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WELD ROOT MAGNIFICATION FACTORS FOR SEMI-ELLIPTICAL CRACKS IN T-BUTT JOINTS

Acta Mechanica Solida Sinica 2013年第3期26卷 317-330页

作者： Zhanxun Song Yeping Xiong Jilong Xie Jing Tang Xing School of Mechanical Electronic & Control EngineeringBeijing Jiaotong University Faculty of Engineering & the Environments Fluid-Structure Interaction Research GroupUniversity of Southampton

Many researchers have focused their efforts on fatigue failures occurring on weld toes. In recent years, more and more fatigue failures occur on weld roots. Therefore, it is important to explore the behaviour of weld root fatigues. This paper investigates numerically the Magnification factors （Mk） for types of semi-elliptical cracks on the weld root of a T-butt joint. The geometry of the joint is determined by four important parameters： crack depth ratio, crack shape ratio, weld leg ratio and weld angle. A singular element approach is used to generate the corresponding finite element meshes. For each set of given four parameters of the semi-elliptical root crack, the corresponding T-butt joint is numerically simulated and its Mk at the deepest point of the weld root crack is obtained for the respective tension and shear loads. The variation range of the four parameters covers 750 cases for each load, totaling 1500 simulations are completed. The numerical results obtained are then represented by the curve to explore the effects of four parameters on the Mk. To obtain an approximate equation representing Mk as a function of the four parameters for each load, a multiple regression method is adopted and the related regression analysis is performed. The error distributions of the two approximate equations are compared with the finite element data. It is confirmed that the obtained approximate functions fit very well to the database from which they are derived. Therefore, these two equations present a valuable reference for engineering applications in T-butt joint designs.

关键词： welded root cracks singular element method stress intensity factors magnificationfactors multiple regressions

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