The Static and Fatigue Behavior of AlSiMg Alloy Plain, Notched, and Diamond Lattice Specimens Fabricated by Laser Powder Bed Fusion
Bibliographic record
Abstract
The fabrication of engineered lattice structures has recently gained momentum due to the development of novel additive manufacturing techniques. Interest in lattice structures resides not only in the possibility of obtaining efficient lightweight materials, but also in the functionality of pre-designed architectured structures for specific applications, such as biomimetic implants, chemical catalyzers, and heat transfer devices. The mechanical behaviour of lattice structures depends not only the composition of the base material, but also on the type and size of the unit cells, as well as on the material microstructure resulting from a specific fabrication procedure. The present work focuses on the static and fatigue behavior of diamond cell lattice structures fabricated from an AlSiMg alloy by laser powder bed fusion technology. In particular, the specimens were fabricated with three different orientations of lattice cells—[001], [011], [111]—and subjected to static tensile testing and force-controlled pull–pull fatigue testing up to 1 × 107 cycles. In parallel, the mechanical behavior of dense tensile plain and notched specimens was also studied and compared to that of their lattice counterparts. Results showed a significant effect of the cell orientation on the fatigue lives: specimens oriented at [001] were ~30% more fatigue-resistant than specimens oriented at [011] and [111].
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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".