Optimizing parameters of CVI process for manufacturing carbon-carbon composites by genetic algorithms

In this paper, the genetic algorithms were used to realize optimization of the chemical vapor infiltration (CVI) process for manufacturing carbon-carbon composites for the first time. The density homogeneity, bulk density and infiltration time were selected as the optimizing objectives, and the density homogeneity was taken as the main optimizing objective. By combining selection, crossover and mutation operator of genetic algorithms with computer simulation technology, the CVI process parameters including deposition temperature and concentration of reacting gases had been optimized. After 30 generation evolutions, the fitness values were converged, and the optimized results were obtained. So, this paper has provided an effective optimization method for CVI process, which can solve the problem of difficulty of establishing a model for the process.