Prediction and Optimization Method of Energy Absorption Characteristics of Thin-walled Metal Circular Pipe Filled with Aluminum Foam

doi:10.12382/bgxb.2023.0366

Abstract

Abstract:

In order to improve the energy absorption characteristics of aluminum foam-filled thin-walled metal circular tube and realize high-efficiency structural design, an approximate model of the energy absorption characteristics of aluminum foam-filled aluminum tube under axial impact is established based on the uniform Latin hypercube design method and radial basis neural network, and it is embedded into the genetic algorithm to realize the structural optimization of components. The results show that the goodness of fit of the approximate model based on the radial basis neural network is greater than 0.99, the root mean square error is less than 0.08, and the calculation time of the approximate model is only 0.56% of the numerical calculation. The approximate model can greatly improve the calculation efficiency while ensuring higher accuracy. Through the optimization of the structure of aluminum foam-filled aluminum tube, it is found that the average compression force of aluminum foam-filled aluminum tube can be maximized by increasing the radius and wall thickness of the circular tube and reducing the height, and on the contrary, its peak compression force can be minimized. The multi-objective optimization based on the genetic algorithm significantly improves the energy absorption characteristics of aluminum foam-filled aluminum tube. The research results can provide a reference for the rapid design and optimization of aluminum foam-filled thin-walled metal circular tube.

Key words: aluminum foam, energy absorption characteristics, radial basis function neural network, approximate model, structure optimization

CLC Number:

U270.1

YAN Song, JIANG Yi, DENG Yueguang, WEN Xianghua. Prediction and Optimization Method of Energy Absorption Characteristics of Thin-walled Metal Circular Pipe Filled with Aluminum Foam[J]. Acta Armamentarii, 2024, 45(6): 1954-1964.

Figures/Tables 21

Fig.1 Construction of approximate model and prediction framework

Fig.2 Finite element model of aluminum foam-filled aluminum tube

Table 1 Material parameters of each component

部件	密度/(kg·m^-3)	弹性模量/MPa	泊松比
铝管	2700	68200	0.33
泡沫铝	330	80	0.01
上、下压块	7800	210000	0.30

Fig.3 Stress-strain curve of aluminum alloy[27]

Fig.4 Stress-strain curve of aluminum foam[27]

Fig.5 Comparison between finite element calculation results and experimental results

Fig.6 Comparison of quasi-static compression results

Fig.7 Comparison of axial impact results

Fig.8 Framework of RBF neural network

Table 2 Value ranges of three parametersmm

输入参数	取值下限	取值上限
r	10	30
T	1	5
H	20	100

Fig.9 Design points distribution

Fig.10 Error analysis of approximate model for peak compressive force

Fig.11 Error analysis of approximate model for average compressive force

Fig.12 Error analysis of approximate model for maximum compressive distance

Table 3 Error analysis of approximate model

输出参数			E_MA	R²
F_max	0.0442	0.0061	0.1465	0.9980
F_m	0.0705	0.0011	0.2098	0.9949
d	0.0735	0.0346	0.3275	0.9945

Table 4 Calculation efficiency comparison of approximate model and finite element min

近似模型	有限元计算
0.5	90

Fig.13 Flowchart of genetic algorithm

Table 5 Single-objective optimal solutions ofaluminum foam-filled aluminum tube

目标函数	r/mm	T/mm	H/mm	F_m/kN	F_max/kN
minF_m	10	1	100	9.952	13.550
maxF_max	30	5	20	169.749	186.806

Fig.14 Comparison between multi-objective optimal solution set and finite element calculation results

Table 6 10 groups ofdata samples and corresponding finite element solutions

编号	r/mm	T/mm	H/mm	F_m/kN	F_max/kN	CFE/%
1	16.08	1.32	72.16	17.667	27.226	64.89
2	25.13	1.42	43.90	29.128	46.802	62.23
3	17.79	2.47	35.66	42.477	60.033	70.71
4	23.75	2.51	46.41	58.624	79.274	73.95
5	19.84	3.51	83.42	86.253	95.173	90.63
6	29.83	2.95	78.31	103.753	115.622	89.73
7	21.97	4.30	89.76	113.283	128.900	87.88
8	23.48	4.68	79.79	130.093	149.039	87.29
9	25.72	4.89	72.78	149.259	170.360	87.61
10	27.98	4.92	85.68	157.298	180.722	87.03

Fig.15 Comparison of compression force efficiencies before and after optimization

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