Prediction and optimization of cure cycle of thick fiber-reinforced composite parts using dynamic artificial neural networks

Curing of thermoset-based composites experience substantial temperature overshoot, especially at the center of thick parts and large temperature gradient exists through the whole part due to large amount of heat released and low conductivity of the composite. This leads to non-uniformity of cure, residual stress and consequently composite cracks and possibly degradation of the polymer. The scope of this work is to optimize the cure cycle in order to improve the properties and gaining a relatively uniform part of composite, using trained recurrent artificial neural networks purposed for speeding up the repetitious model re-calls during the optimization process. Numerical results obtained based on the three-dimensional finite volume method is used to train the network. The optimization problem is aimed to develop multi-linear-stage cure cycles by minimizing the objective function that includes maximum temperature difference through the cure cycle with the constraints of maximum allowable temperature considering degradation temperature of the polymer, minimum temperature of cure initiation, and finally reaching a maximum final conversion. The results showed the effectiveness of the approach used in this study in terms of the optimization computation time.