Characterization of Composite Powder Feedstock from Powder Bed Fusion Additive Manufacturing Perspective
Bibliographic record
Abstract
This research aims at evaluating the characteristics of the 5 wt.% B4C/Ti-6Al-4V composite powder feedstock prepared by two different categories of mechanical mixing for powder bed fusion (PBF) additive manufacturing (AM) of metal matrix composites (MMCs). Microstructural features, particle size, size distribution, sphericity, conditioned bulk density and flow behavior of the developed powders were examined. The flowability of the regularly mixed powders was significantly lower than that of the Ti-6Al-4V powder. However, the flowability of the ball-milled systems was a significant function of the milling time. The decrease in the flowability of the 2 h ball-milled powder compared to the Ti-6Al-4V powder was attributed to the mechanical interlocking and the entangling caused by the B4C particles fully decorating the Ti-6Al-4V particles. Although the flattened/irregular shape of powder particles in the 6 h milled system acted to reduce the flowability, the overall surface area reduction led to higher flowability than that for the 2 h milling case. Regardless of the mixing method, incorporation of B4C particles into the system decreased the apparent density of the Ti-6Al-4V powder. The composite powder obtained by 2 h of ball milling was suggested as the best possible condition, meeting the requirements of PBF–AM processes.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".