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 provides 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 selection of optimum combination of input process parameters. This study concentrates on estimating FN and analysis of input process parameters on FN of heat treated duplex stainless steel cladding. To predict FN, mathematical equations were developed based on four factor five level central composite rotatable design with full replication using regression methods. Then, the developed models were embedded further into integrated ANN-SA to estimate FN. From the results, the integrated ANN-SA is capable of giving maximum FN at optimum process parameters compared to that of experimental, regression and ANN modeling.

Keywords:

Artificial neural network, duplex stainless steel, ferrite number, flux cored arc welding, simulated annealing, solution annealing heat treatment,

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