MICROSTRUCTURAL MODELING OF COLD CREEP/FATIGUE IN NEAR ALPHA TITANIUM ALLOYS USING CELLULAR AUTOMATA METHOD
Bibliographic record
Abstract
It is well known that the presence of large heterogeneous textured regions in forged near alpha titanium alloys could lead to large variations of mechanical properties when fatigue and creep cycles are applied at room temperature. On the other hand, experimental studies and microtexture investigations are complex to set up, lengthy and costly, and one cannot expect to understand the alloy behavior by relying only on empirical approaches. Hence, numerical methods are excellent alternatives for analyzing the influence of microscopic and macroscopic heterogeneities on mechanical properties in shorter times and with minimum need for experimentation. In the present investigation, a cellular automata (CA) method was used to simulate the effect of texture heterogeneities, on both local and global mechanical properties. A 2D array of cells was used and the stresses and strains developed in various heterogeneous regions were evaluated using the Eshelby theory. Using the CA method, various types of microstructures were modeled and compared with each other to quantify the influence of processing parameters on mechanical properties. The results predict, and are used to explain, the experimentally phenomena observed in creep responses during cold fatigue/creep tests of near alpha titanium samples.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".