Statistical Analysis of Fatigue Related Microstructural Parameters for Airframe Aluminum Alloys
Bibliographic record
Abstract
Abstract This paper presents the results of statistical analysis of the fatigue related microstructural parameters, mainly on constituent particles and pores, in 7050-T7452 aluminum forging materials. The statistical distributions of the particle and pore area data, which were obtained from the metallographic measurements on polished surfaces, were fitted well with three-parameter lognormal functions. Comparative studies were carried out for the particle and pore size distributions between the core and periphery materials, and between the hand and die forging materials. An extreme value theory-based model was investigated to correlate the overall material particle distribution with the fatigue crack-nucleating particle distribution. The results indicated that the particle size is not the only parameter affecting the fatigue process; other parameters, such as grain size and grain orientation, are also important parameters for microstructure-based fatigue modeling. Therefore, orientation image microscopy (OIM) analyses were carried out on pristine 7050 samples, on different planes, to determine the grain orientation distributions. Finally, a preliminary OIM analysis was performed on a fractured sample to measure the misorientation angles between the grains near the crack nucleation and short crack regions. It is expected that the test observations and quantitative microstructural data could help to understand the effects of microstructure on the fatigue process and develop a microstructure-based fatigue modeling.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.001 | 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".