In order to ensure high weld qualities and structural integrity of engineering structures, it is crucial to detect areas of high stress concentrations along weld seams. Traditional inspection methods rely on visual inspection and manual weld geometry measurements. Recent advances in the field of automated measurement techniques allow virtually unrestricted numbers of inspections by laser measurements of weld profiles; however, in order to compare weld qualities of different welding processes and manufacturers, a deeper understanding of statistical distributions of stress concentrations along weld seams is required. Hence, this study presents an approach to statistically characterize different types of butt joint weld seams. For this purpose, an artificial neural network is created from 945 finite element simulations to determine stress concentration factors at butt joints. Besides higher quality of predictions compared to empirical estimation functions, the new approach can directly be applied to all types welded structures, including arc- and laser-welded butt joints, and coupled with all types of 3D-measurement devices. Furthermore, sheet thickness ranging from 1 mm to 100 mm can be assessed.
Commonly, to evaluate the influence of the local weld geometry in fatigue test, small-scale specimens are used, assuming those represent a longer weld adequately. In this study, a comparison between short specimens and a long weld is performed. A method is developed for the statistical evaluation of weld toe radii and angles, stress concentration factors and weld quality classes. The results show a strong sampling rate dependence and lower ISO 5817:2014 weld quality results for higher sampling rates. Comparable results between short specimens and a long weld can be achieved using modal values of the parameters assuming a lognormal distribution.
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