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Modeling of TiN coating thickness using ANFIS

In this paper, an approach in predicting thickness of Titanium Aluminum Nitrite (TiN) coatings using Adaptive Network Based Fuzzy Inference System (ANFIS) is implemented. The TiN coatings were coated on tungsten carbide (WC) using Physical Vapor Deposition (PVD) magnetron sputtering process. The...

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Main Authors: Abdul Syukor, Mohamad Jaya, Abd. Samad, Hasan Basari, Sazalinsyah, Razali, Muhd Razali, Muhamad, Md. Nizam, Ab. Rahman
Format: Book
Language:English
Published: Trans Tech Publications 2014
Subjects:
TS Manufactures
Online Access:http://eprints.utem.edu.my/id/eprint/14137/1/Modeling_of_TiN_Coating_Thickness_Using_ANFIS.pdf
http://eprints.utem.edu.my/id/eprint/14137/
http://www.scientific.net/AMM.575.3
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spelling my.utem.eprints.141372015-05-28T04:36:27Z http://eprints.utem.edu.my/id/eprint/14137/ Modeling of TiN coating thickness using ANFIS Abdul Syukor, Mohamad Jaya Abd. Samad, Hasan Basari Sazalinsyah, Razali Muhd Razali, Muhamad Md. Nizam, Ab. Rahman TS Manufactures In this paper, an approach in predicting thickness of Titanium Aluminum Nitrite (TiN) coatings using Adaptive Network Based Fuzzy Inference System (ANFIS) is implemented. The TiN coatings were coated on tungsten carbide (WC) using Physical Vapor Deposition (PVD) magnetron sputtering process. The N2 pressure, argon pressure and turntable speed were selected as the input parameters and the coating thickness as an output of the process. Response Surface Methodology (RSM) was used to design the matrix in collecting the experimental data. In the ANFIS structure, three bell shapes were used as input membership function (MFs). The collected experimental data was used to train the ANFIS model. Then, the ANFIS model was validated with confirmatory test data and compared with other prediction models in terms of the root mean square error (RMSE), residual error and prediction accuracy. The result indicated that the developed ANFIS model result was the lowest RMSE7 and average residual error, besides the highest in prediction accuracy compared to the other models. The result also indicated that the limited experimental data could be used in training the ANFIS model and resulting accurate predictive result. Trans Tech Publications 2014-04-24 Book PeerReviewed application/pdf en http://eprints.utem.edu.my/id/eprint/14137/1/Modeling_of_TiN_Coating_Thickness_Using_ANFIS.pdf Abdul Syukor, Mohamad Jaya and Abd. Samad, Hasan Basari and Sazalinsyah, Razali and Muhd Razali, Muhamad and Md. Nizam, Ab. Rahman (2014) Modeling of TiN coating thickness using ANFIS. Trans Tech Publications, Switzerland. http://www.scientific.net/AMM.575.3
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 TS Manufactures
spellingShingle TS Manufactures
Abdul Syukor, Mohamad Jaya
Abd. Samad, Hasan Basari
Sazalinsyah, Razali
Muhd Razali, Muhamad
Md. Nizam, Ab. Rahman
Modeling of TiN coating thickness using ANFIS
description In this paper, an approach in predicting thickness of Titanium Aluminum Nitrite (TiN) coatings using Adaptive Network Based Fuzzy Inference System (ANFIS) is implemented. The TiN coatings were coated on tungsten carbide (WC) using Physical Vapor Deposition (PVD) magnetron sputtering process. The N2 pressure, argon pressure and turntable speed were selected as the input parameters and the coating thickness as an output of the process. Response Surface Methodology (RSM) was used to design the matrix in collecting the experimental data. In the ANFIS structure, three bell shapes were used as input membership function (MFs). The collected experimental data was used to train the ANFIS model. Then, the ANFIS model was validated with confirmatory test data and compared with other prediction models in terms of the root mean square error (RMSE), residual error and prediction accuracy. The result indicated that the developed ANFIS model result was the lowest RMSE7 and average residual error, besides the highest in prediction accuracy compared to the other models. The result also indicated that the limited experimental data could be used in training the ANFIS model and resulting accurate predictive result.
format Book
author Abdul Syukor, Mohamad Jaya
Abd. Samad, Hasan Basari
Sazalinsyah, Razali
Muhd Razali, Muhamad
Md. Nizam, Ab. Rahman
author_facet Abdul Syukor, Mohamad Jaya
Abd. Samad, Hasan Basari
Sazalinsyah, Razali
Muhd Razali, Muhamad
Md. Nizam, Ab. Rahman
author_sort Abdul Syukor, Mohamad Jaya
title Modeling of TiN coating thickness using ANFIS
title_short Modeling of TiN coating thickness using ANFIS
title_full Modeling of TiN coating thickness using ANFIS
title_fullStr Modeling of TiN coating thickness using ANFIS
title_full_unstemmed Modeling of TiN coating thickness using ANFIS
title_sort modeling of tin coating thickness using anfis
publisher Trans Tech Publications
publishDate 2014
url http://eprints.utem.edu.my/id/eprint/14137/1/Modeling_of_TiN_Coating_Thickness_Using_ANFIS.pdf
http://eprints.utem.edu.my/id/eprint/14137/
http://www.scientific.net/AMM.575.3
_version_ 1665905579114102784
score 13.211869

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