Multi-objective approach to optimize cure process for thick composite based on multi-field coupled model with RBF surrogate model

To mitigate the risk of manufacturing defects of thick composite component and improve the efficiency of this process, a multi-objective optimization approach was proposed to optimize the cure process using the multi-field coupled model, surrogate model and genetic algorithm. A multi-field coupled FE model which takes the heat transfer, resin viscosity and resin flow-compaction process into consideration was developed to forecast the cure state of composite. A surrogate model was also built through radial basis function (RBF) to reduce the computational cost and promote the optimization efficiency. After that, the non-dominated sorting genetic algorithm-II (NSGA-II) was combined with the surrogate model to search for global optimum solution. The results indicate that the proposed multi-objective approach proposed in this paper effectively reduce the cure time, maximum temperature overshoot and maximum gradient of DoC simultaneously, hence leading to good performances on thick composite laminate. This work provides guidance in practical design of cure profile for thick composite part.

Automated summary

A multi-objective optimization approach was proposed in this paper to optimize the cure process of thick composite components, using a multi-field coupled model, surrogate model, and genetic algorithm. The results show that this approach effectively reduces cure time, maximum temperature overshoot, and maximum gradient of DoC, leading to improved performance of thick composite laminate.