Application of ANFIS in predicting TiAlN coatings flank wear
In this paper, a new approach in predicting the flank wear of Titanium Aluminum Nitrite (TiAlN) coatings using Adaptive Network Based Fuzzy Inference System (ANFIS) is implemented. TiAlN coated cutting tool is widely used in machining due to its excellent resistance to wear. The TiAlN coatings...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2011
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/143/ http://eprints.utem.edu.my/id/eprint/143/1/CIMSim11paperprosiding.pdf |
| _version_ | 1848886889980887040 |
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| author | Hasan Basari, Abd Samad Mohamad Jaya, Abdul Syukor Mohd Hashim, Siti Zaiton Muhamad, Mohd Razali Md Nizam, Abd Rahman Habibollah, Haron |
| author_facet | Hasan Basari, Abd Samad Mohamad Jaya, Abdul Syukor Mohd Hashim, Siti Zaiton Muhamad, Mohd Razali Md Nizam, Abd Rahman Habibollah, Haron |
| author_sort | Hasan Basari, Abd Samad |
| building | UTeM Institutional Repository |
| collection | Online Access |
| description | In this paper, a new approach in predicting the
flank wear of Titanium Aluminum Nitrite (TiAlN) coatings
using Adaptive Network Based Fuzzy Inference System
(ANFIS) is implemented. TiAlN coated cutting tool is widely
used in machining due to its excellent resistance to wear. 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 flank wear 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 rule-based and RSM flank wear models in terms of the root mean square error (RMSE), coefficient determination (R2) and model accuracy (A). The result indicated that the ANFIS model using three bell shapes membership function obtained better result compared to the fuzzy and RSM flank wear 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 | 2025-11-15T19:45:40Z |
| format | Conference or Workshop Item |
| id | utem-143 |
| institution | Universiti Teknikal Malaysia Melaka |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T19:45:40Z |
| publishDate | 2011 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utem-1432023-06-01T15:38:19Z http://eprints.utem.edu.my/id/eprint/143/ Application of ANFIS in predicting TiAlN coatings flank wear Hasan Basari, Abd Samad Mohamad Jaya, Abdul Syukor Mohd Hashim, Siti Zaiton Muhamad, Mohd Razali Md Nizam, Abd Rahman Habibollah, Haron TS Manufactures QA75 Electronic computers. Computer science In this paper, a new approach in predicting the flank wear of Titanium Aluminum Nitrite (TiAlN) coatings using Adaptive Network Based Fuzzy Inference System (ANFIS) is implemented. TiAlN coated cutting tool is widely used in machining due to its excellent resistance to wear. 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 flank wear 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 rule-based and RSM flank wear models in terms of the root mean square error (RMSE), coefficient determination (R2) and model accuracy (A). The result indicated that the ANFIS model using three bell shapes membership function obtained better result compared to the fuzzy and RSM flank wear models. The result also indicated that the ANFIS model could predict the output response in high prediction accuracy even using limited training data. 2011-09-20 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utem.edu.my/id/eprint/143/1/CIMSim11paperprosiding.pdf Hasan Basari, Abd Samad and Mohamad Jaya, Abdul Syukor and Mohd Hashim, Siti Zaiton and Muhamad, Mohd Razali and Md Nizam, Abd Rahman and Habibollah, Haron (2011) Application of ANFIS in predicting TiAlN coatings flank wear. In: UNSPECIFIED. |
| spellingShingle | TS Manufactures QA75 Electronic computers. Computer science Hasan Basari, Abd Samad Mohamad Jaya, Abdul Syukor Mohd Hashim, Siti Zaiton Muhamad, Mohd Razali Md Nizam, Abd Rahman Habibollah, Haron Application of ANFIS in predicting TiAlN coatings flank wear |
| title | Application of ANFIS in predicting TiAlN coatings flank wear |
| title_full | Application of ANFIS in predicting TiAlN coatings flank wear |
| title_fullStr | Application of ANFIS in predicting TiAlN coatings flank wear |
| title_full_unstemmed | Application of ANFIS in predicting TiAlN coatings flank wear |
| title_short | Application of ANFIS in predicting TiAlN coatings flank wear |
| title_sort | application of anfis in predicting tialn coatings flank wear |
| topic | TS Manufactures QA75 Electronic computers. Computer science |
| url | http://eprints.utem.edu.my/id/eprint/143/ http://eprints.utem.edu.my/id/eprint/143/1/CIMSim11paperprosiding.pdf |