Effect of Microsegregation on High‐Temperature Microstructure Evolution in Rapid Solidification Processed Nb‐Rich Ni Superalloys
Bibliographic record
Abstract
Rapid solidification processes are used for coating, repairing, and manufacturing of metal alloy components in numerous industries. However, many alloys experience microsegregation of elements at subgrain boundaries at the termination of solidification, which have potential consequences for phase transformations at elevated temperatures if used without homogenization heat treatments. Herein, the effect of this microsegregation and solidification microstructure on the coarsening kinetics of the δ phase in a Nb‐rich Ni superalloy is evaluated. Microsegregation in rapid solidification processed (RSP) alloy 718 results in a greater availability of nucleation sites for the precipitation of the δ phase, resulting in smaller precipitate sizes. Coarsening of the δ phase is found to follow a Lifshitz–Slyozov–Wagner model, which is used to determine that Nb diffusion is the limiting factor in δ phase coarsening. With the formation of the δ phase, and the concurrent coarsening and dissolution of the γ″ strengthening phase, a decrease in both tensile strength and microhardness is observed. An improved understanding of the influence of microsegregation on phase transformations allows for a more informed application of non‐homogenized, RSP alloys in industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".