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Predictive Modeling of TiN Coating Roughness

In this paper, an approach in modeling surface roughness of Titanium Nitrite (TiN) coating using Response Surface Method (RSM) is implemented. The TiN coatings were formed using Physical Vapor Deposition (PVD) sputtering process. N2 pressure, Argon pressure and turntable speed were selected as pro...

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Main Authors: Mohamad Jaya, Abdul Syukor, Mohd Hashim, Siti Zaiton, Haron, Habibollah, Muhamad, Mohd Razali, Abd. Rahman, Md. Nizam, Hasan Basari, Abd Samad
Format: Article
Language:English
Published: Trans Tech Publication, Switzerland 2013
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://eprints.utem.edu.my/id/eprint/10670/1/Predictive_Modeling_of_TiN_Coating_Roughness.pdf
http://eprints.utem.edu.my/id/eprint/10670/
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spelling my.utem.eprints.106702015-05-28T04:12:42Z http://eprints.utem.edu.my/id/eprint/10670/ Predictive Modeling of TiN Coating Roughness Mohamad Jaya, Abdul Syukor Mohd Hashim, Siti Zaiton Haron, Habibollah Muhamad, Mohd Razali Abd. Rahman, Md. Nizam Hasan Basari, Abd Samad TJ Mechanical engineering and machinery In this paper, an approach in modeling surface roughness of Titanium Nitrite (TiN) coating using Response Surface Method (RSM) is implemented. The TiN coatings were formed using Physical Vapor Deposition (PVD) sputtering process. N2 pressure, Argon pressure and turntable speed were selected as process variables. Coating surface roughness as an important coating characteristic was characterized using Atomic Force Microscopy (AFM) equipment. Analysis of variance (ANOVA) is used to determine the significant factors influencing resultant TiN coating roughness. Based on that, a quadratic polynomial model equation represented the process variables and coating roughness was developed. The result indicated that the actual coating roughness of validation runs data fell within the 90% prediction interval (PI) and the residual errors were very low. The findings from this study suggested that Argon pressure, quadratic term of N2 pressure, quadratic term of turntable speed, interaction between N2 pressure and turntable speed, and interaction between Argon pressure and turntable speed influenced the TiN coating surface roughness. Trans Tech Publication, Switzerland 2013 Article PeerReviewed application/pdf en http://eprints.utem.edu.my/id/eprint/10670/1/Predictive_Modeling_of_TiN_Coating_Roughness.pdf Mohamad Jaya, Abdul Syukor and Mohd Hashim, Siti Zaiton and Haron, Habibollah and Muhamad, Mohd Razali and Abd. Rahman, Md. Nizam and Hasan Basari, Abd Samad (2013) Predictive Modeling of TiN Coating Roughness. Advanced Materials Research, 626 (2013). pp. 219-223. ISSN 1662-8985 http://www.ttp.net 10.4028/www.scientific.net/AMR.626.219
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
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Mohamad Jaya, Abdul Syukor
Mohd Hashim, Siti Zaiton
Haron, Habibollah
Muhamad, Mohd Razali
Abd. Rahman, Md. Nizam
Hasan Basari, Abd Samad
Predictive Modeling of TiN Coating Roughness
description In this paper, an approach in modeling surface roughness of Titanium Nitrite (TiN) coating using Response Surface Method (RSM) is implemented. The TiN coatings were formed using Physical Vapor Deposition (PVD) sputtering process. N2 pressure, Argon pressure and turntable speed were selected as process variables. Coating surface roughness as an important coating characteristic was characterized using Atomic Force Microscopy (AFM) equipment. Analysis of variance (ANOVA) is used to determine the significant factors influencing resultant TiN coating roughness. Based on that, a quadratic polynomial model equation represented the process variables and coating roughness was developed. The result indicated that the actual coating roughness of validation runs data fell within the 90% prediction interval (PI) and the residual errors were very low. The findings from this study suggested that Argon pressure, quadratic term of N2 pressure, quadratic term of turntable speed, interaction between N2 pressure and turntable speed, and interaction between Argon pressure and turntable speed influenced the TiN coating surface roughness.
format Article
author Mohamad Jaya, Abdul Syukor
Mohd Hashim, Siti Zaiton
Haron, Habibollah
Muhamad, Mohd Razali
Abd. Rahman, Md. Nizam
Hasan Basari, Abd Samad
author_facet Mohamad Jaya, Abdul Syukor
Mohd Hashim, Siti Zaiton
Haron, Habibollah
Muhamad, Mohd Razali
Abd. Rahman, Md. Nizam
Hasan Basari, Abd Samad
author_sort Mohamad Jaya, Abdul Syukor
title Predictive Modeling of TiN Coating Roughness
title_short Predictive Modeling of TiN Coating Roughness
title_full Predictive Modeling of TiN Coating Roughness
title_fullStr Predictive Modeling of TiN Coating Roughness
title_full_unstemmed Predictive Modeling of TiN Coating Roughness
title_sort predictive modeling of tin coating roughness
publisher Trans Tech Publication, Switzerland
publishDate 2013
url http://eprints.utem.edu.my/id/eprint/10670/1/Predictive_Modeling_of_TiN_Coating_Roughness.pdf
http://eprints.utem.edu.my/id/eprint/10670/
http://www.ttp.net
_version_ 1665905430803513344
score 13.211869

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