Assessing local stresses in scanned fillet weld geometry using bagged decision trees

Abstract

This study addresses the limitations of current parametric equations and artificial neural networks (ANNs) in accurately predicting the stress concentration factor (SCF) of fillet welded joints stemming from the simplification of their real weld profiles. To improve the accuracy, this study introduces bagged trees for estimating local stresses. The dataset used as the foundation for training the bagged trees is extracted from the actual weld geometry of T-shaped joints. It is created via a digitalization process involving the extraction of actual geometric parameters from the joints, which are transformed into finite element models (FEMs). These models are then employed to determine the ratio between the simulated sectional stress and the nominal stress (σsec/∆σnom) under an axial loading condition. A comprehensive comparison is carried out among existing parametric equations, ANNs, and the proposed bagged trees. The results emphasize the inadequacy of idealized geometry models in accurately determining local stresses for real weld profiles. In contrast, bagged trees are a promising method for accurately computing sectional weld stresses (σsec) within real weld geometry.