Neural Computing and Applications

, Volume 29, Issue 7, pp 405–416 | Cite as

Automated selection of optimal material for pressurized multi-layer composite tubes based on an evolutionary approach

Original Article

Abstract

Decision making on the configuration of material layers as well as thickness of each layer in composite assemblies has long been recognized as an optimization problem. Today, on the one hand, abundance of industrial alloys with different material properties and costs facilitates fabrication of more economical or light weight assemblies. On the other hand, in the design stage, availability of different alternative materials apparently increases the complexity of the design optimization problem and arises the need for efficient optimization techniques. In the present study, the well-known big bang–big crunch optimization algorithm is reformulated for optimum design of internally pressurized tightly fitted multi-layer composite tubes with axially constrained ends. An automated material selection and thickness optimization approach is employed for both weight and cost minimization of one-, two-, and three-layer tubes, and the obtained results are compared. The numerical results indicate the efficiency of the proposed approach in practical optimum design of multi-layer composite tubes under internal pressure and quantify the optimality of different composite assemblies compared to one-layer tubes.

Keywords

Metaheuristics Composite assembly Evolutionary algorithm Multi-layer composite tubes Big bang–big crunch algorithm Design optimization 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

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Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  1. 1.Department of Civil EngineeringAtilim UniversityAnkaraTurkey

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