Optimization strategy for curing ultra-thick composite laminates based on multi-objective genetic algorithm

Thermal gradient and temperature overshoot during the curing process of ultra-thick carbon fibre composite have a major impact on its properties. We present a strategy to optimize the curing process based on the surrogate model and genetic algorithm. Firstly, a FE model based on heat transfer was developed for curing a 30 mm thick laminate and validated by experimental data. Then a multi-objective optimization strategy was developed by combining the optimal Latin hypercube sampling method, elliptical basis function (EBF) neural network model and non-dominated sorting genetic algorithm-II (NSGA-II). It is found that the optimized cure cycle can effectively reduce the undesirable maximum DoC (Degree of cure) gradient and the temperature gradient leading to improved mechanical properties for the 30 mm thick laminate.

Automated summary

A strategy based on surrogate model and genetic algorithm was proposed to optimize the curing process of ultra-thick carbon fiber composites, aiming to reduce thermal gradient and temperature overshoot and improve mechanical properties.