Data for Thermo-Mechanical Analysis of Friction Stir Welding

This study explores the development and application of machine learning (ML) metamodels for the thermo-mechanical analysis of Friction Stir Welding (FSW). The main objective is to address the challenge of accurately predicting the thermo-mechanical behaviour of materials in FSW processes. Using finite element models, a high-fidelity dataset consisting of 20 Hammersley design datapoints is generated which is then used to develop a low-fidelity dataset of 420 datapoints using KNN imputation.