A THREE-DIMENSIONAL QUANTITATIVE UNDERSTANDING OF SHORT FATIGUE CRACK GROWTH IN HIGH STRENGTH ALUMINUM ALLOYS
Bibliographic record
Abstract
The behaviors of short fatigue crack (SFC) propagation through grain boundaries (GBs) were monitored during high cycle fatigue in an Al-Li alloy AA8090. The growth behaviors of SFCs were found to be mainly controlled by the twist components (α) of crack plane deflection across each of up to first 20 GBs along the crack path. The crack plane twist at the GB can result in a resistance against SFC growth; therefore SFC propagation preferred to follow a path with minimum α at each GB. In addition to the grain orientation, the tilting of GB could also affect α. An experiment focusing on quantifying GB-resistance was conducted on an Al-Cu alloy AA2024-T351. With a focused ion beam (FIB) and electron backscatter diffraction (EBSD), the micro-notches were made in front of the selected GBs which had a wide range of α, followed by monitoring the interaction of crack propagation from the notches with the GBs during fatigue. The crack growth rate was observed to decrease at each GB it had passed; and such growth-rate decrease was proportional to α. The resistance of the GB was determined to vary as a Weibull-type function of α. Based on these discoveries, a microstructure-based 3-D model was developed to quantify the SFC growth in high-strength Al alloys, allowing the prediction of crack front advancement in 3-D and the quantification of growth rate along the crack front. The simulation results yielded a good agreement with the experimental results about the SFC growth rate on the surface of the AA8090 Al alloy. The model was also used to predict the life of SFC growth statistically in different textures, showing potential application to texture design of alloys. Fatigue crack initiation at constituent particles (β-phase) was preliminarily studied in the AA2024-T351 Al alloy. Cross-sectioning with the FIB revealed that the 3-D geometry, especially the thickness, of fractured constituent particles (β-phase) was the key factor controlling the driving force for micro-crack growth. The resistance to micro-crack growth, mainly associated with crack plane twist at the particle/matrix interface, also influenced the growth behaviors of the micro-cracks at the particles on the surface.
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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".