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The optimized design of composite structures is a difficult task. It requires optimizing simultaneously both structural and manufacturing objectives. The objectives do not have closed form solutions and have multiple local optima that calls for a global search. This paper improves the global search method called GBNM [1], which is based on several restarts of the Nelder-Mead method. Two issues are addressed here. First, the restart procedure is improved by using a one-dimensional probability function and a weighted selection procedure. Second, nonlinear constraints are included by projecting the infeasible points onto the nonlinear constraints. The improved procedure is applied to four mathematical test functions. Numerical results show the proposed approach is more efficient in terms of computational time and probability of finding the global minimum. The improved GBNM is then applied to the simultaneous structural and manufacturing design of a Z-shaped composite bracket. The results are compared to those obtained with the genetic algorithm.

Simultaneous material consolidation and shaping, as performed in manufacturing of composite materials, causes a strong interconnection between structural and manufacturing parameters which makes the design process complicated. In this paper, the design of a carbon fiber bicycle stem is examined through the application of a multi-objective optimization method to illustrate the interconnection between structural and manufacturing objectives. To demonstrate the proposed method, a test case dealing with the design of composite part with complex geometry, small size and hollow structure is described. Bladder-assisted Resin Transfer Molding is chosen as the manufacturing method. A finite element model of the stem is created to evaluate the objectives of the structural design, while a simplified 2D model is used to simulate the flow inside the preform during the injection process. Both models are formulated to take into account the variation of fiber orientation, thickness and fiber volume fraction as a function of braid diameters, injection pressure and bladder pressure. Finally, a multiobjective optimization method, called Normalized Normal Constraint Method, is used to find a set of solutions that simultaneously optimizes weight, filling time and strength. The solution to the problem is a set of optimum designs which represent the Pareto frontier of the problem. Pareto frontier helps to gain insight into the trade-off among objectives, whose presence and importance is confirmed by the numerical results presented in this paper.

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