New model for prediction of ferrite number of stainless steel welds

Babu, S. S.; Vitek, J.M.; Iskander, Y.S.; David, S. A.

doi:10.1179/136217197799481122

Cited by 4 publications

(1 citation statement)

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“…21 In alloy 2205, Si and Nb were also found to stabilise austenite. The observation that Si is an austenite stabiliser is in agreement with that reported by Suresh Babu et al 23 In the present BNN model, in which average Nb concentration of 0?03% over the entire dataset is assumed, shows that Nb acts as an austenite stabiliser. However, one would expect Nb to tie up with C and N and by reducing their content in austenite it should be a ferrite stabiliser.…”

Section: Composition Dependence Of Fn Value In Duplex Stainless Steelsupporting

confidence: 93%

Modelling correlation between alloy composition and ferrite number in duplex stainless steel welds using artificial neural networks

Vasudevan¹,

Bhaduri²,

Raj³

et al. 2005

Materials Science and Technology

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Weld metal composition is thought to be an important factor in influencing the austenite/ferrite ratio of duplex stainless steel microstructures. To produce the required balance in the austenite/ ferrite ratio in the weld microstructure, the chemical composition of the welding consumables should be adjusted. In the present work, Bayesian neural network analysis has been employed to predict the ferrite number in duplex stainless steel welds as a function of composition. The technique accounts for modelling uncertainty, and automatically quantifies the significance of each input variable. In this paper, the influence of variations in the weld composition on the ferrite number have been quantified for two duplex stainless steels. Predictions are accurate compared to published methods. The role of Si and Ti in influencing the ferrite number in these alloys has been brought out clearly in this study while these elements are not given due considerations in the WRC-1992 diagram.

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Section: Composition Dependence Of Fn Value In Duplex Stainless Steelsupporting

confidence: 93%