The competing role of defects and surface roughness on the fatigue behavior of additively manufactured AlSi10Mg alloy
Bibliographic record
Abstract
The fatigue behavior of AlSi10Mg alloy processed by laser-based powder bed fusion of metals (PBF-LB/M) in as-built (AB) and surface treated conditions, is assessed. A new chemo-mechanical polishing (CMP) surface treatment is applied to AB specimens to achieve an average surface roughness of S a ≈ 0.3 μm which is more than two orders of magnitude lower than that of AB specimens ( S a ≈ 48.6 μm). The CMP surface treatment led to a significant improvement in fatigue strength of the alloy as compared to the AB material. The fatigue life of AB + CMP specimens is discovered to be governed by surface or near surface defects, whereas the deepest surface roughness valleys play a dominant role in controlling the fatigue behavior of the AB material. A simplified fracture mechanics-based fatigue life modeling approach was developed by combining real-time defects distribution data from 3D computed tomography (CT) and surface profilometry measurements. The model provided a quick and effective medium for obtaining reasonable first-order fatigue life approximations for AlSi10Mg alloy fabricated using PBF-LB/M.
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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) | 0.000 | 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 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".