PREDICTION OF DELAMINATION SIZE IN DRILLING FIBER REINFORCED POLYMERIC COMPOSITE MATERIALS USING ARTIfICIAL NEURAL NETWORKS TECHNIQUE
Selmy, A., El-Sonbaty, I., Khashaba, U., Megahed, A. (2008). PREDICTION OF DELAMINATION SIZE IN DRILLING FIBER REINFORCED POLYMERIC COMPOSITE MATERIALS USING ARTIfICIAL NEURAL NETWORKS TECHNIQUE. EKB Journal Management System, 31(4), 369-375. doi: 10.21608/erjm.2008.69824
A. I. Selmy; I. A. El-Sonbaty; U. A. Khashaba; A. A. Megahed. "PREDICTION OF DELAMINATION SIZE IN DRILLING FIBER REINFORCED POLYMERIC COMPOSITE MATERIALS USING ARTIfICIAL NEURAL NETWORKS TECHNIQUE". EKB Journal Management System, 31, 4, 2008, 369-375. doi: 10.21608/erjm.2008.69824
Selmy, A., El-Sonbaty, I., Khashaba, U., Megahed, A. (2008). 'PREDICTION OF DELAMINATION SIZE IN DRILLING FIBER REINFORCED POLYMERIC COMPOSITE MATERIALS USING ARTIfICIAL NEURAL NETWORKS TECHNIQUE', EKB Journal Management System, 31(4), pp. 369-375. doi: 10.21608/erjm.2008.69824
Selmy, A., El-Sonbaty, I., Khashaba, U., Megahed, A. PREDICTION OF DELAMINATION SIZE IN DRILLING FIBER REINFORCED POLYMERIC COMPOSITE MATERIALS USING ARTIfICIAL NEURAL NETWORKS TECHNIQUE. EKB Journal Management System, 2008; 31(4): 369-375. doi: 10.21608/erjm.2008.69824

PREDICTION OF DELAMINATION SIZE IN DRILLING FIBER REINFORCED POLYMERIC COMPOSITE MATERIALS USING ARTIfICIAL NEURAL NETWORKS TECHNIQUE

ERJ. Engineering Research Journal
Article 7, Volume 31, Issue 4, October 2008, Page 369-375  XML PDF (357.3 K)
Document Type: Original Article
DOI: 10.21608/erjm.2008.69824
View on SCiNiTO View on SCiNiTO
Authors
A. I. Selmy; I. A. El-Sonbaty; U. A. Khashaba; A. A. Megahed
Mechanical Design and Production Engineering Departiment, Faculty of Engineering, Zagazig University, P.O. Box 44519, Zagazig, Egvpt.
Abstract
Delamination is a well-recognized problem associated with drilling fiber reinforced composite
materials (FRCMs). The most noted problems occur as the drill enters and exits the FRCM. A
method based on the artificial neural networks (ANNs) technique was used to predict delamination
size resulting itom drilling glass fiber reinforced epoxy (GERE) laminates at both drill entry and
exit sides of the hole. The experimental work that was performed to provide the data used to
develop the required ANNs was presented in [I]. From the statistical analysis, using correlation
coefficients between the target and the output values from the ANN, it is concluded that the
obtained ANNs can be used effectively to model and predict delamination size at both drill entry
and exit sides
Keywords
prediction; Delamination size; drilling; Composite materials; Artificial Neural Networks (ANNs)
Statistics
Article View: 84
PDF Download: 152