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227490

MODELLING OF THERMAL DRILLING OF AA7075 ALUMINUM ALLOYS USING REGRESSION ANALYSIS AND ARTIFICIAL NEURAL NETWORKS TECHNIQUES

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Last updated: 29 Dec 2024

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Abstract

In the present research, the effects of the tool spindle speed and the conical angle on the hole
diameter, bushing height and bushing thickness of the thermally drilled AA7075 aluminum alloy sheets were
investigated. The AA75075 Al sheets have 3.4 mm thickness. Three different tools, made from H13 tool steel,
with 25o, 30o and 35o conical angles were manufactured. The holes were drilled at different spindles speeds,
typically, 3100 rpm, 3400 rpm and 3700 rpm. Both regression analysis (RA) and artificial neural (AN) modeling
techniques were used for prediction the effect of the spindle speed and the conical angle on the hole diameter,
bushing height and bushing thickness. The results revealed that the spindle rotational speed and conical angle
affects the hole diameter, bushing height and bushing thickness. The mean absolute errors for the developed
regression models were about 0.0439506, 0.204691 and 0.0595062 for models used to predict the hole diameter,
bushing height and bushing thickness, respectively. The hole diameter, bushing height and thickness were
successfully predicted using artificial neural network (ANN) modelling. The MLP neural network architecture
2‐7‐3 with Tanh transfer function exhibited the best performance with 83.62% accuracy. There is a good
agreement between the measured results and simulated outputs obtained with the ANN modelling.

DOI

10.21608/erjsh.2021.227490

Keywords

Friction drilling, Aluminum alloys, Thermal drilling, artificial neural network

Authors

First Name

Ahmad

Last Name

R.M.M. Alenzi

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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

S. Mohammed

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Affiliation

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

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Volume

49

Article Issue

1

Related Issue

32437

Issue Date

2021-07-01

Receive Date

2022-03-28

Publish Date

2021-07-01

Page Start

60

Page End

66

Print ISSN

3009-6049

Online ISSN

3009-6022

Link

https://erjsh.journals.ekb.eg/article_227490.html

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https://erjsh.journals.ekb.eg/service?article_code=227490

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227,490

Type

Research articles

Type Code

2,276

Publication Type

Journal

Publication Title

Engineering Research Journal (Shoubra)

Publication Link

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

MainTitle

MODELLING OF THERMAL DRILLING OF AA7075 ALUMINUM ALLOYS USING REGRESSION ANALYSIS AND ARTIFICIAL NEURAL NETWORKS TECHNIQUES

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Article

Created At

23 Jan 2023