Effect of loading rate and height–diameter ratio on compression characteristics of aviation alloys
Bibliographic record
Abstract
Loading rate and height–diameter ratio are important factors affecting mechanical properties of materials. In this paper, the effects of the two factors on the stress–strain curves and section deformation characteristics of titanium alloy and aluminum alloy were compared by uniaxial compression tests. The gray correlation coefficient between compressive strength and loading rate, height–diameter ratio, and high compression ratio of the two alloys was calculated by using the gray correlation theory, and multiple regression models of compressive strength of the two alloys were established based on the least squares method. The results show that ( i) the gray correlation coefficient of the two alloys is greater than 0.6, indicating that loading rate and height–diameter ratio have obvious effects on the compressive strengths of aluminum alloy; ( ii) of loading rate and height–diameter ratio, loading rate has more significant effect on compressive strength; and ( iii) the correlation coefficient of the regression model of titanium alloy compressive strength is 0.9, which is higher than that of the corresponding model of aluminum alloy (0.65), indicating that the reliability of the regression model of titanium alloy is higher than that of aluminum alloy, and the established model can better predict the uniaxial compressive strength of titanium alloy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".