Universiti Teknologi Malaysia Institutional Repository

Application of ANFIS in predicting of TIALN coatings hardness

Mohamad Jaya, Abdul Syukor and Basari, Abd. Samad Hasan and Mohd. Hashim, Siti Zaiton and Haron, Habibollah and Muhamad, 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

Full text not available from this repository.


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.

Item Type:Article
Uncontrolled Keywords:coatings hardness
Subjects:H Social Sciences > HD Industries. Land use. Labor
Divisions:Computer Science and Information System (Formerly known)
ID Code:44744
Deposited By: Haliza Zainal
Deposited On:21 Apr 2015 11:31
Last Modified:30 Aug 2017 15:30

Repository Staff Only: item control page