A multi-objective optimization of energy absorption properties of thin-walled circular tube with combined bar extrusion under quasi-static axial loading: Experiments and numerical simulation

管(容器) 参数统计 有限元法 材料科学 结构工程 耐撞性 流离失所(心理学) 巴(单位) 响应面法 挤压 能量(信号处理) 机械 工程类 复合材料 计算机科学 数学 统计 机器学习 物理 气象学 心理治疗师 心理学
作者
Ali Ghasemi,Mahmoud Shakeri,Seyed Ali Sadough Vanini
出处
期刊:International Journal of Mechanical Sciences [Elsevier]
卷期号:180: 105691-105691 被引量:25
标识
DOI:10.1016/j.ijmecsci.2020.105691
摘要

Thin-walled tubes are one of the most popular impact absorbers. Recently, some methods have been proposed to improve crush performance parameters of the thin-walled tube such as filling by foam, modifying geometry etc. Each method has its advantages and disadvantages. It is still a challenge for crashworthy designers to design an efficient energy-absorbing system which has all the necessary requirements. Therefore, the present paper aims to introduce a new design technique which is a combination of bar extrusion and thin-walled circular tube. The crushing behavior of the new proposed energy absorber was studied both experimentally and numerically. Finite element models were developed to estimate the force-displacement curve and the folding shapes of the tube. The numerically predicted load-displacement curve and the calculated crush performance parameters were verified using experimental measurements. Moreover, a parametric study was performed to investigate the effects of different parameters on the proposed energy absorber response. According to the results, the percentage increases in energy absorption (Ea) capacity and specific energy absorption (SEA) for the proposed energy absorber were 39.02% and 14.37% respectively compared to the thin-walled tube. In order to determine the optimized geometric parameters of the proposed energy absorber, a multi-objective optimization technique was implemented. Based on the response surface methodology (RSM), Ea and SEA increased 245.5% and 246.3% for the optimum proposed energy absorber. The predictive models for Ea and SEA were obtained by polynomial equations.
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