Effect of Heat Treatment on the Morphology of γ′ Precipitates in LW 4275 and LW 4280
Bibliographic record
Abstract
Abstract This study investigates the microstructural evolution of the γ′ precipitates in precipitation hardened Ni-based superalloys LW 4275 and LW 4280 comprising 5.2–5.5 wt. % Al, fabricated by laser powder bed fusion. The as-built microstructure established the presence of discrete carbides, while no signs of γ′ precipitates were present in either LW 4275 or LW 4280 specimens due to a high cooling rate of ∼1e+6 °C/sec. Both specimens were heat treated under a sub-solvus temperature of 1080 °C and super-solvus temperature of 1150 °C, which was established by Thermo-Calc modeling, followed by aging at 705 °C for 24h. Scanning electron microscope (SEM) examinations of samples subjected to sub-solvus heat treatment revealed that the size and volume fraction of the primary γ′ increased while the size and volume fraction of the secondary γ′ decreased as the solutionization period increased from 4 to 24h. Studies of the samples subjected to the super-solvus heat treatment revealed that the volume fraction and size of the γ′ precipitates increased with the increasing solutionization time from 4 h to 24h for both alloys. Finally, a detailed investigation of the morphological evolution of γ′ precipitate after both types of heat treatments was addressed.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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".