Foaming of friction stir processed Al/MgCO <sub>3</sub> precursor via flame heating
Bibliographic record
Abstract
Abstract In the recent years, metal foams have become promising candidate materials in the engineering sector owing to their light weight and excellent energy absorption properties. Friction stir processing (FSP) has emerged as a cost-effective route to fabricate metal foam precursors from bulk substrates. Although the short processing time in FSP is able to provide high productivity, the cost of the foaming agent, TiH 2 in the case of aluminum foams is still high. This paper introduces flame heating to achieve localized foaming of aluminum alloy AA5754 to explore the possibility of using magnesium carbonate as a foaming agent stirred using multi-pass FSP. A specially designed slot based strategy using two plates arranged in lap configuration is devised to stir the foaming agent and understand the material movement after each subsequent pass. Microscopy techniques were carried out to evaluate the distribution of the foaming agent after each pass and the resulting microstructure of the processed plates as well as the morphology of the foamed sample. EDX results showed higher Mg and O content around the pore walls.
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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".