Statistical Analysis-Based Prediction Model for Fatigue Characteristics in Lap Joints Considering Weld Geometry, Including Gaps

Abstract

Automotive chassis components, constructed as lap joints and produced by gas metal arc welding (GMAW), require fatigue durability. The fatigue properties of the weld in a lap joint are largely determined by weld geometry factors. When there is no gap or a consistent gap in the lap joint, improving the geometry of the weld toe can alleviate stress concentration and enhance fatigue properties. However, due to machining tolerances, it is difficult to completely eliminate or consistently manage the gap in the joint. In the case of a lap-welded joint with an inconsistent gap, it is necessary to identify the weld geometry factors related to fatigue properties. Evaluating the fatigue behavior of materials and welded joints requires significant time and cost, meaning that research that seeks to predict fatigue properties is essential. More research is needed on predicting fatigue properties related to automotive chassis components, particularly studies on predicting the fatigue properties of lap-welded joints with gaps. This study proposed a regression model for predicting fatigue properties based on crucial weld geometry factors in lap-welded joints with gaps using statistical analysis. Welding conditions were varied in order to build various weld geometries in joints configured in a lap with gaps of 0, 0.2, 0.5, and 1.0 mm, and 87 S–N curves for the lap-welded joints were derived. As input variables, 17 weld geometry factors (7 lengths, 7 angles, and 3 area factors) were selected. The slope of the S–N curve using the Basquin model from the S–N curve and the safe fatigue strength were selected as output variables for prediction in order to develop the regression model. Multiple linear regression models, multiple non-linear regression models, and second-order polynomial regression models were proposed to predict fatigue properties. Backward elimination was applied to simplify the models and reduce overfitting. Among the three proposed regression models, the multiple non-linear regression model had a coefficient of determination greater than 0.86. In lap-welded joints with gaps, the weld geometry factors representing fatigue properties were identified through standardized regression coefficients, and four weld geometry factors related to stress concentration were proposed.

Keywords

• lap-welded joint
• GMAW
• fatigue characteristic prediction
• regression model
• joint gap
• weld geometry