The Prediction And Optimization Of The Springback Of Composite Structure For Autoclave Molding

Carbon fiber reinforced resin matrix composite is a significant material that has some advantage properties,such as lightweight,high specific strength and modulus.The wide usage of composite structure in the vehicle not only can reduce the weight,but also reach the aim of energy conservation and emission reduction.Autoclave molding is widely applied for the manufacture of composite structure.However,the spring-in will appear during the cure forming process due to the material properties of composite and the technological conditions which has a bad effect on the forming quality of composite structure.Therefore,the prediction and optimization of the springback of composite structure have been the important issue that many researchers pay attention.Numerical simulations based on the finite element method are powerful tools for the prediction of cure forming process.Based on the theory basis of the cure process for resin matrix composites,the finite element models of cure process of L part and U part are built to simulate the process of cure and the cure-induced distortions by using ABAQUS user subroutines.Then,the result of numerical simulation is compared with the experimental data to verify the correction of the finite element model.In order to verify the accuracy of the finite element model,the autoclave molding experiment is implemented and the finite element model corresponding to the experiment is built.Furthermore,the results of experiment and FE model are compared.For the springback of composite structure,Multi-level successive optimization method is put forward to optimize the layup of composite structure and reduce the spring-in angle.Multi-level successive optimization method can transfer the high-dimension problem to low-dimension problem and improve the computational efficiency.This optimization method also can reduce the spring-in angle of composite structure.Multi-level successive optimization method consists of sensitive analysis method,surrogate model and the related optimization algorithm.The sequence of the design variable is determined by the result of sensitive analysis and the surrogate model is built by using Least squares support vector regression method.Then,every level of the design variables is optimized to reduce the spring-in by using the optimization algorithm.The optimization results show that Multi-level successive optimization method is effective.Moreover,this method can improve the forming quality of composite structure and reduce the cure-induce distortion by limited optimization times.