Processability and characterization of A20X aluminum alloy fabricated by laser powder bed fusion
Bibliographic record
Abstract
The processing window for laser powder bed fusion (LPBF) of A20X aluminum alloy was determined to increase the build rate and reduce the fabrication cost for this material. The influence of hatch spacing, laser scan speed, and powder layer thickness on the microstructure and mechanical properties were also systematically investigated. Over a wide processing window (40–115 J/mm 3 energy density), near fully dense and crack-free components were achieved. In all cases, the as-built microstructure consisted of an aluminum matrix with TiB 2 particles and Al 2 Cu precipitates, where volume fraction and size of both TiB 2 and Al 2 Cu phases were found to be independent of the LPBF process parameters. A fine equiaxed grain structure with a random crystallographic orientation was observed in the vertical section (XZ-plane) of the as-built samples. This led to an isotropic elastic modulus , ultimate tensile strength , and total elongation. The yield strength, however, was found to be anisotropic , exhibiting a 16 % difference for different orientations. The average grain size decreased 54 % due to increased scan speed and reduced powder layer thickness. This resulted in a remarkable improvement in the alloy micro-hardness value from 108 HV to 124 HV, where the experimental data obeyed the Hall-Petch relationship.
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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 | 0.001 |
| 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".