Modeling of ANFIS in predicting TiN coatings roughness

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.

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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.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Manufacturing Engineering > Department of Manufacturing Process
Faculty of Information and Communication Technology > Department of Industrial Computing
Depositing User: Mr. Abdul Syukor Mohamad Jaya
Date Deposited: 20 Jan 2014 13:56
Last Modified: 28 May 2015 04:12
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