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Research Papers: Design Automation

Topology Optimization of Multicell Tubes Under Out-of-Plane Crushing Using a Modified Artificial Bee Colony Algorithm

[+] Author and Article Information
Jianguang Fang

School of Aerospace, Mechanical
and Mechatronic Engineering,
The University of Sydney,
Sydney NSW 2006, Australia;
School of Civil and Environmental Engineering,
University of Technology Sydney,
Sydney NSW 2007, Australia
e-mail: fangjg87@gmail.com

Guangyong Sun

School of Aerospace, Mechanical
and Mechatronic Engineering,
The University of Sydney,
Sydney NSW 2006, Australia
e-mail: sgy800@126.com

Na Qiu

School of Automotive Studies,
Tongji University,
Shanghai 201804, China

Grant P. Steven

School of Aerospace, Mechanical
and Mechatronic Engineering,
The University of Sydney,
Sydney NSW 2006, Australia
e-mail: grant.steven@sydney.edu.au

Qing Li

School of Aerospace, Mechanical
and Mechatronic Engineering,
The University of Sydney,
Sydney NSW 2006, Australia
e-mail: qing.li@sydney.edu.au

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received February 9, 2016; final manuscript received March 10, 2017; published online May 18, 2017. Assoc. Editor: James K. Guest.

J. Mech. Des 139(7), 071403 (May 18, 2017) (16 pages) Paper No: MD-16-1115; doi: 10.1115/1.4036561 History: Received February 09, 2016; Revised March 10, 2017

Multicell tubal structures have generated increasing interest in engineering design for their excellent energy-absorbing characteristics when crushed through severe plastic deformation. To make more efficient use of the material, topology optimization was introduced to design multicell tubes under normal crushing. The design problem was formulated to maximize the energy absorption while constraining the structural mass. In this research, the presence or absence of inner walls were taken as design variables. To deal with such a highly nonlinear problem, a heuristic design methodology was proposed based on a modified artificial bee colony (ABC) algorithm, in which a constraint-driven mechanism was introduced to determine adjacent food sources for scout bees and neighborhood sources for employed and onlooker bees. The fitness function was customized according to the violation or the satisfaction of the constraints. This modified ABC algorithm was first verified by a square tube with seven design variables and then applied to four other examples with more design variables. The results demonstrated that the proposed heuristic algorithm is capable of handling the topology optimization of multicell tubes under out-of-plane crushing. They also confirmed that the optimized topological designs tend to allocate the material at the corners and around the outer walls. Moreover, the modified ABC algorithm was found to perform better than a genetic algorithm (GA) and traditional ABC in terms of best, worst, and average designs and the probability of obtaining the true optimal topological configuration.

Copyright © 2017 by ASME
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References

Figures

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Fig. 1

Finite element model of a multicell tube under out-of-plane loading

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Fig. 2

Initial configuration with seven design variables

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Fig. 3

Unconnected designs: (a) type A and (b) type B

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Fig. 4

Energy absorption versus mass fraction

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Fig. 5

Curves of fconsi: (a) the feasible and infeasible cases and (b) variation with respect to d1

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Fig. 6

Flowcharts of ABC based topology optimization for multicell tubes: (a) general procedures and (b) procedures for fitness evaluation

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Fig. 7

History of the objective function for the first example with different mass constraints

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Fig. 8

Comparison of impact force versus displacement curves for the first example: (a) w = 20% and (b) w = 40%

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Fig. 9

Design variables of the second example

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Fig. 10

History of the objective function for the second example

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Fig. 11

Comparison of impact force versus displacement curves for the second example (case III)

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Fig. 12

History of energy absorption and number of FEA

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Fig. 13

Design variables of the third example

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Fig. 14

History of the objective function for the third example

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Fig. 15

Comparison of impact force versus displacement curves for the third example (case IV)

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Fig. 16

Design variables of the fourth example

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Fig. 17

History of the objective function for the fourth example

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Fig. 18

Comparison of impact force versus displacement curves for the fourth example: (a) case I and (b) case II

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Fig. 19

Design variables of the fifth example

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Fig. 20

History of the objective function for the fifth example

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Fig. 21

Comparison of impact force versus displacement curves for the fifth example (case III)

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