Design of dimensionally stable composites by evolutionary optimization

Riche, Rodolphe Le; Gaudin, Jocelyn

doi:10.1016/s0263-8223(98)00009-9

Cited by 31 publications

(14 citation statements)

References 6 publications

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“…Multilevel strategies were developed over the last twenty years. These approaches can be divided into two main categories: multilevel parameter optimization (which includes sequential methods [5][6][7] and iterative methods [8][9][10][11][12][13]) and multilevel model optimization (which includes hierarchical multilevel optimization [1,14] and imbricated multilevel optimization [15]). Similar strategies have also been used in the context of multidisciplinary optimization where each level of the optimization process concerns a specific discipline [9,16].…”

Section: The Optimization Processmentioning

confidence: 99%

Generation of a cokriging metamodel using a multiparametric strategy

2012

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In the course of designing structural assemblies, performing a full optimization is very expensive in terms of computation time. In order or reduce this cost, we propose a multilevel model optimization approach. This paper lays the foundations of this strategy by presenting a method for constructing an approximation of an objective function. This approach consists in coupling a multiparametric mechanical strategy based on the LATIN method with a gradientbased metamodel called a cokriging metamodel. The main difficulty is to build an accurate approximation while keeping the computation cost low. Following an introduction to multiparametric and cokriging strategies, the performance of kriging and cokriging models is studied using one-and two-dimensional analytical functions; then, the performance of metamodels built from mechanical responses provided by the multiparametric strategy is analyzed based on two mechanical test examples.

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Section: The Optimization Processmentioning

confidence: 99%

Generation of a cokriging metamodel using a multiparametric strategy

2012

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“…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]. GA have also been applied in the design of a variety of composite structures ranging from simple rectangular plates to complex geometrical sheets, such as sandwich panels [45], rigid sheets [46], bolt joints [47], and laminated cylindrical panels [34].…”

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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“…• the optimization of dimensional and thermal buckling stability under hygrothermal loads (Le Riche and Gaudin, 1998;Spallino and Thierauf, 2000);…”

Section: Optimization Of Composite Structures By Genetic Algorithmsmentioning

confidence: 99%

An Improved Genetic Algorithm for the Optimization of Composite Structures

Gantovnik

Fadel

̈rdal

2006

Volume 1: 32nd Design Automation Conference, Parts a and B

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There are many diverse applications that are mathematically modelled in terms of mixed discrete-continuous variables. The optimization of these models is typically difficult due to their combinatorial nature and potential existence of multiple local minima in the search space. Genetic algorithms (GAs) are powerful tools for solving such problems. GAs do not require gradient or Hessian information. However, to reach an optimal solution with a high degree of confidence, they typically require a large number of analyses during the optimization search. Performance of these methods is even more of an issue for problems that include continuous variables. iii DedicationThe author wishes to dedicate this work to the memory of his grandfather, Vladimir Alexandrovich Pticyn , who was a mechanical engineer.iv Acknowledgments

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Design of dimensionally stable composites by evolutionary optimization

Cited by 31 publications

References 6 publications

Generation of a cokriging metamodel using a multiparametric strategy

Generation of a cokriging metamodel using a multiparametric strategy

Memetic Computing Applied to the Design of Composite Materials and Structures

An Improved Genetic Algorithm for the Optimization of Composite Structures

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