Influence of Shot Peening on AlSi10Mg Parts Fabricated by Additive Manufacturing
Bibliographic record
Abstract
The additive manufacturing (AM) of aluminum alloys promises to considerably enhance the performance of lightweight critical parts in various industrial applications. AlSi10Mg is one of the compatible Al alloys used in the selective laser melting of lightweight components. However, the surface defects obtained from the as-built parts affect their mechanical properties, and thus represent an obstacle to using them as final products. This study aims to improve the surface characteristics of the as-built AlSi10Mg parts using shot peening (SP). To achieve this goal, different SP intensities were applied to various surface textures of the as-built samples. The SP results showed a significant improvement in the as-built surface topography and a higher value of effective depth using 22.9 A intensity and Gp165 glass beads. The area near the shot-peened surface showed a significant microstructure refinement to a specific depth due to the dynamic precipitation of nanoscale Si particles. Surface hardening was also detected and high compressive residual stresses were generated due to severe plastic deformation. The surface characteristics obtained after SP could result in a significant improvement in the mechanical properties and fatigue strength, and thus promise performance enhancement for critical parts in various industrial applications.
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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".