Prediction of mechanical properties of welded steel X70 pipeline using neural network modelling

An artificial neural network (ANN) model was developed to predict tensile and impact properties of a submerged arc helical welded (SAHW) pipeline steel API X70 based upon its chemical composition. Weight percent of the elements was considered as the input, while the tensile and Charpy impact properties were considered as the outputs. Scatter diagrams and two statistical parameters (absolute fraction of variance and relative error) were used to evaluate the prediction performance of the developed artificial neural network model. The predicted values were found to be in excellent agreement with the experimental data and the current model has a good learning precision and generalization (for training, validation and testing data sets). The results revealed that the developed model is very accurate and has a strong potential for capturing the interaction between the mechanical properties and chemical composition of welded high strength low alloy (HSLA) steels.