3D laser scans are increasingly used for the analysis of the weld quality. Based on these scans, it is possible to determine the weld profiles relevant for the fatigue strength, i.e. the weld angle and notch radii or other features as spatter or undercuts. These methods are currently developed and demonstrated in scientific investigations. However, in order to enable the applicability of 3D laser scans for quality assessment in practice, methods are still needed to automatically and reliably identify the weld seam and the weld direction from 3D scans. With these information, the weld profile in 2D cross-sections can be evaluated. Current studies have shown that the detection of the weld seam is the major challenge for weld quality assessment based on local geometrical parameters (weld angle and notch radii). In this paper, different algorithms are presented that enable an automated weld seam detection. On the one hand, these are algorithm-based methods that are based on curvature of the surface, on the random sample consensus (RANSCAC) algorithm or artificial neural (ANN) networks are considered. All methods are presented in detail and applied as well as verified on a larger number of different weld geometries. A particular focus is placed on curved seams, i.e. welds on pipe joints, pipe flange connections or wrap-around welds.

Spot welds play an important role in automotive structures and therefore these joints need to exhibit good reliability under durability loading over the lifetime of the vehicle. For many years, numerical methods for estimating their durability performance exist. They can be subdivided in structural and notch stress approaches as well as approaches that use fracture mechanics. However, the common approaches are developed to reliably predict crack initiation under Mode I loading conditions. This can lead to a non-conservative dimensioning und subsequently to failure in service in cases where in-plane or out-of-plane shear dominates. This paper proposes a multi-scale approach to account for additional Mode II / III loading using stress intensity factors. Two parametric model are used to set up a limited number of macro- and meso-scale models from which the correlation between forces and moments and local SIF at spot weld is evaluated. To reduce the computational effort in practical application, a machine learning model is trained that estimates SIFs. This model can be used to reliably assess spot weld fatigue performance in full body analyses. The model is validated based on selected fatigue test data.

Welded joints made of ductile materials show a significant reduction in fatigue life when subjected to non-proportional loading compared to proportional loading. While existing research on non-proportional loading strongly focuses on a 90° phase shift between normal and shear stress, which is widely considered to be most damaging, complex loading other than out-of-phase is rarely and insufficiently studied. This paper presents fatigue tests under various forms of non-proportional loading to distinguish the effects on fatigue life. These forms include different phase shifts, different frequencies, different R-ratios of normal and shear stress as well as an alternating application of both stress components. Finally, hypothesis testing is applied to differentiate whether different forms of non-proportional loading have different effects on fatigue life.