Effect of Foaming Time and Temperature on the Hardness of Al-Si-Cu-Mg Alloy Foam Cell Walls
Bibliographic record
Abstract
Al-Si-Cu-Mg alloy foams of different compositions and different cell morphologies were produced using the powder metallurgical method and by varying the foaming time and temperature during production. Hardness of the produced precursors and foams was measured using nanoindentation and micro indentation hardness measurement methods. Results obtained from both of the methods showed similar trend although the nanoindentation hardness of the specimens was consistently higher than the corresponding micro Vickers hardness. The precursor and foams obtained from Al-5wt.%Si-4wt.%Cu-4wt.%Mg (alloy 544) showed a higher hardness value than Al-3wt.%Si-2wt.%Cu-2wt.%Mg (alloy 322) precursor and foams made at the same foaming temperature and time because of their higher content of alloying elements. The hardness value of foam walls increased with the increase in foaming time at all foaming temperatures due to the increase of eutectic phase. Same density foams obtained from the same precursor but at different foaming temperatures were found to have different hardness values which indicate that local and global properties of foams with similar densities obtained from the same precursor will differ from each other if their foaming conditions are not the same
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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 | 0.001 |
| 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".