Prediction of ferrite number in stainless steel welds using Bayesian neural network model

Abstract

Bayesian neural network analysis has been used to develop an accurate model for predicting the ferrite content in stainless steel welds. The analysis reveals the influence of compositional variations on ferrite content for the stainless steel weld metals, and examines the significance of individual elements on ferrite content in stainless steel welds based on the optimised neural network model. This neural network model for ferrite prediction in stainless steel welds has been developed using the database used for generating the WRC-1992 diagram and our laboratory data. The optimised committee model predicts the ferrite number (FN) in stainless steel welds with better accuracy than the constitution diagrams and the other FN prediction methods. Using this generalised Bayesian Neural Network (BNN) model, the influence of variations of the individual elements on the FN in austenitic and duplex stainless steel welds has been determined. It is found that the change in FN is a non-linear function of the variation in the concentration of the elements, with elements such as chromium, nickel, nitrogen, molybdenum, silicon, titanium and vanadium are found to influence the FN more significantly than the rest of the elements in stainless steel welds. Manganese is found to have less influence on the FN. While titanium influences the FN more significantly than niobium, the WRC-1992 diagram considers only the niobium for calculating chromium equivalent. The role of silicon and titanium in influencing the FN in stainless steel welds has been brought out clearly, while these elements are not given due considerations in the WRC-1992 diagram.