Stacking sequence optimization with genetic algorithm using a two-level approximation
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A new method for laminate stacking sequence optimization based on a two-level approximation and genetic algorithm (GA), and an optimization model including continuous size variables (thicknesses of plies) and discrete variables that represent the existence of each ply is proposed.Abstract:
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.read more
Citations
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Laminate stacking sequence optimization with strength constraints using two-level approximations and adaptive genetic algorithm
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References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Journal ArticleDOI
Optimum stacking sequence design of composite materials Part II: Variable stiffness design
TL;DR: In this paper, a composite laminate may be designed as a permutation of several straight-fiber layers or as a matrix embracing fibers positioned in curvilinear paths.
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Optimum stacking sequence design of composite materials Part I: Constant stiffness design
TL;DR: In this paper, the main optimization methods for composite laminate with uniform stacking sequence through their entire structure are described, their characteristic features are contrasted, and the potential areas requiring more investigation are highlighted.
Journal ArticleDOI
Design of experiments for stacking sequence optimizations with genetic algorithm using response surface approximation
Akira Todoroki,Tetsuya Ishikawa +1 more
TL;DR: In this paper, a new method of experimental design to obtain a response surface of buckling load of laminated composites is described, and a set of stacking sequences selected from candidate stacks using D-optimality is proposed.
Journal ArticleDOI
Multi-step blended stacking sequence design of panel assemblies with buckling constraints
TL;DR: In this article, a multi-step framework for composite panel assemblies and subsequent blending of the designs to ensure laminate continuity across multi-panel configurations is proposed, where the structure is first optimised using panel thickness and lamination parameters as continuous design variables.
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