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Modeling of anfis in predicting tin coatings roughness

In this paper, an approach in predicting surface roughness 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....

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Main Authors:	Jaya, A. S. M., Mohd Hashim, Siti Zaiton, Haron, H., Ngah, R., Muhamad, M.R., Rahman, M. N. A.
Format:	Conference or Workshop Item
Published:	2013
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://eprints.utm.my/id/eprint/51173/ http://apps.webofknowledge.com.ezproxy.utm.my/full_record.do?product=WOS&search_mode=GeneralSearch&qid=5&SID=R2Cjh3fA6kIeWhVr585&page=1&doc=1
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spelling	my.utm.511732017-07-18T07:07:23Z http://eprints.utm.my/id/eprint/51173/ Modeling of anfis in predicting tin coatings roughness Jaya, A. S. M. Mohd Hashim, Siti Zaiton Haron, H. Ngah, R. Muhamad, M.R. Rahman, M. N. A. QA75 Electronic computers. Computer science In this paper, an approach in predicting surface roughness 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 N-2 pressure, argon pressure and turntable speed were selected as the input parameters and the surface roughness 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, triangular, trapezoidal, bell and Gaussian shapes were used for as input membership function (MFs). The collected experimental data was used to train the ANFIS model. Then, the ANFIS model were validated with the actual testing data and compared with regression model in terms of the residual error and model accuracy. The result indicated that the ANFIS model using three bell shapes MFs obtained better result compared to the polynomial regression model. The number of MFs showed significant influence to the ANFIS model performance. The result also indicated that the limited experimental data could be used in training the ANFIS model and resulting accurate predictive result. 2013 Conference or Workshop Item PeerReviewed Jaya, A. S. M. and Mohd Hashim, Siti Zaiton and Haron, H. and Ngah, R. and Muhamad, M.R. and Rahman, M. N. A. (2013) Modeling of anfis in predicting tin coatings roughness. In: 2013 5TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND INFORMATION TECHNOLOGY (CSIT), MAR 27-28, 2013, Amman, Jordon. http://apps.webofknowledge.com.ezproxy.utm.my/full_record.do?product=WOS&search_mode=GeneralSearch&qid=5&SID=R2Cjh3fA6kIeWhVr585&page=1&doc=1
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/
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Jaya, A. S. M. Mohd Hashim, Siti Zaiton Haron, H. Ngah, R. Muhamad, M.R. Rahman, M. N. A. Modeling of anfis in predicting tin coatings roughness
description	In this paper, an approach in predicting surface roughness 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 N-2 pressure, argon pressure and turntable speed were selected as the input parameters and the surface roughness 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, triangular, trapezoidal, bell and Gaussian shapes were used for as input membership function (MFs). The collected experimental data was used to train the ANFIS model. Then, the ANFIS model were validated with the actual testing data and compared with regression model in terms of the residual error and model accuracy. The result indicated that the ANFIS model using three bell shapes MFs obtained better result compared to the polynomial regression model. The number of MFs showed significant influence to the ANFIS model performance. The result also indicated that the limited experimental data could be used in training the ANFIS model and resulting accurate predictive result.
format	Conference or Workshop Item
author	Jaya, A. S. M. Mohd Hashim, Siti Zaiton Haron, H. Ngah, R. Muhamad, M.R. Rahman, M. N. A.
author_facet	Jaya, A. S. M. Mohd Hashim, Siti Zaiton Haron, H. Ngah, R. Muhamad, M.R. Rahman, M. N. A.
author_sort	Jaya, A. S. M.
title	Modeling of anfis in predicting tin coatings roughness
title_short	Modeling of anfis in predicting tin coatings roughness
title_full	Modeling of anfis in predicting tin coatings roughness
title_fullStr	Modeling of anfis in predicting tin coatings roughness
title_full_unstemmed	Modeling of anfis in predicting tin coatings roughness
title_sort	modeling of anfis in predicting tin coatings roughness
publishDate	2013
url	http://eprints.utm.my/id/eprint/51173/ http://apps.webofknowledge.com.ezproxy.utm.my/full_record.do?product=WOS&search_mode=GeneralSearch&qid=5&SID=R2Cjh3fA6kIeWhVr585&page=1&doc=1
_version_	1643652961239302144
score	13.211869

Modeling of anfis in predicting tin coatings roughness

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