The Application of Globular Water-Atomized Iron Powders for Additive Manufacturing by a LENS Technique
Bibliographic record
Abstract
The water-atomized ATOMET 28, 1001, 4701, and 4801 powders, manufactured by Rio Tinto Metal Powders, were used for additive manufacturing by a laser engineered net shaping (LENS) technique. Their overall morphology was globular and rounded with a size distribution from about 20 to 200 µm. Only the ATOMET 28 powder was characterized by a strong inhomogeneity of particle size and irregular polyhedral shape of powder particles with sharp edges. The powders were pre-sieved to a size distribution from 40 to 150 µm before LENS processing. One particular sample-LENS-fabricated from the ATOMET 28 powder-was characterized by the largest cross-sectional (2D) porosity of 4.2% and bulk porosity of 3.9%, the latter determined by microtomography measurements. In contrast, the cross-sectional porosities of bulk, solid, nearly cubic LENS-fabricated samples from the other ATOMET powders exhibited very low porosities within the range 0.03⁻0.1%. Unexpectedly, the solid sample-LENS-fabricated from the reference, a purely spherical Fe 99.8 powder-exhibited a porosity of 1.1%, the second largest after that of the pre-sieved, nonspherical ATOMET 28 powder. Vibrations incorporated mechanically into the LENS powder feeding system substantially improved the flow rate vs. feeding rate dependence, making it completely linear with an excellent coefficient of fit, R² = 0.99. In comparison, the reference powder Fe 99.8 always exhibited a linear dependence of the powder flow rate vs. feeding rate, regardless of vibrations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".