Laser Powder Bed Fusion of Water-Atomized Iron-Based Powders: Process Optimization
Bibliographic record
Abstract
The laser powder bed fusion (L-PBF) technology was adapted for use with non-spherical low-cost water-atomized iron powders. A simplified numerical and experimental modeling approach was applied to determine—in a first approximation—the operation window for the selected powder in terms of laser power, scanning speed, hatching space, and layer thickness. The operation window, delimited by a build rate ranging from 4 to 25 cm3/h, and a volumetric energy density ranging from 50 to 190 J/mm3, was subsequently optimized to improve the density, the mechanical properties, and the surface roughness of the manufactured specimens. Standard L-PBF-built specimens were subjected to microstructural (porosity, grain size) and metrological (accuracy, shrinkage, minimum wall thickness, surface roughness) analyses and mechanical testing (three-point bending and tensile tests). The results of the microstructural, metrological and mechanical characterizations of the L-PBF-built specimens subjected to stress relieve annealing and hot isostatic pressing were then compared with those obtained with conventional pressing-sintering technology. Finally, by using an energy density of 70 J/mm3 and a build rate of 9 cm3/h, it was possible to manufacture 99.8%-dense specimens with an ultimate strength of 330 MPa and an elongation to failure of 30%, despite the relatively poor circularity of the powder used.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".