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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 Phys...

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Main Authors: Mohamad Jaya, Abdul Syukor, Hasan Basari, Abd Samad, Mohd Hashim, Siti Zaiton, Haron, Habibollah, Mohammad, Muhd. Razali, Abd. Rahman, Md. Nizam
Format: Article
Language:English
Published: 2011
Online Access:http://eprints.utem.edu.my/id/eprint/197/1/Published-_Application_of_ANFIS_in_Predicting_of_TiAlN_Coatings_Hardness-inproceeding.pdf
http://eprints.utem.edu.my/id/eprint/197/
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spelling my.utem.eprints.1972023-05-31T12:55:44Z http://eprints.utem.edu.my/id/eprint/197/ Application of ANFIS in Predicting of TiAlN Coatings Hardness Mohamad Jaya, Abdul Syukor Hasan Basari, Abd Samad Mohd Hashim, Siti Zaiton Haron, Habibollah Mohammad, Muhd. Razali Abd. Rahman, Md. Nizam 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. 2011 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/197/1/Published-_Application_of_ANFIS_in_Predicting_of_TiAlN_Coatings_Hardness-inproceeding.pdf Mohamad Jaya, Abdul Syukor and Hasan Basari, Abd Samad and Mohd Hashim, Siti Zaiton and Haron, Habibollah and Mohammad, 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 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
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
Hasan Basari, Abd Samad
Mohd Hashim, Siti Zaiton
Haron, Habibollah
Mohammad, Muhd. Razali
Abd. Rahman, Md. Nizam
spellingShingle Mohamad Jaya, Abdul Syukor
Hasan Basari, Abd Samad
Mohd Hashim, Siti Zaiton
Haron, Habibollah
Mohammad, Muhd. Razali
Abd. Rahman, Md. Nizam
Application of ANFIS in Predicting of TiAlN Coatings Hardness
author_facet Mohamad Jaya, Abdul Syukor
Hasan Basari, Abd Samad
Mohd Hashim, Siti Zaiton
Haron, Habibollah
Mohammad, 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
publishDate 2011
url http://eprints.utem.edu.my/id/eprint/197/1/Published-_Application_of_ANFIS_in_Predicting_of_TiAlN_Coatings_Hardness-inproceeding.pdf
http://eprints.utem.edu.my/id/eprint/197/
_version_ 1768012317697507328
score 13.211869

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