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

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

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Optimization, modelling, PVD, PSO, roughness, RSM, TiN coating.
Divisions:	Faculty of Electrical and Electronic Engineering Technology
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	06 Mar 2023 12:44
Last Modified:	06 Mar 2023 12:44
URI:	http://eprints.utem.edu.my/id/eprint/26386
Statistic Details:	View Download Statistic

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