NEURAL NETWORK ANALYSIS APPLICATION TO PERMEABILITY DETERMINATION OF FIBERGLASS AND CARBON PREFORMS

作     者：Hossein Golestanian Mehrdad Poursina 

作者机构：Mechanical Engineering GroupFaculty of EngineeringUniversity of ShahreKordShahreKord ***Iran 

出 版 物：《Chinese Journal of Polymer Science》 (高分子科学(英文版))

年 卷 期：2009年第27卷第2期

页      面：221-229页

核心收录：

学科分类：08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 080502[工学-材料学] 

主　　题：Artificial neural network Permeability Porosity Resin transfer molding. 

摘      要：Preform permeability is an important process parameter in liquid injection molding of composite *** parameter is currently determined with time consuming and expensive experimental *** paper presents the application of a back-propagation neural network to predicting fiber bed permeability of three types of reinforcement mats. Resin flow experiments were performed to simulate the injection cycle of a resin transfer molding *** results of these experiments were used to prepare a training set for the back propagation neural network *** reinforcements consisted of plain-weave carbon,plain-weave fiberglass,and chopped fiberglass *** effects of reinforcement type, porosity and injection pressure on fiber bed permeability in the preform principal directions were ***,in the training of the neural network reinforcement type,these process parameters were used as the input *** bed permeability values were the specified output of the *** a result of the specified parameters,the program was able to estimate fiber bed permeability in the preform principal directions for any given processing *** results indicate that neural network may be used to predict preform permeability.