Fatigue assessment of weld seams considering elastic–plastic material behavior using the local strain approach

In this contribution, the notch strain approach is applied to seam welds including butt welds and filet welds. This allows the fatigue assessment for all regimes of fatigue life, including the low cycle fatigue regime (N > 10). Linear-elastic finite element analyses are used to determine the local stresses. The modeling of the geometry to be assessed is similar to that of the effective notch stress concept. Necessary input values besides linear-elastic stresses are estimated from the hardness of the heat-affected zone. Subsequently, the elastic–plastic stresses and strains are estimated, and a service life calculation is carried out. The used algorithm for the notch strain approach is based on the German FKM-guideline “nonlinear” and includes the influence of stress gradients, highly stressed surfaces, and the influence of surface roughness. Up to now, this FKM-guideline has only been approved for non-welded components. However, this article shows that, with a few modifications, it can also be applied to welds

3D Multiclass Digital Core Models via microCT, SEM-EDS and Deep Learning

We describe an integrated methodology for constructing a 3D multiclass model of a rock sample, based on X-ray microtomography (microCT) and quantitative evaluation of minerals (QEMSCAN) by automated SEM-EDS (Scanning Electron Microscopy, Energy Dispersive Spectroscopy). We focus on building an automated operator-independent workflow, allowing to distinguish between voxels featuring substantially different physical properties, such as void, quartz, denser and less dense clay aggregates. The workflow is demonstrated using a set of five ⌀8 mm Berea sandstone miniplugs. For each miniplug, a ~40003 voxel microCT image is acquired. Next, each miniplug is cut into smaller pieces, and the 45 resulting polished surfaces are subjected to the QEMSCAN analysis, producing ~40002 pixel mineral maps. Each mineral map is automatically spatially registered with the corresponding microCT image using an in-house surface-based algorithm. Further, the ground truth images for the supervised multiclass segmentation are constructed from the mineral maps. We compare 3D and 2D convolutional neural network (CNN) architectures with the baseline Naïve Bayes classifier, which is roughly equivalent to the approaches commonly used in practice today. We find that supervised CNN-based segmentation is fairly stable, despite microCT image quality non-uniformness and achieves higher quality scores compared to feature based and baseline approaches

Fatigue Crack Initiation and Propagation Relation of Notched Specimens with Welded Joint Characteristics

This study focuses on predicting the fatigue life of notched specimens with geometries and microstructure representative of welded joints. It employs 26 series of fatigue tests on welded and non-welded specimens containing notches located in different material zones, including the parent material, weld metal, and heat-affected zone. Overall, 351 test samples made of six structural steels are included in the present evaluation. For each individual specimen, the stress concentration factor, as well as the stress distribution in the notched section, was determined for subsequent fracture mechanics calculation. The latter is employed to estimate the fraction of fatigue life associated with crack propagation, starting from a small surface crack until fracture. It was shown that the total fatigue life can be realistically predicted by means of fracture mechanics calculations, whereas estimates of the fatigue life until macroscopic crack initiation are subject to numerous uncertainties. Furthermore, methods of statistical data analyses are applied to explore correlations between the S–N curves and the notch acuity characterized by the notch radius, opening angle, and the stress concentration factor. In particular, a strong correlation is observed between the notch acuity and the slope of the S–N curves