Multi-objective optimization of composite structures made by resin transfer moulding

Due to the wide range of variables involved and sophisticated analysis techniques required, optimum structural design of laminated composite materials is known to be a challenge for designers. The complexity of the design problem is increased when manufacturing issues are also taken into account due to their strong connection with the structural performance. The major challenge involved in the coupled design problem is to handle multiple conflicting objectives. The problem, with its proper consideration through multi-objective optimization, is studied in this research.;For each strategy a hybrid algorithm consisting of a local search, a global search and a multi-objective optimization technique is proposed. The hybrid algorithms are developed particularly to provide a convergence rate higher than the one provided by available GA-based optimization methods. The extent of achieving this goal is evaluated by several test problems.;The proposed hybrid algorithms are applied to two practical composite design problems, the results of which confirm the presence of a strong interconnection among structural and manufacturing objectives and demonstrate the effectiveness of the coupled design approach in providing alternative designs. Although dominated by the structural parameters, performance of a composite structure made by resin transfer moulding is shown to be strongly affected by the manufacturing objectives, whose consideration during the design process can result in a significant improvement in the overall performance of the composite part.;To solve the related multi-objective optimization problem, which for its intrinsic nature does not have a single solution, three strategies are proposed here. The first strategy requires reduced computational effort, while it yields only one solution. The second strategy requires more computational effort and returns a moderate number of optimum solutions. The third strategy, though involves high computational effort, provides a large number of optimum solutions and a clear image of the trade-off among objectives.