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Particle swarm optimization algorithm to enhance the roughness of thin film in tin coatings

Nowadays, lots of disciplines require optimization to determine optimal parameters to accomplish top quality services which include parameters optimization of thin film coating. Modification of sharp tool characteristics and costs are two primary matters in the procedure of Physical Vapour Deposit...

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Main Authors: Jamil Alsayaydeh, Jamil Abedalrahim, Zainon, Maslan, Mohammad Alshannaq, Osama Saleh, Hammouda, Montaser B A, Ali, Mohanad Faeq, Alkhashaab, Mohammed Abdul Razaq, Mohamad Jaya, Abdul Syukor
Format: Article
Language:English
Published: Asian Research Publishing Network (ARPN) 2022
Online Access:http://eprints.utem.edu.my/id/eprint/26386/2/JEAS_0122_8835.PDF
http://eprints.utem.edu.my/id/eprint/26386/
http://www.arpnjournals.org/jeas/research_papers/rp_2022/jeas_0122_8835.pdf
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spelling my.utem.eprints.263862023-03-06T12:44:33Z http://eprints.utem.edu.my/id/eprint/26386/ Particle swarm optimization algorithm to enhance the roughness of thin film in tin coatings Jamil Alsayaydeh, Jamil Abedalrahim Zainon, Maslan Mohammad Alshannaq, Osama Saleh Hammouda, Montaser B A Ali, Mohanad Faeq Alkhashaab, Mohammed Abdul Razaq Mohamad Jaya, Abdul Syukor Nowadays, lots of disciplines require optimization to determine optimal parameters to accomplish top quality services which include parameters optimization of thin film coating. Modification of sharp tool characteristics and costs are two primary matters in the procedure of Physical Vapour Deposition (PVD). The purpose of this study is to figure out the optimal parameters in PVD coating process for better thin-film roughness. Three input parameters are chosen to describe the solutions over the target data, such as Nitrogen gas pressure (N2), Turntable speed (TT), and Argon gas pressure (Ar), although the surface roughness had been chosen being a result response of the Titanium nitrite (TiN). Atomic Force Microscopy (AFM) tools were applied to describe the roughness of coating layer. Within this research, a process of modelling using Response Surface Method (RSM) was applied for surface roughness of Titanium Nitrite (TiN) coating to get a best result. Particle Swarm Optimization (PSO) was applied as an optimization technique for the coating process to enhance characteristics of thin film roughness. In validation process, different experimental runs of actual data were conducted. It was found that residual error (e) is less than 10, to indicate that the model can accurately predict the surface roughness. Also, PSO could reduce the value of coating roughness at reduction of ≈ 48% to get a minimum value compared to actual data. Asian Research Publishing Network (ARPN) 2022 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/26386/2/JEAS_0122_8835.PDF Jamil Alsayaydeh, Jamil Abedalrahim and Zainon, Maslan and Mohammad Alshannaq, Osama Saleh and Hammouda, Montaser B A and Ali, Mohanad Faeq and Alkhashaab, Mohammed Abdul Razaq and Mohamad Jaya, Abdul Syukor (2022) Particle swarm optimization algorithm to enhance the roughness of thin film in tin coatings. ARPN Journal Of Engineering And Applied Sciences, 17 (2). pp. 186-193. ISSN 1819-6608 http://www.arpnjournals.org/jeas/research_papers/rp_2022/jeas_0122_8835.pdf
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description Nowadays, lots of disciplines require optimization to determine optimal parameters to accomplish top quality services which include parameters optimization of thin film coating. Modification of sharp tool characteristics and costs are two primary matters in the procedure of Physical Vapour Deposition (PVD). The purpose of this study is to figure out the optimal parameters in PVD coating process for better thin-film roughness. Three input parameters are chosen to describe the solutions over the target data, such as Nitrogen gas pressure (N2), Turntable speed (TT), and Argon gas pressure (Ar), although the surface roughness had been chosen being a result response of the Titanium nitrite (TiN). Atomic Force Microscopy (AFM) tools were applied to describe the roughness of coating layer. Within this research, a process of modelling using Response Surface Method (RSM) was applied for surface roughness of Titanium Nitrite (TiN) coating to get a best result. Particle Swarm Optimization (PSO) was applied as an optimization technique for the coating process to enhance characteristics of thin film roughness. In validation process, different experimental runs of actual data were conducted. It was found that residual error (e) is less than 10, to indicate that the model can accurately predict the surface roughness. Also, PSO could reduce the value of coating roughness at reduction of ≈ 48% to get a minimum value compared to actual data.
format Article
author Jamil Alsayaydeh, Jamil Abedalrahim
Zainon, Maslan
Mohammad Alshannaq, Osama Saleh
Hammouda, Montaser B A
Ali, Mohanad Faeq
Alkhashaab, Mohammed Abdul Razaq
Mohamad Jaya, Abdul Syukor
spellingShingle Jamil Alsayaydeh, Jamil Abedalrahim
Zainon, Maslan
Mohammad Alshannaq, Osama Saleh
Hammouda, Montaser B A
Ali, Mohanad Faeq
Alkhashaab, Mohammed Abdul Razaq
Mohamad Jaya, Abdul Syukor
Particle swarm optimization algorithm to enhance the roughness of thin film in tin coatings
author_facet Jamil Alsayaydeh, Jamil Abedalrahim
Zainon, Maslan
Mohammad Alshannaq, Osama Saleh
Hammouda, Montaser B A
Ali, Mohanad Faeq
Alkhashaab, Mohammed Abdul Razaq
Mohamad Jaya, Abdul Syukor
author_sort Jamil Alsayaydeh, Jamil Abedalrahim
title Particle swarm optimization algorithm to enhance the roughness of thin film in tin coatings
title_short Particle swarm optimization algorithm to enhance the roughness of thin film in tin coatings
title_full Particle swarm optimization algorithm to enhance the roughness of thin film in tin coatings
title_fullStr Particle swarm optimization algorithm to enhance the roughness of thin film in tin coatings
title_full_unstemmed Particle swarm optimization algorithm to enhance the roughness of thin film in tin coatings
title_sort particle swarm optimization algorithm to enhance the roughness of thin film in tin coatings
publisher Asian Research Publishing Network (ARPN)
publishDate 2022
url http://eprints.utem.edu.my/id/eprint/26386/2/JEAS_0122_8835.PDF
http://eprints.utem.edu.my/id/eprint/26386/
http://www.arpnjournals.org/jeas/research_papers/rp_2022/jeas_0122_8835.pdf
_version_ 1759693068881100800
score 13.211869

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