Crashworthiness behavior assessment and multi-objective optimization of horsetail-inspired sandwich tubes based on artificial neural network
Moslem Rezaei Faraz, Shahram Hosseini, Amirreza Tarafdar, Mojtaba Forghani, Hamed Ahmadi, Neil Fellows, Gholamhossein Liaghat
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.