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.
Stress concentration factors (SCFs) at weld toes and weld roots as required for the effective notch stress concept (see [1, 2]) are usually computed using finite element analysis (FEA) which requires a certain amount of effort for model generation, the solving process, and postprocessing. Regression functions of many FEAs within given parameter bounds provide the possibility of a fast prediction of SCFs. This paper provides new and accurate regression formulae for the estimation of notch stresses at idealized weld geometries on the basis of multiple linear-elastic FEAs for the transverse stiffener (non-load carrying T-joint) under tension and bending of the load carrying slab. Regression of sampled finite element results has been performed using (a) second-order polynomial regression with coupling terms (PRC) and (b) artificial neural networks (ANN). The presented formulae are compared with several existing estimations of stress concentration factors. The new methods appear to show a higher quality of prognosis as well as apply to significant larger ranges of the geometrical parameters of the weld joint. The formulae presented here for the transverse stiffener add another welded joint to a series of similar surrogate models presented from Munich University of Applied Sciences in earlier publications and made available for use by the web-based tool SCF-Predictor.
This paper presents the influences of the irregularity “insufficient penetration welding” on the fatigue strength based on experimental and numerical investigations. For this purpose, the fatigue strength of partial penetration butt welds of mild steel (S355) with different non-welded gaps were subjected to fatigue loading. Three different planned gap dimensions (h = 4, 6, 8 mm) at one-sided (Y-seam) and two-sided (DY-seam) partial penetration butt welds were analysed. The results were obtained on a small-scale test specimen with a steel plate thickness of t = 20 mm, without axial and angular misalignment. For the numerical effective notch stress approach, regression formula for efficient analysis of stress concentration factors was determined. The results show that butt joints which are not fully penetrated on one or both sides, and which were previously not permissible for welded joints subject to fatigue loading, can be evaluated in the future on the basis of these investigations.
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