Microstructure, Processability, and Strength of SiC-Reinforced AlSi9Mg Composite After Laser Surface Remelting and Post-Heat Treatment
Bibliographic record
Abstract
The present study investigated the microstructure, processability, and mechanical strength of an AlSi9Mg-20vol.%SiC composite to assess its processing and mechanical performance during the laser powder bed fusion process. A simple laser surface remelting approach was adopted to simulate laser-based rapid solidification. The results revealed that this composite generally exhibited good laser processability, and the samples with the highest laser energy density and lowest scan speed possessed the best processability owing to the elimination of microcracks and pores. After laser processing, all the samples displayed a fine Al-Si cellular structure accompanied by in-situ formed fine needle-shaped Al4SiC4 particles. Increasing laser energy density considerably increased the area fraction of the Al4SiC4. The T5 aging treatment preserved the fine cellular structure and promoted the precipitation of a large number of Si nanoparticles and MgSi precipitates. During T6 solid solution treatment, the Si networks were broken down into coarse Si particles, disintegrating the cellular structure and reducing the strength. The T5 treatment was identified as the most suitable post-heat treatment for enhancing the microhardness and strength of the composite. Compared to conventionally laser-processed AlSi10Mg alloys, the AlSi9Mg-20vol.%SiC composite exhibited a significant increase in microhardness and yield strength.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".