A Comparative Study of Artificial Neural Network and Response Surface Methodology for Optimization of Friction Welding of Incoloy 800 H

1.School of Engineering and Technology, Indira Gandhi National Open University, New Delhi 110016, India

2.Department of Production Engineering, National Institute of Technology, Tiruchirappalli 620015, Tamilnadu, India

3.School of Engineering and Technology, Indira Gandhi National Open University, New Delhi 110016, India

4.Department of Production Engineering, National Institute of Technology, Tiruchirappalli 620015, Tamilnadu, India

[1]	D. J. Kim;D. Y. Seo;J. Tsang;W. J. Yang;J. H. Lee;H. Saari;C. S. Seok .The crack growth behavior of Incoloy 800H under fatigue and dwell-fatigue conditions at elevated temperature[J].Journal of Mechanical Science and Technology,2012(7):2023-2027.
[2]	R. PAVENTHAN,P. R. LAKSHMINARAYANAN,V. BALASUBRAMANIAN.铝合金和不锈钢摩擦焊接参数的预测和优化[J].中国有色金属学报（英文版）,2011(07):1480-1485.
[3]	S.T. Selvamani;K. Palanikumar.Optimizing the friction welding parameters to attain maximum tensile strength in AISI 1035 grade carbon steel rods[J].Measurement,2014:10-21.
[4]	T. Udayakumar;K. Raja;A. Tanksale Abhijit;P. Sathiya .Experimental investigation on mechanical and metallurgical properties of super duplex stainless steel joints using friction welding process[J].Journal of manufacturing processes,2013(4):558-571.
[5]	A. K. LAKSHMINARAYANAN,V. BALASUBRAMANIAN.Comparison of RSM with ANN in predicting tensile strength of friction stir welded AA7039 aluminium alloy joints[J].中国有色金属学会会刊（英文版）,2009(01):9-18.

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