Multiple objective crashworthiness optimization of circular tubes with functionally graded thickness via artificial neural networks and genetic algorithms

Baykasoğlu, Adil; Baykaşoğlu, Cengiz

doi:10.1177/0954406215627181

Cited by 35 publications

(14 citation statements)

References 64 publications

(119 reference statements)

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“…This is done in order to generate an objective function for the optimization process as well as to reduce the computational costs significantly. An ANN is considered as a black box that takes the inputs and creates outputs accordingly (Baykasog˘lu and Baykasog˘lu, 2017). This is done in MATLAB neural network toolbox, which is an efficient and user-friendly option for creating such networks.…”

Section: Function Approximation Using Annmentioning

confidence: 99%

Optimization of a piezoelectric energy harvester considering electrical fatigue

Niasar

Dahmardeh

Googarchin

2020

Journal of Intelligent Material Systems and Structures

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The application of piezoelectric materials in energy harvesting devices has become an interesting topic for designers in recent years. Most of the reported works consider electrical power as a major parameter throughout the design procedure. What is less discussed is considering other design factors like the fatigue life of the harvester as well as the financial costs of the device. In this research, a methodology is introduced to design an optimum harvester, taking into account the aforementioned design factors. The finite element model of a piezoelectric harvester used in roadways is developed and validated. The fatigue test constants of the piezoelectric material are extracted via a novel approach. A parametric study is conducted on the model to generate a dataset containing electrical and mechanical characteristics, where it is used as an input for training an artificial neural network to model the behavior of the harvester. In order to evaluate the level of importance of the optimization objectives, two methods are employed: the Shannon entropy method and equal weighting factors. Results show the effectiveness of the model, where considering the electromechanical characteristics of the module is important in terms of overall performance, efficiency, durability, and financial costs.

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Section: Function Approximation Using Annmentioning

confidence: 99%

Optimization of a piezoelectric energy harvester considering electrical fatigue

Niasar

Dahmardeh

Googarchin

2020

Journal of Intelligent Material Systems and Structures

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“…), 6,7 multi-cell configurations 8–11 and longitudinal geometries (i.e. conical, s-shaped structures, corrugated structures and tubes with functionally graded thickness) 12–23 have been investigated in the past years to improve their crushing capacity.…”

Section: Introductionmentioning

confidence: 99%

Crashworthiness performance of concentric structures with different cross-sectional shapes under multiple loading conditions

Pirmohammad

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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This paper evaluates the crashworthiness performance of concentric structures with different numbers of tubes (i.e. one to five) and cross-sectional shapes (i.e. hexagon, octagon, decagon and circle) under the multiple loadings of θ = 0, 10, 20 and 30°. An experimentally validated finite element model generated in LS-DYNA is employed to calculate the crashworthiness parameters including the specific energy absorption, maximum crush force and crush force efficiency. A total of 20 concentric structures are analyzed to explore the effects of number of tubes and cross-sectional shapes on the crushing performance. A multi-criteria decision-making method known as TOPSIS is also used to compare and rank the concentric structures in terms of crushing performance. Based on the results, the hexagonal structure including two tubes and octagonal, decagonal and circular structures including three tubes demonstrate the best results among their corresponding cross-sectional shapes. These structures show 9, 39, 38 and 39% higher specific energy absorption compared to their corresponding single tubal cases, respectively. However, in comparison to single tubal cases, they generate 4, 57, 57 and 58% higher maximum crush force, respectively. As such, the values for the improvement of the crush force efficiency are 3, 26, 25 and 21%, respectively. Furthermore, the decagonal structure including three tubes provides the highest energy absorbing characteristics as compared with all the other structures studied in this research. Meanwhile, taking into account all the multiple loading conditions, this structure shows 50% higher specific energy absorption than the hexagonal structure including single tube (as the weakest structure).

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“…On the other hand, nature-inspired algorithms including genetic algorithm (GA) and non-dominated sorting genetic algorithm (NSGA-II) are utilized in a wide range of researches whenever the closed form of objective functions are available. 32,33 These algorithms can be used to overcome the challenges due to multiple objective functions and mixed design variables. 34 Ehsani and Rezaeepazhand 35 employed GA to optimize stacking sequence and pattern composition for maximizing the axial and shear buckling load of laminated grids with different boundary conditions and aspect ratios in laminated grid structures.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective optimization of a newly developed additively manufactured functionally graded anisotropic porous lattice structure

Mahbod

Asgari

2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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In this paper, the elastic behavior of uniform and functionally graded porous lattice structures made by a double pyramid dodecahedron unit cell is investigated. Analytical solutions are derived in order to estimate the elastic moduli of the proposed structures in two directions. The analytical solution is validated by finite element simulations and experimental tests while the results show good agreement in general. The average difference between the numerical and analytical values of elastic modulus is under 14.44%, while the average error of experimental test and analytical solution is 15.69%. A comprehensive optimization is performed by considering elastic moduli in two different directions as objective functions. Various uniform lattice structures with different relative densities are optimized using NSGA-II algorithm as well as lattice structures with graded distribution of porosity. A variety of optimal designs are achieved by multiobjective optimization algorithm and the best point of the Pareto front is selected by the TOPSIS method. Furthermore, the functionally graded lattice structures are optimized by considering desirable relative densities in each layer and applying constructive constraints. Different distribution patterns of relative density are considered in layers in order to present the flexible design capability of the developed structure. The obtained results show that the elastic modulus is significantly dependent on the relative density of each layer as well as cell configuration. Also, different lattice structures could be achieved by applying desirable prescribed distribution of properties. A comparison between optimized and base model indicated that elastic moduli was considerably improved in optimized models. In optimization of uniform models, [Formula: see text] was increased by 115%, 89%, and 69% in optimized structures for relative densities of 10%, 30%, and 50%, respectively. Moreover, [Formula: see text] was improved in optimized models by 27%, 24%, and 18% for relative densities of 10%, 30%, and 50%, respectively.

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Multiple objective crashworthiness optimization of circular tubes with functionally graded thickness via artificial neural networks and genetic algorithms

Cited by 35 publications

References 64 publications

Optimization of a piezoelectric energy harvester considering electrical fatigue

Optimization of a piezoelectric energy harvester considering electrical fatigue

Crashworthiness performance of concentric structures with different cross-sectional shapes under multiple loading conditions

Multiobjective optimization of a newly developed additively manufactured functionally graded anisotropic porous lattice structure

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