Experimental and Numerical Determination of Mixed Mode Crack Extension Angle
Bibliographic record
Abstract
Abstract Crack initiation angles for mixed mode fracture are measured and predicted using photoelastic and finite element techniques. Different crack inclination angles (0°, 10°, 20°, 30°, and 40°) are considered. The Schroedl and Smith method is used for estimating pure opening mode I stress intensity factor. On the other hand, the Sanford and Dally method is used to estimate stress intensity factors in mixed mode (I and II). The Smith and Smith method for estimating mixed mode SIF is also examined. In addition, the ANSYS finite element code is employed to estimate the pure and mixed mode SIF numerically. The estimated values of the SIF from both finite element analysis and photoelasticity are incorporated into six criteria for crack initiation angle prediction. The applicability of the crack initiation criteria such as maximum tangential stress (MTS), minimum strain energy density (S), maximum dilatational strain (T), maximum triaxial stress (M), modified MTS (MMTS), (R) criterion, and fringe symmetry axis method (FSAM) are investigated. An experimental setup is used to measure the initiation angles at different crack inclinations. The measured crack initiation angles are used to validate the predicted values. The results showed that the initiation angles estimated by different criteria using SIFs determined experimentally and numerically compared well with the measured ones, especially at higher inclination angles.
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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 | 0.001 | 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".