Evaluation of the Effect of Nano Al₂O₃ Additive on Al-Si-Cu Alloys Performance Produced by Squeeze Casting and High Pressure Die Castings: Experimentation and Mathematical Modeling
Bibliographic record
Abstract
Light alloy metals (Hypoeutectic Al-Si-Cu alloys) have retained their importance and properties as primary candidates when examining the correlation of the cost function that makes them suitable for many applications.The research aims to fabricate Al-Si-Cu alloys using nano-Al2O3 as heterogeneous nucleation of a eutectic solution to appropriately squeeze casting and pressure die casting processes under varying pressures (150, 200, and 250 MPa), pouring temperature with 780, mold preheated to 250.The yield strength and elongation are 12%, and also the hardness is 33.1% higher than that of gravity castings.In addition, the samples from the squeeze casts have lower wear rates than the samples of the pressure die cast.The design of experiments (DOE) method was applied to study and monitor the properties of Al-Si alloy.The relationship between the process parameters, including casting pressure and the percentages of Nano additives, and the main process responses was analyzed.Based on the used modeling tool, the final equations of the responses, including hardness, corrosion, tensile strength, and wear rate, were found.The modeling results showed good agreement with the experimental results, with a maximum error of 1.54% and a minimum error of 0.25%.
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 | 0.001 | 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".