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228169

ARTIFICIAL NEURAL NETWORK MODELLING OF THE MECHANICAL CHARACTERISTICS OF FRICTION STIR WELDED AA7020-T6 ALUMINUM ALLOY

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Last updated: 29 Dec 2024

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Abstract

In the current work, lap joints of the AA7020-T6 aluminum sheets of 3 mm thickness were welded using friction stir welding (FSW). Tensile-shear tests were conducted to evaluate the mechanical characteristics of the AA7020-T6 lap joints. A statistical analysis of variance (ANOVA) was performed to find which FSW process parameters (i.e. the tool rotational and welding speeds) are statistically significant. With the signal to noise (S/N) ratio and ANOVA analyses, the optimal levels of the FSW process parameters could be determined. Also, an artificial neural network (ANN) model was developed to predict the tensile-shear load of the AA7020-T6 Al alloy lap joints. It has been found that the reduction of the tool rotational speed and/or increasing the welding speeds increase(s) the tensile-shear load of the AA7020-T6 aluminum friction stir welded lap joints. The welding speed showed the highest statistical significance on the tensile-shear load of the AA7020-T6 aluminum lap friction stir (FS) welded joints when compared with the tool rotational speed. The developed ANN model showed a good agreement between the predicted and experimental results.

DOI

10.21608/erjsh.2020.228169

Keywords

Friction Stir Welding, artificial neural network, Tensile-shear load, analysis of variance

Authors

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I.

Last Name

Abdullah

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Affiliation

Mechanical Engineering Department, Shoubra Faculty of Engineering, Benha University, Cairo, Egypt.

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First Name

S.

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S. Mohammed

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Affiliation

Mechanical Engineering Department, Shoubra Faculty of Engineering, Benha University, Cairo, Egypt.

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First Name

S.

Last Name

A. Abdallah

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Affiliation

Mechanical Engineering Department, Shoubra Faculty of Engineering, Benha University, Cairo, Egypt.

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Volume

46

Article Issue

1

Related Issue

32693

Issue Date

2020-10-01

Receive Date

2022-03-30

Publish Date

2020-10-01

Page Start

6

Page End

10

Print ISSN

3009-6049

Online ISSN

3009-6022

Link

https://erjsh.journals.ekb.eg/article_228169.html

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https://erjsh.journals.ekb.eg/service?article_code=228169

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228,169

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Research articles

Type Code

2,276

Publication Type

Journal

Publication Title

Engineering Research Journal (Shoubra)

Publication Link

https://erjsh.journals.ekb.eg/

MainTitle

ARTIFICIAL NEURAL NETWORK MODELLING OF THE MECHANICAL CHARACTERISTICS OF FRICTION STIR WELDED AA7020-T6 ALUMINUM ALLOY

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Article

Created At

23 Jan 2023