Optimization of Ferrite Number of Solution Annealed Duplex Stainless Steel Claddings Using Integrated Artificial Neural Network – Genetic Algorithm
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Abstract
Cladding is the most economical process used on the surface of low carbon structural steel to improve the corrosion resistance. The corrosion resistant property is based on the amount of ferrite present in the clad layer. Generally, the ferrite content present in the layer is expressed in terms of ferrite number (FN). The optimum range of ferrite number plays an instrumental role to bring adequate surface properties like chloride stress corrosion cracking resistance, pitting and crevice corrosion resistance and mechanical properties. For achieving maximum economy and enhanced life, duplex stainless steel (E2209T1-4/1) is deposited on the surface of low carbon structural steel of IS: 2062. The problem faced in the weld cladding towards achieving the required amount of ferrite number is the selection of an optimum combination of input process parameters (welding current, welding speed, contact tip to specimen distance and gun angle). This paper mainly concentrates on estimating FN and analysis of input process parameters on FN of heat treated duplex stainless steel cladding. To predict FN, mathematical equations was developed based on four factor five level central composite rotatable design with full replication using regression methods. In this paper, artificial neural network (ANN) and genetic algorithm (GA) techniques were integrated and labeled as ANN-GA to identify the optimum process parameters in FCAW to get maximum FN. From the results, the integrated ANN-GA (I OR II) is capable of giving maximum FN at optimum process parameters compared to that of experimental, regression and ANN modeling
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P. K. Palani and N. Murugan, “Prediction of delta ferrite content
and effect of welding process parameters in cladding by FCAW”. Materials and manufacturing processes, vol 21, n. 5, pp. 431-438, 2006.
N. Murugan, R.S. Parmer, “Effects of MIG process parameters on the geometry of the bead in the automatic surfacing of stainless steel”. Journal Mater. Process. Tech, 41, 381-398, 1994.
D. J. K Kotecki, “Ferrite determination in stainless steel – Advances since 1974,” Welding Journal, 76(1): 24s-36s, 1997.
T. Kannan and N. Murugan, “Prediction of ferrite number of duplex stainless steel clad metals using RSM.” Welding Journal, 91s – 100s, (2006).
W. G. Cochran and G. M. Cox, “Experimental design,” (John Wiley & sons, 1987).
L. Karlsson, Esab AB, Goteborg (Sweden), “Intermetallic phase precipitation in duplex stainless steels and weld metals metallurgy, influence on properties and welding aspects,” Welding in the world, 43 n° 5, 20-41, 1999.
L. Karlsson , L. Ryen and S. Pak, “Precipitation of intermetallic phase in 22% Cr duplex stainless steel weld metals,” Welding Journal, 28s – 40s, 1995.
J.K.L Lai, K.W Wong, D.J Li, “Effect of solution treatment on the transformation behavior of cold-rolled duplex stainless steels,” Material Science and Engineering, 203, 356 – 364, 1995.
R. Badji, B. Maza, M. Bouabdallah, B. Bacroix and C. Kahloun, “Effect of post weld heat treatment on microstructure and mechanical properties of welded 2205 duplex stainless steel,” material science forum, 467, 217-222, 2004.
Riad Badji, Mabrouk Bouabdallah, Brigitte Bacroix, Charlie Kahloun, Brahim Belkessa, Halim Maza, “Phase transformation and mechanical behavior in annealed 2205 duplex stainless steel welds,” Materials Characterization, 59, 447 – 453, 2008.
Heejoon Hwang and Yongsoo Park, “Effects of heat treatment on the phase ratio and corrosion resistance of duplex stainless steel,” Material Transactions, 50, 1548 – 1552, 2009.
D.J Kotecki, “Heat treatment of Duplex stainless steel weld metals,” Welding Journal, 431s - 440s, 1989.
D.C Montgomery, “Design and analysis of experiments,” (John Wiley & Sons, Pte Ltd, 2003).
H. Ates, “Prediction of gas metal arc welding parameters based on artificial neural networks,” Materials and Design, vol. 28, no. 7, pp. 2015 – 2023, 2007.
D.S Nagesh, G.L Datta, “Prediction of weld bead geometry and penetration in shielded metal arc welding using artificial neural network,” Journal of Materials Processing Technology,123, 303 – 312 (2002).
Dongeheol Kim, Sehun Rhee and Hyunsung Parks, "Modelling and optimization of a GMAW welding process by Genetic Algorithm and response surface methodology. International Journal of Production Research, 40, 1699-1711, 2002.
Ping Li, M.T.C Fang, J. Lucas, “Modelling of submerged arc weld beads using self – adaptive offset neural networks,” Journal of Materials Processing Technology, 71, 288-298, 1997.
D.S Nagesh, G.L Datta, “Genetic algorithm for optimization of welding variables for height to width ratio and application of ANN for prediction of bead geometry for TIG welding process,” Applied soft computing, 10, 897-907, 2010.
P.K Palani, N. Murugan, “Optimization of weld bead geometry for stainless steel cladding deposited by FCAW,” Journal of Materials Processing Technology, 190, 291-299, 2007.
I.S Kim, , J.S Son, C.E Park, C.W Lee, Yarlagadda K.D.V.Prasad, “A study on Prediction of weld bead Height in robotic arc welding using a neural network,” Journal of Materials Processing Technology, 130, 229-234 2002.
Parikshit Dutta, Dilip Kumar Pratihar, “Modeling of TIG welding process using conventional regression analysis and neural network – based approaches,” Journal of materials processing Technology, 184, 56-68, 2007.
J. Yoganandh, T. Kannan, S.P Kumaresh Babu and S. Natarajan, Optimization of GMAW process parameters in Austenic stainless steel cladding using Genetic algorithm based computational models. Experimental Techniques, 1- 11 2012.
T. Kannan and Dr. N. Murugan, “Optimization of FCAW process parameters in duplex stainless steel weld cladding. Manufacturing Today, 4, 2005.
Dr.N. Murugan, Member, P.K. Palani, Non – member, “Optimization of bead Geometry in automatic stainless steel cladding by MIG welding using a Genetic Algorithm,” IE (I) Journal – PR 84, 49 – 54 2004.
Vidyut Dey, Dilip Kumar Pratihar, G.L. Dutta, M.N. Jha, T.K. Saha, A.V. Bapat, “Optimization of bead geometry in electron beam welding using a Genetic algorithm. Journal of Materials Processing Technology, 209, 1151-1157 2009.
J.M. Vitek, Y.S. Iskander and E.M. Oblow, “Improved Ferrite Number prediction in stainless steel arc welds using Artificial neural network – part 2: Neural network results,” Welding Research Supplement, 41s-49 2000.
M. Vasudevan, A.K. Bhaduri, Baldev Raj, K. Prasad Rao, “Delta ferrite prediction in stainless steel welds using neural network analysis and comparison with other prediction methods,” Journal of Materials Processing Technology, 142, 20-28 2003.
Golberg, D.E., Genetic Algorithms in Search, Optimization, and Machine Learning, 1989.
Azlan Mohd Zain, Habibollah Haron,Safian Sharif, “Estimation of the minimum Machining performance in the abrasive waterjet machining using integrated ANN-SA,” Expert System with applications, 38, 8316-8326, 2011.
Setareh, M., Isvandzibaei, M.R., Free vibration functionally graded material circular cylindrical shell with various volume fraction laws under symmetrical boundary conditions, (2011) International Review on Modelling and Simulations (IREMOS), 4 (4), pp. 1876-1882.
Moarrefzadeh, A., Alipour, R., Numerical simulation of temperature field by plasma arc welding in stainless steel process, (2010) International Review on Modelling and Simulations (IREMOS), 3 (1), pp. 101-107.
Rani, H.P., Divya, T., Sahaya, R.R., Kain, V., Barua, D.K., Exploration of wall thinning degradation mechanism in double elbow pipe, (2013) International Review on Modelling and Simulations (IREMOS), 6 (1), pp. 224-234.
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