Journal of the Society of Materials Science, Japan
Online ISSN : 1880-7488
Print ISSN : 0514-5163
ISSN-L : 0514-5163
Original Papers
Dimension and Stacking Sequence Optimization of Large Composite Structures Using Kriging and MOGA Method
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2007 Volume 56 Issue 5 Pages 432-437


Laminated composite structures are increasingly used for aerospace structures, such as interstage structures of launch vehicles. For an optimal design of the laminated composite structures, both dimensions and stacking sequences of the structures should be optimized simultaneously since they affect each other. The stacking sequence optimization of composite laminates is a combinatorial optimization problem with some constraints. That makes the optimization of dimensions and stacking sequences a complex mixed problem that contains both combinatorial and continuous-discrete variable optimizations. Therefore, the design of the laminated composite structures has generally been carried out using a genetic algorithm (GA). GA inherently requires many evaluations of optimization functions during the process. It must be time consuming especially for composite structural optimizations, which needs a computationally expensive analysis for the evaluation of the functions such as stress and buckling load. Authors have proposed an effective and global optimization method of the composite structures. The method utilizes a kriging method for approximating expensive functions of the optimization. Using the kriging models makes it possible not only to reduce the computational cost to evaluate the functions but also to search for the optimal design globally with using multi-objective genetic algorithm (MOGA). In the method, fractal branch and bound (FBB) method is also adopted, which is a practical and low-cost stacking sequence optimization method. In the present paper, the proposed method is applied to a complicated design problem of a large-sized composite structure. A design of a rocket structure under combined load is addressed here. The result shows that the method is effective in the large-sized optimization problems.

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© 2007 by The Society of Materials Science, Japan
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