Crack tip displacement fields measured by digital image correlation for evaluating variable mode-mixity during fatigue crack growth
Source abstract
Abstract
Abstract The assessment of mixed-mode fatigue is an actual object of research. Various hypotheses have been published with the focus on calculating equivalent stress intensity factors and on predicting kink angles after a change of mode-mixity from one cycle to the next. Relatively few works are known for cases when the mode-mixity changes continuously during the fatigue cycles. Such investigations on determining equivalent stress intensity factors and their effects on crack growth have been performed in the present study. Thin-walled tubes with a through-wall crack have been loaded by proportional and non-proportional tension and torsion. In the experimental investigation, the region of fatigue crack growth was observed by means of applying the digital image correlation technique. The variations of the displacement fields during the cycles are shown. Depending on the ratio of applied loads as well as on the actual orientation of the crack tip tangent, fracture parameters associated with opening modes I, II and III are identified, not only during a cycle but also from cycle to cycle. As the crack path is known from experiment, its curvature – both kinks and continuously developing warped cracks were observed – may be attributed to the variation of the displacement field. Results of ongoing work are presented in this paper. Modelling crack closure is a special challenge. Closure is clearly visible in the measurement. It is a combination of conventional plasticity induced closure and roughness induced closure; on top, the curvature of the cracks enforces a geometrical type of crack flank locking.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Fatigue and fracture mechanics · Structural Health Monitoring Techniques · Structural Load-Bearing Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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