Surrogate model development using simulation data to predict weld residual stress: A case study based on the NeT-TG1 benchmark

Surrogate models have been used as a substitute for traditional engineering simulation to get target simulation results faster. These surrogate models usually need sufficient data to fulfill their optimum performance. However, the acquisition of high quality datasets for cases like welding residual stress is both difficult and costly. This work developed a workflow that can rapidly develop machine learning based surrogate models to predict post-weld residual stresses using finite element (FE) simulation data. The workflow comprises five steps: (i) baseline FE model creation, (ii) FE model processing (iii) scripting (iv) data generation and (v) surrogate model development. The workflow presented in the paper automates all the steps involved in generating, calculating, and extracting results from FE simulations, with the aim of reducing the time and cost required for data acquisition. In addition, to minimize the possibility of FE analyses not completing successfully during the automated data generation process and to ensure that the training data can be obtained without any manual intervention, the FE model was simplified without significant loss of accuracy. A single weld bead-on-plate structure, taken from the NeT project Task Group 1, was used as a case study to validate the developed workflow. The overall aim of this work was to validate the feasibility of developing a surrogate model for specific welding conditions with no experimental dataset and with the aid of simulations, and to investigate the predictive performance of an Artificial Neural Network based surrogate model. Four welding parameters, arc advance speed, net energy input rate, arc travel length and position, were considered as the input for the surrogate model to predict the longitudinal stress along the line running across the sample thickness in the mid-length of the specimen. The relative root mean square error of the predictive surrogate model for the test dataset was 0.0024. Residual stress prediction based on the NeT-TG1 benchmark welding case met the R6 validation criteria and the developed model generates predictions that are in line with other methods used by contributors to the NeT-TG1 round robin.