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Application of ANFIS in predicting of TIALN coatings hardness

In this paper, a new approach in predicting the hardness of Titanium Aluminum Nitrite (TiAlN) coatings using Adaptive Neuro-Fuzzy Inference System (ANFIS) is implemented. TiAlN coated cutting tool is widely used in machining due to its excellent properties. The TiAlN coatings were formed using Physi...

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Main Authors:	Mohamad Jaya, Abdul Syukor, Basari, Abd. Samad Hasan, Mohd. Hashim, Siti Zaiton, Haron, Habibollah, Muhamad, Muhd. Razali, Abd. Rahman, Md. Nizam
Format:	Article
Published:	INSInet Publications 2011
Subjects:	HD Industries. Land use. Labor
Online Access:	http://eprints.utm.my/id/eprint/44744/
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spelling	my.utm.447442017-08-30T07:30:11Z http://eprints.utm.my/id/eprint/44744/ Application of ANFIS in predicting of TIALN coatings hardness Mohamad Jaya, Abdul Syukor Basari, Abd. Samad Hasan Mohd. Hashim, Siti Zaiton Haron, Habibollah Muhamad, Muhd. Razali Abd. Rahman, Md. Nizam HD Industries. Land use. Labor In this paper, a new approach in predicting the hardness of Titanium Aluminum Nitrite (TiAlN) coatings using Adaptive Neuro-Fuzzy Inference System (ANFIS) is implemented. TiAlN coated cutting tool is widely used in machining due to its excellent properties. The TiAlN coatings were formed using Physical Vapor Deposition (PVD) magnetron sputtering process. The substrate sputtering power, bias voltage and temperature were selected as the input parameters and the hardness as an output of the process. A statistical design of experiment called Response Surface Methodology (RSM) was used in collecting optimized data. The ANFIS model was trained using the limited experimental data. The triangular, trapezoidal, bell and Gaussian shapes of membership functions were used for inputs as well as output. The results of ANFIS model were validated with the testing data and compared with fuzzy and nonlinear RSM hardness models in terms of the root mean square error (RMSE) and model prediction accuracy. The result indicated that the ANFIS model using 3-3-3 triangular shapes membership function obtained better result compared to the fuzzy and nonlinear RSM hardness models. The result also indicated that the ANFIS model could predict the output response in high prediction accuracy even using limited training data. INSInet Publications 2011 Article PeerReviewed Mohamad Jaya, Abdul Syukor and Basari, Abd. Samad Hasan and Mohd. Hashim, Siti Zaiton and Haron, Habibollah and Muhamad, Muhd. Razali and Abd. Rahman, Md. Nizam (2011) Application of ANFIS in predicting of TIALN coatings hardness. Australian Journal of Basic and Applied Sciences, 5 (9). pp. 1647-1657. ISSN 1991-8178
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	HD Industries. Land use. Labor
spellingShingle	HD Industries. Land use. Labor Mohamad Jaya, Abdul Syukor Basari, Abd. Samad Hasan Mohd. Hashim, Siti Zaiton Haron, Habibollah Muhamad, Muhd. Razali Abd. Rahman, Md. Nizam Application of ANFIS in predicting of TIALN coatings hardness
description	In this paper, a new approach in predicting the hardness of Titanium Aluminum Nitrite (TiAlN) coatings using Adaptive Neuro-Fuzzy Inference System (ANFIS) is implemented. TiAlN coated cutting tool is widely used in machining due to its excellent properties. The TiAlN coatings were formed using Physical Vapor Deposition (PVD) magnetron sputtering process. The substrate sputtering power, bias voltage and temperature were selected as the input parameters and the hardness as an output of the process. A statistical design of experiment called Response Surface Methodology (RSM) was used in collecting optimized data. The ANFIS model was trained using the limited experimental data. The triangular, trapezoidal, bell and Gaussian shapes of membership functions were used for inputs as well as output. The results of ANFIS model were validated with the testing data and compared with fuzzy and nonlinear RSM hardness models in terms of the root mean square error (RMSE) and model prediction accuracy. The result indicated that the ANFIS model using 3-3-3 triangular shapes membership function obtained better result compared to the fuzzy and nonlinear RSM hardness models. The result also indicated that the ANFIS model could predict the output response in high prediction accuracy even using limited training data.
format	Article
author	Mohamad Jaya, Abdul Syukor Basari, Abd. Samad Hasan Mohd. Hashim, Siti Zaiton Haron, Habibollah Muhamad, Muhd. Razali Abd. Rahman, Md. Nizam
author_facet	Mohamad Jaya, Abdul Syukor Basari, Abd. Samad Hasan Mohd. Hashim, Siti Zaiton Haron, Habibollah Muhamad, Muhd. Razali Abd. Rahman, Md. Nizam
author_sort	Mohamad Jaya, Abdul Syukor
title	Application of ANFIS in predicting of TIALN coatings hardness
title_short	Application of ANFIS in predicting of TIALN coatings hardness
title_full	Application of ANFIS in predicting of TIALN coatings hardness
title_fullStr	Application of ANFIS in predicting of TIALN coatings hardness
title_full_unstemmed	Application of ANFIS in predicting of TIALN coatings hardness
title_sort	application of anfis in predicting of tialn coatings hardness
publisher	INSInet Publications
publishDate	2011
url	http://eprints.utm.my/id/eprint/44744/
_version_	1643651532072157184
score	13.211869

Application of ANFIS in predicting of TIALN coatings hardness

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