Applications of artificial intelligence methods for modelling of solidus temperature for hypoeutectic Al-Si-Cu alloys
Bibliographic record
Abstract
This paper presents the application of neural networks for prediction of the solidus temperature of various hypoeutectic Al-Si-Cu casting alloys cooled with different Cooling Rates (CR). Knowledge of solidus temperature allows the prediction of a variety of metallurgical characteristics, that is, melt treatment, casting temperature and solidification range. Currently, the literature reports only one equation for determination of solidus temperature for hypoeutectic aluminium alloys. This paper presents computational algorithm, comparison with different models' predicted solidus temperature and influence of alloying elements on solidus temperature. The results of this investigation show that there is a good correlation between experimental and calculated dates and the neural network has great potential in modelling of solidus temperature of Al-Si-Cu alloys. The worked out model can be applied in the computer system for calculating of chemical composition and CR influence on the solidus temperature of Al-Si-Cu alloys. [Received 22 November 2006, Accepted 15 January 2007]
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".