Section thickness effect on microstructure, mechanical and electrical properties of permanent steel mold cast eutectic Al-1.8Fe alloy
Bibliographic record
Abstract
Abstract A eutectic aluminum alloy, with a composition of 1.8 wt% iron, underwent casting using permanent steel mold casting (PSMC) across three distinct section thicknesses: 2mm, 8mm, and 20mm. The microstructure of the as-cast alloy was analysed by the scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS). The microstructure analyses indicated that the cast Al-1.8Fe alloy consisted of the primary Al phase, eutectic Al phase, micron-sized eutectic Al-Fe phase, and nano-sized eutectic Al-Fe phase. The results of tensile testing revealed notable improvements in mechanical properties for the cast Al-1.8Fe alloy as the section thickness decreased from 20mm to 2mm. Specifically, Ultimate Tensile Strength (UTS), Yield Strength (YS), elongation (ef), modulus, toughness, resilience, and electrical conductivity increased from 85.99 MPa, 28.33 MPa, 15%, 63 GPa, 8.58 MJ/m3, 6.37 kJ/m3, 48.44 %IACS to 157.74 MPa, 84.83 MPa, 19%, 66.4 GPa, 23.87 MJ/3, 54.17 kJ/m3, 51.09 %IACS, respectively. Conversely, porosity levels decreased from 5.17% to 1.87% as thickness increased from 2mm to 20mm. The enhanced mechanical properties and electrical conductivity observed in the 2mm sample are attributed to its fine microstructure and low porosity. Additionally, SEM fractography revealed that fracture behavior in PSMC Al-1.8Fe was influenced by section thickness.
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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".