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Computational Intelligence Approach for Predicting the Hardness Performances in Titanium Aluminium Nitride (TiA1N) Coating Process
Muhammad 'Arif Mohamad, Nor Azizah Ali, Habibollah Haron
Pages - 1 - 14     |    Revised - 20-01-2014     |    Published - 11-02-2014
Volume - 5   Issue - 1    |    Publication Date - February 2014  Table of Contents
Support Vector Machine, Artificial Neural Network, RSM-Fuzzy, Hardness, TiA1N Coatings, PVD Magnetron Sputtering.
This paper presents a computational approach on predicting of hardness performances for Titanium Aluminium Nitride (TiA1N) coating process. A new application in predicting the hardness performances of TiA1N coatings using a method called Support Vector Machine (SVM) and Artificial Neural Network (ANN) is implemented. TiAlN coatings are usually used in high-speed machining due to its excellent properties in surface hardness and wear resistance. Physical Vapor Deposition (PVD) magnetron sputtering process has been used to produce the TiA1N coatings. Based on the experimental dataset of previous work, the SVM and ANN model is used in predicting the hardness of TiA1N coatings. The influential factors of three coating process parameter namely substrate sputtering power, substrate bias voltage and substrate temperature were selected as input while the output parameter is the hardness. The results of proposed SVM and ANN models are compared to the experimental result and the hybrid RSM-Fuzzy model from previous work. The comparisons of SVM and ANN models against hybrid RSM-Fuzzy were based on predictive performances in order to obtain the most accurate model for prediction of hardness in TiA1N coating. In terms of predictive performance evaluation, four performances matrix were applied that are percentage error, mean square error (MSE), co-efficient determination (R 2) and model accuracy. The result has proved that the proposed SVM model shows the better result compared to the ANN and hybrid RSM-fuzzy model. The good performances of the results obtained by the SVM method shows that this method can be applied for prediction of hardness performances in TiA1N coating process with better predictive performances compared to ANN and hybrid RSM-Fuzzy.
CITED BY (1)  
1 Norlina, M. S., Mazidah, P., Md Sin, N. D., & Rusop, M. (2014, December). Computational intelligence approach in optimization of a nanotechnology process. In Research and Development (SCOReD), 2014 IEEE Student Conference on (pp. 1-5). IEEE.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
A. R. Md Nizam ( 2010). Modelling of Physical Vapur Deposition (PVD) Process on Cutting Tool using Response Surface Methodology (RSM). PhD: Coventry University.
A.S.M Jaya, Muhamad M.R., Rahman M.N.A, Napiah Z.A.F.M, Hashim S.Z.M, Haron H,. (2011) Hybrid RSM- fuzzy modeling for hardness prediction of TiAlN coatings.Intelligent Systems Design and Applications (ISDA) 11th International Conference, 313-318.
Burges C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 121-167.
D. C. Montgomery (2005), Design and Analysis of Experiments, 6th ed. New Jersey:John Wiley and Sons.
Gunn S.R. (1998). Support Vector Machine for Classification and Regression. Technical Report. Faculty of Engineering, Science and Mathematics School of Electronics and Computer Science. University of Southampton.
H. C. Jiang, W. L. Zhang, W. X. Zhang, and B. Peng, (2010). Effects of argon pressure on magnetic properties and low-field magneto striction of amorphousTbFe films, Physica B,. 405, 834-838.
Haykin, S., (1999) Neural Network- A Comprehensive Foundation. Prentice Hall.
Hsu, C. W., & Lin, C. J., (2002). A simple decomposition method for support vector machine. Machine Learning, 46(1–3), 219–314.
Huang,C.,L. and Wang,C.J., (2006). A GA-based feature selection and parameters optimization for support vector machines. Expert Systems with Applications 31, 231–240.
Liu, T.C., Li, R.K., Chen, M.C., (2006). Development of an artificial neural network to predict lead frame dimensions in an etching process. Int. J. Adv. Manuf. Technol. 27,1211–1216.
M. J. Anderson and P. J. Whitcomb, (2000). DOE Simplified: Practical Tools for Effective Experimentation. Portland, OR: Productivity Press.
Mohamad, M.A., Haron, H., Ali, N.A., (2012). Prediction of Hardness in Titanium Aluminium Nitride TiA1N Coating Process: A Review. Computational Intelligence,Modelling and Simulation (CIMSiM), 2012 Fourth International Conference, 111-116.
P. L. Sun, C. H. Hsu, S. H. Liu, C. Y. Su, and C. K. Lin,. (2010) Analysis on microstructure and characteristics of TiAlN/CrN nano-multilayer films deposited by cathodic arc deposition. Thin Solid Films.
Radhika and Shashi, (2009). Atmospheric temperature prediction using support vector machine. International Journal of Computer Theory and Engineering, 1(1), 1793-8201.
Singh, A.K., Panda, S.S., Pal, S.K., Chakraborty, D., (2006). Prediction drill wear using an artificial neural network. Int. J. Adv. Manuf. Technol. 28, 456–462.
T. Zhou, P. Nie, X. Cai, and P. K. Chu,. (2009) Influence of N2 partial pressure on mechanical properties of (Ti,Al)N films deposited by reactive magnetron sputtering.Vacuum, 83, 1057-1059.
Tosun, N., Ozler, L., (2002). A study of tool life in hot machining using artificial neural networks and regression analysis method. J. Mater. Process. Technol. 124, 99–104.
Tuffy K., Byrne, G., and Dowling, D. (2004). Determination of the optimum TiN coating thickness on WC inserts for machining carbon steels. Journal of Materials Processing Technology, 155 (156), 1861-1866.
Vapnik, V.( 1998.) . The support vector method of function estimation. In J. Suykens, J.Vandewalle (Eds.), Nonlinear modeling: Advanced black-ox techniques. 55–86 Dordrecht: Kluwer,
Wang et al. (2011). Prediction of Machine Tool Condition Using Support Vector Machine.Journal of Physics: Conference Series 305, 012113,doi:10.1088/1742-6596/305/1/012113.
Mr. Muhammad 'Arif Mohamad
Universiti Teknologi Malaysia - Malaysia
Dr. Nor Azizah Ali
Universiti Teknologi Malaysia - Malaysia
Professor Habibollah Haron
Universiti Teknologi Malaysia - Malaysia

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