Optimum design of composite laminates using genetic algorithms

Callahan, Kelvin J.; Weeks, G. E.

doi:10.1016/0961-9526(92)90001-m

Cited by 107 publications

(41 citation statements)

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“…Genetic algorithms (GA), the most popular evolutionary algorithm, mimic the mechanics of natural genetics for artificial systems based on operation that are the counterparts of natural ones. In the last decade, different GA based approaches for ply orientations or stacking sequence optimization for different functional purposes have been devised and reported in literature [4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Particle swarm optimization approach for multi-objective composite box-beam design

Sundaram

Sujit

Rao

2007

Composite Structures

113

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Abstract:-This paper presents a multi-agent search technique to design an optimal composite box-beam helicopter rotor blade. The search technique is called particle swarm optimization ('inspired by the choreography of a bird flock'). The continuous geometry parameters (cross-sectional dimensions) and discrete ply angles of the box-beams are considered as design variables. The objective of the design problem is to achieve a) specified stiffness value and b) maximum elastic coupling. The presence of maximum elastic coupling in the composite box-beam increases the aeroelastic stability of the helicopter rotor blade. The multi-objective design problem is formulated as a combinatorial optimization problem and solved collectively using particle swarm optimization technique. The optimal geometry and ply angles are obtained for a composite box-beam design with ply angle discretizations of 10• and 45 •. The performance and computational efficiency of the proposed particle swarm optimization approach is compared with various genetic algorithm based design approaches. The simulation results clearly show that the particle swarm optimization algorithm provides better solutions in terms of performance and computational time than the genetic algorithm based approaches.

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Section: Introductionmentioning

confidence: 99%

Particle swarm optimization approach for multi-objective composite box-beam design

Sundaram

Sujit

Rao

2007

Composite Structures

113

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“…An extensive body of research (e.g. Nagendra et al 1992, Callahan and Weeks 1992, Le Riche and Haftka 1993 has instead used genetic algorithms for stacking sequence optimisation of composite laminates. These global search methods are capable of searching the complex design space without becoming trapped by local optima (Ghiasi et al 2009).…”

Section: Reliability-based Design Optimisation Case Studymentioning

confidence: 99%

“…11 Example stability margin PDFs for optimised designs using a design instability speed of 150m/s and layup strategies i) and iii) Fig. 12 Plots of eigenvalue real parts against air-speed for deterministic and reliability-based designs multi-modal, with numerous local optima, as the laminate stiffnesses are nonlinear expressions of periodic functions in the design variables (Ghiasi et al 2009;Callahan and Weeks 1992). As such, the design space is non-convex, and gradient based optimisers are likely to converge to local optima, depending upon the choice of initial design.…”

Section: Reliability-based Design Optimisation Case Studymentioning

confidence: 99%

Reliability-based aeroelastic design of composite plate wings using a stability margin

Scarth

Cooper

2017

Struct Multidisc Optim

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Reliability-based design is concerned with ensuring that constraints are enforced with acceptable probability under inherent variability in properties. In aircraft design, such a constraint may be that aeroelastic instability does not occur at velocities encountered by the aircraft. This approach can be complicated, as the aeroelastic instability speed is a discontinuous function of material properties, on account of particular modes only becoming unstable for some parameter values. In reliability analysis, it is common to use surrogate models due to the computational expense associated with Monte Carlo Simulation, however, such methods can be inaccurate when emulating discontinuous functions such as the aeroelastic instability speed. In this paper, an alternative approach is proposed in which Gaussian process surrogate models are fitted directly to each of the modal eigenvalues at the design air-speed, and used to emulate a stability margin based upon the most critical eigenvalue. Using this approach, it is shown that the reliability may be estimated for the aeroelastic stability using smooth emulators, thereby overcoming the problems associated with discontinuities. The method is demonstrated for layup optimisation of composite plate wings with uncertain ply angles, in which the probability of aeroelastic instability occurring is minimised for a prescribed air-speed. In uncertainty quantification, a good agreement is found with Monte Carlo Simulation with an order of two magnitudes reduction in model runs. Through reliability-based design, reductions in the probability of failure of up to 99.8% are achieved by increasing the stability margin at the design speed.

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“…Genetic Algorithms (GA) have been the most popular method in the design of laminated polymers and optimizing piling sequence [23]. Callahan and Weeks [24], Nagendra et al [25], Le Riche and Haftka [26], and Ball et al [27] have been the first to adopt and use GA for the design of piling sequences of composite laminated materials. GA has also been used in problems with different objective functions, such as strength [28,29], buckling loads [9,28,[30][31][32][33][34], dimensional stability [35], strain energy absorption [36], weight (either as a restriction or as an objective to minimize) [37,38], bending/torsion connection, stiffness [36,39], basic frequencies [34,[40][41][42], distortion [43], or finding laminate reference parameters [44].…”

Section: State Of the Art Of Composite Materials Designmentioning

confidence: 99%

Memetic Computing Applied to the Design of Composite Materials and Structures

Peláez

Gómez-Ruiz

Veintimilla

et al. 2017

Mathematical Problems in Engineering

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Presently, there exists an important need for lighter and more resistant structures, with reduced manufacturing costs. Laminated polymers are materials which respond to these new demands. Main difficulties of the design process of a composite laminate include the necessity to design both the geometry of the element and the material configuration itself and, therefore, the possibilities of creating composite materials are almost unlimited. Many techniques, ranging from linear programming or finite elements to computational intelligence, have been used to solve this type of problems. The aim of this work is to show that more effective and dynamic methods to solve this type of problems are obtained by using certain techniques based on systematic exploitation of knowledge of the problem, together with the combination of metaheuristics based on population as well as on local search. With this objective, a memetic algorithm has been designed and compared with the main heuristics used in the design of laminated polymers in different scenarios. All solutions obtained have been validated by the ANSYS5 software package.

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Optimum design of composite laminates using genetic algorithms

Cited by 107 publications

References 1 publication

Particle swarm optimization approach for multi-objective composite box-beam design

Particle swarm optimization approach for multi-objective composite box-beam design

Reliability-based aeroelastic design of composite plate wings using a stability margin

Memetic Computing Applied to the Design of Composite Materials and Structures

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