Multi-step blended stacking sequence design of panel assemblies with buckling constraints

IJsselmuiden, Samuel T.; Abdalla, Mostafa; Seresta, Omprakash; Gürdal, Zafer

doi:10.1016/j.compositesb.2008.12.002

Cited by 75 publications

(60 citation statements)

References 20 publications

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“…8. The problem was proposed by Soremekun et al [8] and subsequently examined in [9,26,11]. The dimensions of the panels and the local loadings are given in the figure.…”

Section: Resultsmentioning

confidence: 99%

“…FE modeling is required to assess load redistributions in complex structures which raises the problem of the calculation costs. A two-step design method, as in [26], or response surface methods, as in [27], may be needed to circumvent the difficulty. Fig.…”

Section: Resultsmentioning

confidence: 99%

“…The array of connectivity v is given below (r = 18). [8,9,26,11]. The critical buckling mode is the mode of minimal buckling factor k.…”

Section: Appendix a Connectivity Between Panelsmentioning

confidence: 99%

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Evolutionary Optimization

Blum

Chiong

Clerc³

et al. 2012

Variants of Evolutionary Algorithms for Real-World Applications

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a b s t r a c tThis article introduces the concept of stacking sequence table (SST) for the optimal design of laminated composite structures with ply drops. The SST describes the sequence of ply-drops ensuring the transition between a thick guide laminate and a thinner one. A blended design is represented by a SST combined with a thickness distribution over the regions of the structure. An evolutionary algorithm is specialized for SST-based blending optimization. Optimization of the sequence of ply-drops with the proposed algorithm enables satisfying design guidelines that could not have been considered in previous studies. An extensive set of design guidelines representative of the actual industrial requirements is introduced. The method is applied to an 18-panel benchmark problem from the literature with convincing results. In particular, the present results show that strength-related guidelines can be enforced without significantly penalizing the stiffness behavior and consequently the mass of the structure.

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“…8. The problem was proposed by Soremekun et al [8] and subsequently examined in [9,26,11]. The dimensions of the panels and the local loadings are given in the figure.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Evolutionary Optimization

Blum

Chiong

Clerc³

et al. 2012

Variants of Evolutionary Algorithms for Real-World Applications

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“…A bi-level optimization strategy has been shown by Yamazaki (1996), Liu et al (2000, Ijsselmuiden et al (2009), Liu et al (2011 to provide a suitable means for solving such problems efficiently without requiring an excessive amount of the computing time. A practical approach to laminated composite design has been suggested by Zhou et al (2009Zhou et al ( , 2010 that is a three-phase optimization process guiding the composite laminate designs from a concept to the final plybook details.…”

Section: Bi-level Optimization Strategymentioning

confidence: 99%

Weight and mechanical performance optimization of blended composite wing panels using lamination parameters

Liu

Торопов

Barton

et al. 2015

Struct Multidisc Optim

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In this paper, a lamination parameter-based approach to weight optimization of composite aircraft wing structures is addressed. It is a bi-level procedure where at the top level lamination parameters and numbers of plies of the pre-defined angles (0, 90, 45 and −45°) are used as design variables, the material volume is treated as an objective function to be minimized subject to the buckling, strength and ply percentage constraints. At the bottom level the optimum stacking sequence is obtained subject to the requirements on blending and preservation of mechanical properties. To ensure composite blending, a multi-stage optimization is performed by a permutation genetic algorithm aiming at matching the lamination parameters passed from the top level optimization as well as satisfying the layup rules. Two new additional criteria, the 90°ply angle jump index and the stack homogeneity index, are introduced to control the uniformity of the three ply angles (0, 90, 45 and −45) spread throughout the stack as well as improve the stack quality and mechanical performance by encouraging 45°angle change between neighbouring groups of plies. The results of the application of this approach are Page 2 of 28 compared to published results to demonstrate the potential of the developed technique. Keywords:Laminated composite, optimization, stacking sequence, blending, lamination parameters IntroductionStacking sequence optimization of laminated composite structures to satisfy ply continuity (blending) requirements has recently attracted considerable attention by Gürdal et al. (1999), Kristinsdottir et al. (2001), Liu and Haftka (2001), Seresta et al. (2007), Liu and Krog (2008), Liu et al. (2011). Liu et al. (2000 presented a bi-level (global and bottom) strategy for optimization of a composite wing box structure. At the global level, continuous optimization of thicknesses of 0, 90, 45 and 45°plies was performed to minimize the weight of a wing box subject to strain and buckling constraints. For a given number of plies of each orientation and in-plane loads, a permutation genetic algorithm (GA) was used at the bottom level to optimize the stacking sequence in order to maximize the buckling load. The optimum buckling load, which was treated as a function of the loading and the numbers of plies of 0, 90, 45 and 45°orientation, was evaluated by a cubic polynomial response surface approximation. This bi-level approach was also used for maximization of buckling load of composite panels by Liu et al. (2004), layup optimization of anisotropic laminated composite panels by Bloomfield et al. (2009) and stacking sequence optimization of blended composite structures by Liu et al. (2011). The use of lamination parameters to represent the in-plane and flexural stiffness in the optimization of laminated composites was first used by Tsai et al. (1968) and later applied to the buckling optimization of orthotropic laminated plates by Fukunaga and Hirano (1982). Miki (1982), Fukunaga and Chou (1988), and Fukunaga and Sekine (1993) also u...

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“…Based on lamination parameters, two-step approaches were proposed and refined by several authors (Yamazaki 1996;IJsselmuiden et al 2009;Irisarri et al 2011). The first step takes lamination parameters as continuous design variables to search for the optimal stiffness and thickness of the laminate.…”

Section: Introductionmentioning

confidence: 99%

Stacking sequence optimization with genetic algorithm using a two-level approximation

Chen

Lin

et al. 2013

Struct Multidisc Optim

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We propose a new method for laminate stacking sequence optimization based on a two-level approximation and genetic algorithm (GA), and establish an optimization model including continuous size variables (thicknesses of plies) and discrete variables (0/1 variables that represent the existence of each ply). To solve this problem, a first-level approximate problem is constructed using the branched multipoint approximate (BMA) function. Since mixed-variables are involved in the first-level approximate problem, a new optimization strategy is introduced. The discrete variables are optimized through the GA. When calculating the fitness of each member in the population of GA, a second-level approximate problem that can be solved by the dual method is established to obtain the optimal thicknesses corresponding to the each given ply orientation sequence. The two-level approximation genetic algorithm optimization is performed starting from a ground laminate structure, which could include relatively arbitrarily discrete set of angles. The method is first applied to cylindrical laminate design examples to demonstrate its efficiency and accuracy compared with known methods. The capacity of the optimization strategy to solve more complex problems is then demonstrated using a design example. With the presented method, the stacking sequence in analytical tools can be directly taken as design variables and no intermediate variables need be adopted.

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Multi-step blended stacking sequence design of panel assemblies with buckling constraints

Cited by 75 publications

References 20 publications

Evolutionary Optimization

Evolutionary Optimization

Weight and mechanical performance optimization of blended composite wing panels using lamination parameters

Stacking sequence optimization with genetic algorithm using a two-level approximation

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