Modeling of ?' Precipitate Size of IN738LC Using Levenberg–Marquardt Backpropagation Neural Network
Bibliographic record
Abstract
The γ' precipitate size of IN738LC is predicted using a Levenberg-Marquard backpropagation neural network in matlab toolbox. A cast polycrystalline Ni based super alloy IN738LC (a gas turbine material) is considered and the γ' precipitate size is described as a function of 5 variables (solutionizing temperature, solutionizing duration, ageing temperature, ageing duration, and cooling method (furnace cooling, water quenching, induction cooling, salt bath cooling, accelerated air cooling and oil quenching). The model converges very well and accurately predicts the precipitate size. Because first stage gas turbine blades operate at very high and varying temperatures for extended period of time, the prediction of their precipitate size is crucial as precipitate morphology is responsible for most high temperature properties. The model developed in this work can be useful for predicting creep and other mocrstuctural properties at high temperatures.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".