Beta
ManageSearchChatDiscover
69824

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

Article

Last updated: 25 Dec 2024

OverviewSimilar Items

Subjects

-

Tags

-

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

DOI

10.21608/erjm.2008.69824

Keywords

prediction, Delamination size, drilling, Composite materials, Artificial Neural Networks (ANNs)

Authors

First Name

A. I.

Last Name

Selmy

MiddleName

-

Affiliation

Mechanical Design and Production Engineering Departiment, Faculty of Engineering, Zagazig University, P.O. Box 44519, Zagazig, Egvpt.

Email

-

City

-

Orcid

-

First Name

I. A.

Last Name

El-Sonbaty

MiddleName

-

Affiliation

Mechanical Design and Production Engineering Departiment, Faculty of Engineering, Zagazig University, P.O. Box 44519, Zagazig, Egvpt.

Email

-

City

-

Orcid

-

First Name

U. A.

Last Name

Khashaba

MiddleName

-

Affiliation

Mechanical Design and Production Engineering Departiment, Faculty of Engineering, Zagazig University, P.O. Box 44519, Zagazig, Egvpt.

Email

-

City

-

Orcid

-

First Name

A. A.

Last Name

Megahed

MiddleName

-

Affiliation

Mechanical Design and Production Engineering Departiment, Faculty of Engineering, Zagazig University, P.O. Box 44519, Zagazig, Egvpt.

Email

-

City

-

Orcid

-

Volume

31

Article Issue

4

Related Issue

10537

Issue Date

2008-10-01

Receive Date

2020-02-03

Publish Date

2008-10-01

Page Start

369

Page End

375

Print ISSN

1110-1180

Online ISSN

3009-6944

Article File

ERJM_Volume 31_Issue 4_Pages 369-375.pdf

PDF . 365.9kB

Link

https://erjm.journals.ekb.eg/article_69824.html

Detail API

https://erjm.journals.ekb.eg/service?article_code=69824

Order

7

Type

Original Article

Type Code

1,118

Publication Type

Journal

Publication Title

ERJ. Engineering Research Journal

Publication Link

https://erjm.journals.ekb.eg/

MainTitle

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

Details

Type

Article

Created At

22 Jan 2023