Calculation of the Stress Intensity Factor in an Inclusion-Containing Matrix
Bibliographic record
Abstract
The intent of this paper is to propose an engineering approach to estimate the stress intensity factor of a micro crack emerging from an inclusion in relation with the morphology of the inclusion and its relative stiffness with the matrix. A micromechanical model, based on the FEA (finite element analysis) of the behavior of cracks initiated at micro structural features such as inclusions, has been developed using LEFM (Linear Elastic Fracture Mechanics) to predict the stress intensity factor of a micro crack emerging from an inclusion. Morphology of inclusions has important connotations in the development of the analysis. Stress intensity factor has been estimated from the FEA model for different crack geometries. Metallographic analysis of inclusions has been carried out to evaluate the typical inclusion geometry. It also suggests that micro cracks less than 1 μm behave differently than larger cracks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".