Residual stress prediction of arc welded austenitic pipes with artificial neural network ensemble using experimental data.

The prediction of weld-induced residual stress assists the structural integrity assessment of welded structures. In this study, residual stress measurements of girth welded austenitic stainless-steel pipes were used to develop two Artificial Neural Network (ANN) ensemble models to predict through-thickness residual stress profiles in Weld Centre Line (WCL). One model was developed for axial and the other for hoop residual stress prediction. The inputs of the models were the pipe radius to thickness ratio, the thickness, the heat input (weld arc electrical energy per unit run length [kJ/mm]) and the normalised through-thickness position. The hyperparameters were tuned, and the models were trained with various initial weight vectors, creating an ensemble of ANNs. The models' performance was assessed by a test set and by sensitivity studies which revealed the models' output trends. • Ensemble machine learning for small and noisy residual stress dataset • Model predictions below R6 Level 2 profiles and close to experimental measurements • Sensitivity studies revealed the influence of model inputs on residual stress [ABSTRACT FROM AUTHOR]