Optimization for CFRP pultrusion process based on genetic algorithm-neural network
The temperature and the degree of cure of carbon fiber reinforced polymer (CFRP) are coupled during pultrusion. In this paper, the governing equations for heat transfer and resin curing are solved by the combination of finite element method, finite different method and indirect decoupling method. The kinetic parameters needed for simulation are obtained from differential scanning calorimetry (DSC) measurement. The temperature of composites on real time during pultrusion is monitored by the fiber Bragg grating (FBG) sensor. And the final degree of cure of composites is also measured through Sorbitic extraction. It shows that the simulation procedure is effective and reliable and predicts temperature and degree of cure which are in good agreement with the experimental results. On the basis of the simulated results, the relationship between processing parameters and degree of cure is formed by artificial neural network. And genetic algorithm combines with the artificial neural network to solve the bi-objective optimization for CFRP pultrusion. It shows that there are considerable improvements in pull speeds and die temperatures after optimization by ANN and GA.
optimization pultrusion genetic algorithm carbon fiber reinforced polymer neural network fiber bragg grating sensor