Effect of laser shock peening on the microstructure and mechanical property of AlSi10Mg alloy parts formed by SLM
Bibliographic record
Abstract
Selective laser melting (SLM) is considered to be a highly significant additive manufacturing (AM) technology, with the capacity to produce complex shapes that would be difficult to achieve using other methods. However, the broad application of this method is limited by problems like harmful microstructures and porosity, especially during the processing of aluminum alloys. Laser shock peening (LSP) provides a promising approach to reduce the adverse effects linked to aluminium SLM. This research examines how a critical LSP parameter, specifically the number of impacts, influences AlSi10Mg parts produced by SLM. The results were assessed with porosity, microstructure, and microhardness. Results show a 72% reduction in porosity. Furthermore, microstructural analysis revealed discernible grain refinement, accompanied by enhanced hardness. Tensile testing further confirmed the effectiveness of LSP, showing increases in both ultimate tensile strength and yield strength. These results suggest that LSP can effectively address the limitations of the SLM process for demanding applications when used as a post-processing technique.
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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".