Mathematical model of influence of rapid induction heating on nucleation and growth of precipitates
Bibliographic record
Abstract
A mathematical model of the influence of rapid induction heating on the nucleation and growth of secondary phases in diffusion controlled processes has been developed. The total stress produced by the electromagnetic field and acting on the volume V of a specimen was derived and added to other external stresses applied to the system. Then, the total effective stress was introduced into mathematical equations of diffusion controlled nucleation and growth processes. In addition, the effect of rapid induction heating on the age hardening treatment of a selected cast nickel base superalloy, IN738LC, was investigated experimentally. For this purpose, two types of rapid aging with equal heating rates were applied: one treatment was induction aging and the other was salt bath aging. Microstructural characteristics of γ′ precipitates and hardening behaviour were studied by means of scanning electron microscopy (SEM), electron image analysis, transmission electron microscopy (TEM), and hardness testing. According to the results obtained, although the rates of heating in induction and salt bath aging were equal, the rates of nucleation and growth of γ′ precipitates in induction aging were much faster than those obtained in salt bath aging, especially in the first minutes of the aging process. Furthermore, the characteristics of γ′ precipitates with induction aging were more favorable than those with salt bath and normal aging. It was observed that the growth rate of γ′ in induction aging deviated considerably from the t1/3 growth law of the standard modified Lifshitz, Slyozov, and Wagner (MLSW) theory. The remarkable improvement of microstructural characteristics obtained with induction aging can be attributed to the existence of the external electromagnetic force produced by rapid induction heating.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".