Stress Intensity Factors and T-Stress Solutions for Mixed-Mode Compact-Tension-Shear (CTS) Specimens With Slanted Propagating Cracks
Bibliographic record
Abstract
Abstract More and more attention has been paid to the problems of fatigue fracture in mixed fracture modes, and researchers have developed a variety of laboratory specimen types. Among them, the Compact-Tension-Shear (CTS) specimen with its loading device has been widely used in mixed-mode I&II fatigue crack growth (FCG) experiments. In this paper, for CTS specimen with slanted propagating crack, the variation of stress field parameters including stress intensity factors (SIFs) and T-stresses were investigated through three-dimensional finite element (FE) analysis. The results show that the direction of crack initiation tends to make KII vanish and then the crack propagates in a mode I dominant manner even under mode II dominant loading angles. In addition, among the considered range of geometries, T11 is greatly influenced by the initial crack length ratio, the slanted propagating crack length ratio and the loading angle, but rarely by the specimen thickness ratio. However, T33 as an out-of-plane constraint parameter, is subject to the coupling effects of in-plane geometries, out-of-plane geometries and the mixed-mode loading. The conclusions obtained in the current work will provide usable constraint-related crack front stress field parameter solutions for the analysis of fatigue crack propagating behavior in mixed fracture mode.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".