An Expert System for Life Prediction of Woven-Roving GFRE Closed End Thick Tube Subjected to Combined Bending Moments and Internal Hydrostatic Pressure Using Artificial Neural Network

Abstract:

As the study of fatigue failure of composite materials needs a large number of experiments as well as long time, so there is a need for new computational technique to expand the spectrum of the results and to save time. The present work represents a new technique to predict the fatigue life of Woven Roving Glass Fiber Reinforced Epoxy (GFRE) subjected to combined completely reversed bending moments and internal hydrostatic pressure, with different pressure ratios (P_r) between the applied pressure and burst pressure equal to (0, 0.25, 0.5 and 0.75). Two fiber orientations (θ), [0^o,90^o]_3s and [±45^o]_3s are considered. Two neural network structures, feed-forward (FFNN) and generalized regression (GRNN), are designed, trained and tested. The groups of data considered are the maximum stress and the Pressure ratio with the different fiber orientation. On the other hand, more accurate prediction method is obtained by using a useful expert system which is designed to aid the designer to decide whether his suggested data for the composite structure is suitable or not. In this expert system a neural network is designed to consider the design data as input and to get yes or no as output. The results show the feed-forward neural network is better results than that given by the generalized regression neural network. The designed expert system helped the designer with reliable conclusions about his decision of the combination of the proposed data.

• Periodical: Advanced Materials Research (Volume 845)
• Pages: 12-17
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.845.12
• Online since: December 2013
• Authors: Mohamed N. Abouelwafa, Hassan A. El-Gamal, Yasser S. Mohamed, Wael A. Al-Tabey*
• Keywords: Expert System, Fatigue of Composites, Neural Network Modeling, Neural Network Predication

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