Analysis of the Influence of Residual Stress on Fatigue Life of Welded Joints

Abstract

This work presents a new computational approach to estimate the fatigue life in welded joints, taking into account three-dimensional, non-proportional, and out-of-phase stresses; the thermomechanical properties of steel; parameters of the welding process; and the effects of residual stresses. We use the mechanical equations of continuous and cumulative fatigue damage found from Multiaxial RainFlow, the Wang and Brown and critical plane methods, and the Findley criteria approach. The numerical finite element method, available with software, is used to simulate the formation of residual stress and quantify its influence by solving equations. Based on the analysis of numerical results, we were able to prevision the crack location and angles of propagation of a high cycle fatigue crack with totally random loads in a metallic structure. We conclude that the result of the computational method is valid and can be used to estimate and life in fatigue in welded joints.

The influence of welding heat input on Residual Notch Stress Intensity Factor

“…Residual stresses (RSs) are recognized to influence the fatigue strength of welded joints in the high cycle fatigue (HCF) regime [1][2][3][4][5][6][7][8]. Their value is tremendously influenced by a big number of parameters such as metallurgy of welded alloys [9,10], process parameters, boundary conditions and welding technology, among the others.…”

Prediction of Residual Stress of Carburized Steel Based on Machine Learning

“…Therefore, increasing the strength of the surface of a part and increasing its residual compressive stress both play key roles in increasing its fatigue life. Residual compressive stress can suppress the initiation of surface cracks, thereby improving the fatigue life of manufactured parts [5,6]. However, because residual stress field tests are lengthy and damage the component, we instead aimed to predict the residual stress of steel using its microstructure and carbon content based on a data-driven method.…”