Very high cycle fatigue behavior of AlSi7Mg alloy
Bibliographic record
Abstract
The recyclability of metal powders in Laser Powder Bed Fusion (L-PBF) processes is crucial for both economic and environmental sustainability in additive manufacturing. This study investigates the influence of powder recycling and subsequent heat treatment of the recycled powder on the very high cycle fatigue performance and defect characteristics of L-PBF-manufactured AlSi7Mg components. CT analysis revealed comparable total defect counts between new powder and recycled powder specimens, with recycled powder showing fewer surface defects but slightly higher internal defects. Both specimens exhibited almost identical spatial distributions of defects, with new powder demonstrating marginally better sphericity. Specimens fabricated from recycled powder exhibited the highest fatigue performance across all stress levels. Those fabricated from new powder performed better than those using heated recycled powder but were still outperformed by recycled powder. Specimens fabricated with heated recycled powder demonstrated the lowest fatigue performance. The improved fatigue performance of recycled powder despite slightly lower defect sphericity suggests that beyond powder condition and defects count, other factors such as microstructural characteristics, defect position and alignment, oxidation state, particle size distribution, and loading frequency play significant roles in determining fatigue behavior. These findings provide insights into the effects of powder condition on the fatigue performance of L-PBF AlSi7Mg components, highlighting the complex interplay of various factors affecting material behavior in very high cycle fatigue conditions.
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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 | 0.001 | 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".