Processing of hydroxyapatite (HA)–Ti–6Al–4V composite powders via laser powder bed fusion (LPBF): effect of HA particle size and content on the microstructure and mechanical properties
Bibliographic record
Abstract
This research study examines the influence of hydroxyapatite (HA) powder particle size and content on the processability, microstructure, and mechanical properties of the laser powder bed fusion (LPBF) fabricated Ti–6Al–4V (Ti64)-HA composites. For this purpose, various Ti64-HA composite powders were produced, and the effects of the HA particle size and content on the optical reflectance were investigated. Composite powders and the monolithic Ti64 powder (as the reference) were subjected to the LPBF process within a wide range of volumetric energy densities by varying the LPBF process parameters. Parts with the highest relative density for each material were assessed by studying their microstructure, nanohardness, and nanoindentation-derived yield strength. Results revealed that the incorporation of the highly reflective HA powder into the Ti64 slightly enhanced the reflectance of Ti64-HA composite powders over that of the monolithic Ti64 powder. The LPBF processability of the composite powders was found to highly depend on the content of the HA constituent. For any given volumetric energy density employed in this study, composites containing 1 wt%HA were crack-free. However, composite systems containing higher HA content (2.5 wt%) featured randomly distributed transgranular cracks. Addition of 1 and 2.5 wt%HA to the Ti64 resulted in a significant improvement in the nanohardness (∼30 and 75%) and yield strength (∼20 and 37%). The dominant strengthening mechanisms in composite samples were the Hall-Petch and geometrically necessary dislocations strengthening, accounting for 57–66% and 13–20% of the total yield strength improvement.
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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".