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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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2011
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| Online Access: | http://eprints.utem.edu.my/id/eprint/197/ http://eprints.utem.edu.my/id/eprint/197/1/Published-_Application_of_ANFIS_in_Predicting_of_TiAlN_Coatings_Hardness-inproceeding.pdf |
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| author | Mohamad Jaya, Abdul Syukor Hasan Basari, Abd Samad Mohd Hashim, Siti Zaiton Haron, Habibollah Mohammad, Muhd. Razali Abd. Rahman, Md. Nizam |
| 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 |
| building | UTeM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T19:45:53Z |
| format | Article |
| id | utem-197 |
| institution | Universiti Teknikal Malaysia Melaka |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T19:45:53Z |
| publishDate | 2011 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utem-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 |
| 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 |
| title | 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_short | Application of ANFIS in Predicting of TiAlN Coatings Hardness |
| title_sort | application of anfis in predicting of tialn coatings hardness |
| url | http://eprints.utem.edu.my/id/eprint/197/ http://eprints.utem.edu.my/id/eprint/197/1/Published-_Application_of_ANFIS_in_Predicting_of_TiAlN_Coatings_Hardness-inproceeding.pdf |