Effect of the parameters of weld toe geometry on the FAT class as obtained by means of fracture mechanics-based simulations

Abstract

The fracture mechanics-based IBESS approach is applied to the determination of FAT classes of butt welds with crack initiation along the weld toe. The aim is an investigation of the effect of the geometrical parameters toe radius, flank angle, reinforcement and secondary notches such as roughness or undercuts. The influence of these parameters is discussed both individually and in combination; however, excluding statistical distributions of them and the material data. The results, when compared with conventional FAT classes for butt welds, are encouraging with respect to a potential contribution of IBESS to the discussion of more advanced quality criteria for welds. To that purpose, demands for further research are proposed.

Statistical Characterization of Stress Concentrations along Butt Joint Weld Seams Using Deep Neural Networks

“…Here, the weld toe radii seem to be better described by a lognormal distribution. This agrees with a number of recent studies [20,36,37]. As the sample distributions of the measured weld toe angles are symmetrical, it is possible that this parameter is indeed well-described by a normal distribution; however, more data should be assessed to verify this result.…”

“…To this day, there are only a few studies that consider distributions of weld geometries or stress concentration factors [12,13,16,[36][37][38][39][40][41][42][43]; however, there is no thorough investigation of what type of distribution type is suitable for the different geometry parameters and SCFs. Thus, a better understanding of statistical distributions of weld geometry parameters and stress concentrations is needed; nevertheless, first reliable tools to determine stress concentration factors are required.…”

“…Gamma distributions are frequently used for such tasks; however, more research is required. Ottersböck et al [20] and Schork et al [36,37] used a lognormal distribution to fit their data, which also accommodates very large numbers of small undercut depths; however, a lognormal distribution is not suited to describe distributions for which the maximum of data is equal to zero. In general, data for undercut depths are seldom presented nor considered in the literature.…”

“…Based on Figure 10, weld reinforcement heights could either be normally distributed or described by a lognormal, gamma, or inverse gamma distribution. In the literature, the height of welds is often described by a normal distribution [20,36,37,43].…”

An algorithm for statistical evaluation of weld toe geometries using laser triangulation

“…For fatigue analysis the local geometry of the weld toe is an important factor. In welded constructions the weld reinforcement leads to a stress concentration which affects the fatigue behaviour [1,2]. Especially high strength steels are sensitive to notches and lose their strength potential under cyclic loading compared to the base material [3,4].…”