Constructing a Validated Deformation Mechanisms Map Using Low Temperature Creep Strain Accommodation Processes for Nickel-Base Alloy 718
Bibliographic record
Abstract
A creep Deformation Mechanism Map (DMM) of an engineering alloy can be an effective tool for developing physics-based prognostics systems. Many classical diffusion based rate equations have been developed for time dependent plastic flow where dislocation glide, dislocation glide-plus-climb and vacancy diffusion driven grain boundary migration (diffusion creep) are rate controlling. These creep rate equations have been proven experimentally for simple metals and alloys and form the basis of constructing an Ashby’s DMM. Long term creep testing and analysis of complex engineering alloys has shown that power law breakdown phenomenon is related to the dominance of Grain Boundary Sliding (GBS) as opposed to diffusion creep. Rate equations are now available for GBS in complex alloys and, in this paper, a DMM is constructed for a fine grained Alloy 718 and this is validated by comparison with a collection of experimental data obtained from the literature. The GBS accommodated by wedge type cracking is considered dominant at low homologous temperatures (0.3 to 0.5Tm i. e. melting temperature in Kelvin) whereas GBS accommodated by power-law or cavitation creep dominates above 0.55Tm.
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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 | 0.000 |
| 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".