Creep Mechanisms vis-à-vis Power Law vs. Grain Boundary Sliding in α-β Titanium Alloys for Physics Based Prognostics
Bibliographic record
Abstract
This work is performed in support of our continued physics-based prognostics system development using a life cycle management-expert system (LCM-ES) framework. The physical damage based modeling approach involving global behavior and localized response of a component at the microstructural level is used. The current research aims at constructing parts of a deformation mechanism map (DMM) for α-β Ti alloy. The appropriate constitutive equations are used for power-law creep and grain boundary sliding mechanisms. Simulations are performed using the Newton- Raphson method using Matlab software code in order to obtain contour lines corresponding to strain rates ranging from 104 to 10-12 over the homologous temperature ranges of 0.10 to 0.655. The dominance of power-law creep and grain boundary sliding over a wider range of stresses and temperatures in Ti-64 alloy is studied. The simulation results are validated using experimental data points. The predicted contour lines in the map match fairly well. The structure- creep mechanism relationships in α-β Ti alloy under different stress, temperature and strain rate conditions are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".