Modeling of CVI process in fabrication of carbon/carbon composites by an artificial neural network

Aijun Li, Hejun Li, Kezhi Li, Zhengbing Gu

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

The chemical vapor infiltration(CVI) process in fabrication of carbon-carbon composites is very complex and highly inefficient, which adds considerably to the cost of fabrication and limits the application of the material. This paper tries to use a supervised artificial neural network(ANN) to model the nonlinear relationship between parameters of isothermal CVI(ICVI) processes and physical properties of C/C composites. A model for preprocessing dataset and selecting its topology is developed using the Levenberg-Marquardt training algorithm and trained with comprehensive dataset of tubal C/C components collected from experimental data and abundant simulated data obtained by the finite element method. A basic repository on the domain knowledge of CVI processes is established via sufficient data mining by the network. With the help of the repository stored in the trained network, not only the time-dependent effects of parameters in CVI processes but also their coupling effects can be analyzed and predicted. The results show that the ANN system is effective and successful for optimizing CVI processes in fabrication of C/C composites.

Original languageEnglish
JournalScience in China, Series E: Technological Sciences
Volume46
Issue number2
DOIs
StatePublished - Apr 2003

Keywords

  • Artificial neural network
  • C/C composites
  • Finite element method
  • ICVI process
  • Levenberg-Marquard algorithm

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