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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| Language: | en |
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2013
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| Online Access: | http://eprints.utem.edu.my/id/eprint/10672/1/Modeling_of_ANFIS_in_Predicting_TiN_Coatings_Roughness.pdf http://eprints.utem.edu.my/id/eprint/10672/ http://ieeexplore.ieee.org/ |
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| author | Jaya, A. S. M. Hashim, S.Z.M. Haron, H. Ngah, Razali Muhamad, M.R. Rahman, M. N. A. |
| author_facet | Jaya, A. S. M. Hashim, S.Z.M. Haron, H. Ngah, Razali Muhamad, M.R. Rahman, M. N. A. |
| author_sort | Jaya, A. S. M. |
| building | UTEM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknikal Malaysia Melaka |
| content_source | UTEM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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 N2 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 |
| id | my.utem.eprints-10672 |
| institution | Universiti Teknikal Malaysia Melaka |
| language | en |
| publishDate | 2013 |
| record_format | eprints |
| spelling | my.utem.eprints-106722015-05-28T04:12:42Z http://eprints.utem.edu.my/id/eprint/10672/ Modeling of ANFIS in predicting TiN coatings roughness Jaya, A. S. M. Hashim, S.Z.M. Haron, H. Ngah, Razali 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 N2 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 application/pdf en http://eprints.utem.edu.my/id/eprint/10672/1/Modeling_of_ANFIS_in_Predicting_TiN_Coatings_Roughness.pdf Jaya, A. S. M. and Hashim, S.Z.M. and Haron, H. and Ngah, Razali and Muhamad, M.R. and Rahman, M. N. A. (2013) Modeling of ANFIS in predicting TiN coatings roughness. In: 5th International Conference on Computer Science and Information Technology (CSIT), 2013 , 27-28 March 2013, Amman. http://ieeexplore.ieee.org/ |
| spellingShingle | QA75 Electronic computers. Computer science Jaya, A. S. M. Hashim, S.Z.M. Haron, H. Ngah, Razali Muhamad, M.R. Rahman, M. N. A. Modeling of ANFIS in predicting TiN coatings roughness |
| title | 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_short | Modeling of ANFIS in predicting TiN coatings roughness |
| title_sort | modeling of anfis in predicting tin coatings roughness |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utem.edu.my/id/eprint/10672/1/Modeling_of_ANFIS_in_Predicting_TiN_Coatings_Roughness.pdf http://eprints.utem.edu.my/id/eprint/10672/ http://ieeexplore.ieee.org/ |
| url_provider | http://eprints.utem.edu.my/ |