA-II) optimization results, the specimen with four core tubes and a thickness of 1 mm, and a height of 92 mm has the optimal crashworthiness performance. Finally, the optimal specimen is fabricated and the results of the numerical and MLP methods are validated versus experimental approach.

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Authors

Faraza, Moslem Rezaei
Hosseini, Shahram
Tarafdar, Amirreza
Forghani, Mojtaba
Ahmadi, Hamed
Fellows, Neil
Liaghat, Gholamhossein

Oxford Brookes departments

School of Engineering, Computing and Mathematics

Dates

Year of publication: 2023
Date of RADAR deposit: 2024-01-12


Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License


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