Neural network-based HAZ hardness prediction system for multi-layer welding extended its application to various steels

Abstract

Temper bead welding is one of effective repair welding methods in case that post weld heat treatment is not easily applied. In order to evaluate the effectiveness of temper bead welding, hardness in HAZ becomes important factor. The neural network-based hardness prediction system of HAZ in temper bead welding for A533B low-alloy steel has been constructed by the authors in the previous study. However, for HAZ hardness prediction of other steels, it is necessary to obtain hardness database for each steel which is time-cost consuming, if the same method is used. The present study has been conducted to develop the generalized hardness prediction method applicable for other steels by utilizing the hardness data-base of A533B steel assuming that the hardness in HAZ of steels after tempering have a linear relationship with LMP (Larson-Miller parameter). On using the newly proposed extended method, only a few hardness data-base for the other steels is needed to obtain. Hardness distribution in HAZ of temper bead welding for other steels was calculated by using the extended hardness prediction system. The thermal cycles used for calculation were numerically obtained by a finite element method. The experimental results have shown that the predicted hardness is in good accordance with the measured one for steels without secondary hardening. It follows that the currently proposed extended method is effective for estimating the tempering effect during temper bead welding for the steels without secondary hardening.