Modeling of TiN coating thickness using ANFIS
In this paper, an approach in predicting thickness 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...
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| Main Authors: | , , , , |
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| Format: | Book |
| Language: | en |
| Published: |
Trans Tech Publications
2014
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/14137/1/Modeling_of_TiN_Coating_Thickness_Using_ANFIS.pdf http://eprints.utem.edu.my/id/eprint/14137/ http://www.scientific.net/AMM.575.3 |
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| Summary: | In this paper, an approach in predicting thickness 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 coating thickness 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,
three bell shapes were used as input membership function (MFs). The collected experimental data
was used to train the ANFIS model. Then, the ANFIS model was validated with confirmatory test
data and compared with other prediction models in terms of the root mean square error (RMSE),
residual error and prediction accuracy. The result indicated that the developed ANFIS model result
was the lowest RMSE7 and average residual error, besides the highest in prediction accuracy
compared to the other models. The result also indicated that the limited experimental data could be
used in training the ANFIS model and resulting accurate predictive result. |
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