Multiobjective Optimization of Laminated Composites using Finite Element                 Method and Genetic Algorithm

Deka, Dhyan Jyoti; Sandeep, G.; Chakraborty, Debabrata; Dutta, Anjan

doi:10.1177/0731684405043555

Cited by 24 publications

(9 citation statements)

References 14 publications

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“…The Method of weighted-sum, which is the most common MOO method, belongs to this group. Examples of applications of this method in stacking sequence design of composite materials can be found in [17][18][19][20]. Another common non-generating method is called the ε-constraint method.…”

Section: Optimization Algorithmmentioning

confidence: 99%

Optimum Structural and Manufacturing Design of a Braided Hollow Composite Part

et al. 2009

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Simultaneous material consolidation and shaping, as performed in manufacturing of composite materials, causes a strong interconnection between structural and manufacturing parameters which makes the design process complicated. In this paper, the design of a carbon fiber bicycle stem is examined through the application of a multi-objective optimization method to illustrate the interconnection between structural and manufacturing objectives. To demonstrate the proposed method, a test case dealing with the design of composite part with complex geometry, small size and hollow structure is described. Bladder-assisted Resin Transfer Molding is chosen as the manufacturing method. A finite element model of the stem is created to evaluate the objectives of the structural design, while a simplified 2D model is used to simulate the flow inside the preform during the injection process. Both models are formulated to take into account the variation of fiber orientation, thickness and fiber volume fraction as a function of braid diameters, injection pressure and bladder pressure. Finally, a multiobjective optimization method, called Normalized Normal Constraint Method, is used to find a set of solutions that simultaneously optimizes weight, filling time and strength. The solution to the problem is a set of optimum designs which represent the Pareto frontier of the problem. Pareto frontier helps to gain insight into the trade-off among objectives, whose presence and importance is confirmed by the numerical results presented in this paper.

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Section: Optimization Algorithmmentioning

confidence: 99%

Optimum Structural and Manufacturing Design of a Braided Hollow Composite Part

et al. 2009

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“…In typical engineering applications, composite materials are under very complicated loading conditions, not only in-plane loading but also out-of-plane loading. Most of the studies on the optimization of the laminate composite material minimized the thickness [18], [7], weight [19], [20], [21], and cost and weight [20], [22], or maximized the static strength of the composite laminates for a targeted thickness [7], [8], [23], [24]. In the present study, the cost and weight of laminates are minimized by modifying the objective function.…”

Section: Introductionmentioning

confidence: 98%

A Technique for Constrained Optimization of Cross-ply Laminates using a New Variant of Genetic Algorithm

Zhang¹,

Yokoyama²

2021

IJACSA

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The main challenge presented by the design of laminated composite material is the laminate layup, involving a set of fiber orientations, composite material systems, and stacking sequences. In nature, it is a combinatorial optimization problem with constraints that can be solved by the genetic algorithm. The traditional approach to solve a constrained problem is reformulating the objective function. In the present study, a new variant of the genetic algorithm is proposed for the design of composite material by using a mix of selection strategies, instead of modifying the objective function. To check the feasibility of a laminate subject to in-plane loading, the effect of the fiber orientation angles and material components on the first ply failure is studied. The algorithm has been validated by successfully optimizing the design of cross-ply laminate under different inplane loading cases. The results obtained by this algorithm are better than works in related literature.

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“…The stacking sequence and fiber orientation of composite laminates give the designer additional degree of freedom to tailor the design with respect to strength or stiffness. One widely known advantage of using composite material is can significantly reducing the weight of target structure, and many researchers attempted to improve the efficiency of using composite material by minimizing the thickness [1][2][3][4][5][6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Optimum Design of Laminated Composites for Minimum Thickness by a Variant of Genetic Algorithm

ZHANG

Yokoyama

2022

J. Text. Eng.

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In the present study, a method of optimizing the design of laminated composite with new genetic algorithm techniques, selection strategy and mutation operator, is proposed. Experiments are designed to minimize thickness (or weight) of midplane-symmetric composite laminate subject to in-plane loading, with fiber orientation and ply thickness chosen as design variables. To avoid spurious laminate designs, both the Tsai-Wu and the maximum stress criteria are taken to determine whether load-bearing capacity is exceeded or not. The state-of-art experiment results are obtained for some loading cases, related numerical results are presented under different cases. The simulation results clearly show that the proposed algorithm provide better or equivalent solutions for the design of laminated composites than work in related literature.

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Multiobjective Optimization of Laminated Composites using Finite Element Method and Genetic Algorithm

Cited by 24 publications

References 14 publications

Optimum Structural and Manufacturing Design of a Braided Hollow Composite Part

Optimum Structural and Manufacturing Design of a Braided Hollow Composite Part

A Technique for Constrained Optimization of Cross-ply Laminates using a New Variant of Genetic Algorithm

Optimum Design of Laminated Composites for Minimum Thickness by a Variant of Genetic Algorithm

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