Abstract:
Resin Transfer Molding (RTM) and Compression RTM (CRTM) are popular methods for high volume production of superior quality composite parts. However, the design parameters of these methods must be carefully chosen in order to reduce cycle time, capital layout and running costs, while maximizing final part quality. These objectives are principally governed by the filling and curing phases of the manufacturing cycle, which are strongly coupled in the case of completely non-isothermal processing. Independently optimizing either phase often leads to conditions that adversely affect the progress of the other. In light of this fact, this work models the complete manufacturing cycle as a static Stackelberg game with two virtual decision makers (DMs) monitoring the filling and curing phases, respectively. The model is implemented through a Bi-level Multiobjective Genetic Algorithm (BMOGA), which is integrated with an Artificial Neural Network (ANN) for rapid function evaluations. The obtained results are thus efficient with respect to the objectives of both DMs and provide the manufacturer with a diverse set of solutions to choose from.
