Additive manufactured versus cast AlSi10Mg alloy: Microstructure and micromechanics
Bibliographic record
Abstract
This study aims at assessing the effect of solution heat treatment (at a temperature just below the eutectic temperature) followed by various cooling rates on the microstructure and mechanical properties of additively manufactured AlSi10Mg and the cast counterpart. The mechanical properties were evaluated using a depth-sensing nanoindentation platform. The cast and additively manufactured parts were solutionized at 540 °C for 2 h followed by water quenching, air cooling, and furnace cooling. Results show extensive microstructural changes (e.g. size and morphology of eutectic-silicon phase) and evolutions in the mechanical properties of the heat-treated materials relative to the as-printed and as-cast ones. Besides, the microstructure and micromechanical properties of the materials broadly alter the cast and additive manufacturing conditions. Depending on the cooling condition, the mentioned cooling cycles directly affect the morphology of eutectic-silicon in both cast and additive manufactured materials starting with silicon fragmentation, then followed by silicon spheroidization, and silicon coarsening. The microstructural evolution affects the local micromechanical properties of the studied materials. The results of this study provide insights into the control of microstructure and hence mechanical properties of AlSi10Mg alloy by addressing suitable heat treatment cycles. This study, for the first time, assesses and compares the effect of various post-fabrication cooling rates in the cast and additive manufacturing conditions in an AlSi10Mg alloy.
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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) | 0.002 | 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".