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Optimization of surface roughness in deep hole drilling using moth-flame optimization

This study emphasizes on optimizing the value of machining parameters that will affect the value of surface roughness for the deep hole drilling process using moth-flame optimization algorithm. All experiments run on the basis of the design of experiment (DoE) which is two level factorial with four...

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Main Authors: Kamaruzaman, Anis Farhan, Mohd. Zain, Azlan, Alwee, Razana, Md. Yusof, Noordin, Najarian, Farhad
Format: Article
Language:English
Published: Penerbit UTM Press 2019
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/85298/1/AzlanMohdZain2019_OptimizationofSurfaceRoughnessinDeepHole.pdf
http://eprints.utm.my/id/eprint/85298/
https://elektrika.utm.my/index.php/ELEKTRIKA_Journal/article/view/195/
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spelling my.utm.852982020-03-17T08:04:48Z http://eprints.utm.my/id/eprint/85298/ Optimization of surface roughness in deep hole drilling using moth-flame optimization Kamaruzaman, Anis Farhan Mohd. Zain, Azlan Alwee, Razana Md. Yusof, Noordin Najarian, Farhad TK Electrical engineering. Electronics Nuclear engineering This study emphasizes on optimizing the value of machining parameters that will affect the value of surface roughness for the deep hole drilling process using moth-flame optimization algorithm. All experiments run on the basis of the design of experiment (DoE) which is two level factorial with four center point. Machining parameters involved are spindle speed, feed rate, depth of hole and minimum quantity lubricants (MQL) to obtain the minimum value for surface roughness. Results experiments are needed to go through the next process which is modeling to get objective function which will be inserted into the moth-flame optimization algorithm. The optimization results show that the moth-flame algorithm produced a minimum surface roughness value of 2.41μ compared to the experimental data. The value of machining parameters that lead to minimum value of surface roughness are 900 rpm of spindle speed, 50 mm/min of feed rate, 65 mm of depth of hole and 40 l/hr of MQL. The ANOVA has analysed that spindle speed, feed rate and MQL are significant parameters for surface roughness value with P-value <0.0001, 0.0219 and 0.0008 while depth of hole has P-value of 0.3522 which indicates that the parameter is not significant for surface roughness value. The analysis also shown that the machining parameter that has largest contribution to the surface roughness value is spindle speed with 65.54% while the smallest contribution is from depth of hole with 0.8%. As the conclusion, the application of artificial intelligence is very helpful in the industry for gaining good quality of products. Penerbit UTM Press 2019 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/85298/1/AzlanMohdZain2019_OptimizationofSurfaceRoughnessinDeepHole.pdf Kamaruzaman, Anis Farhan and Mohd. Zain, Azlan and Alwee, Razana and Md. Yusof, Noordin and Najarian, Farhad (2019) Optimization of surface roughness in deep hole drilling using moth-flame optimization. Journal of Electrical Engineering, 18 (3-2). pp. 62-68. ISSN 0128-4428 https://elektrika.utm.my/index.php/ELEKTRIKA_Journal/article/view/195/
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Kamaruzaman, Anis Farhan
Mohd. Zain, Azlan
Alwee, Razana
Md. Yusof, Noordin
Najarian, Farhad
Optimization of surface roughness in deep hole drilling using moth-flame optimization
description This study emphasizes on optimizing the value of machining parameters that will affect the value of surface roughness for the deep hole drilling process using moth-flame optimization algorithm. All experiments run on the basis of the design of experiment (DoE) which is two level factorial with four center point. Machining parameters involved are spindle speed, feed rate, depth of hole and minimum quantity lubricants (MQL) to obtain the minimum value for surface roughness. Results experiments are needed to go through the next process which is modeling to get objective function which will be inserted into the moth-flame optimization algorithm. The optimization results show that the moth-flame algorithm produced a minimum surface roughness value of 2.41μ compared to the experimental data. The value of machining parameters that lead to minimum value of surface roughness are 900 rpm of spindle speed, 50 mm/min of feed rate, 65 mm of depth of hole and 40 l/hr of MQL. The ANOVA has analysed that spindle speed, feed rate and MQL are significant parameters for surface roughness value with P-value <0.0001, 0.0219 and 0.0008 while depth of hole has P-value of 0.3522 which indicates that the parameter is not significant for surface roughness value. The analysis also shown that the machining parameter that has largest contribution to the surface roughness value is spindle speed with 65.54% while the smallest contribution is from depth of hole with 0.8%. As the conclusion, the application of artificial intelligence is very helpful in the industry for gaining good quality of products.
format Article
author Kamaruzaman, Anis Farhan
Mohd. Zain, Azlan
Alwee, Razana
Md. Yusof, Noordin
Najarian, Farhad
author_facet Kamaruzaman, Anis Farhan
Mohd. Zain, Azlan
Alwee, Razana
Md. Yusof, Noordin
Najarian, Farhad
author_sort Kamaruzaman, Anis Farhan
title Optimization of surface roughness in deep hole drilling using moth-flame optimization
title_short Optimization of surface roughness in deep hole drilling using moth-flame optimization
title_full Optimization of surface roughness in deep hole drilling using moth-flame optimization
title_fullStr Optimization of surface roughness in deep hole drilling using moth-flame optimization
title_full_unstemmed Optimization of surface roughness in deep hole drilling using moth-flame optimization
title_sort optimization of surface roughness in deep hole drilling using moth-flame optimization
publisher Penerbit UTM Press
publishDate 2019
url http://eprints.utm.my/id/eprint/85298/1/AzlanMohdZain2019_OptimizationofSurfaceRoughnessinDeepHole.pdf
http://eprints.utm.my/id/eprint/85298/
https://elektrika.utm.my/index.php/ELEKTRIKA_Journal/article/view/195/
_version_ 1662754379562745856
score 13.211869

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