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.

Attached files

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

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

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