Comparison of microstructure, crystallographic texture, and mechanical properties in Ti–15Mo–5Zr–3Al alloys fabricated via electron and laser beam powder bed fusion technologies
Bibliographic record
Abstract
Depending on the application, establishing a strategy for selecting the type of powder bed fusion technology—from electron beam (EB-PBF) or laser powder bed fusion (L-PBF)—is important. In this study, we focused on the β-type Ti–15Mo–5Zr–3Al alloy (expected for hard-tissue implant applications) as a model material, and we examined the variations in the microstructure, crystallographic texture, and resultant mechanical properties of specimens fabricated by L-PBF and EB-PBF. Because the melting mode transforms from the conduction mode to the keyhole mode with an increase in the energy density in L-PBF, the relative density of the L-PBF-built specimen decreases at higher energy densities, unlike that of the EB-PBF-built specimen. Although both EB-PBF and L-PBF can obtain cubic crystallographic textures via bidirectional scanning with a 90° rotation in each layer, the formation mechanisms of the textures were found to be different. The <100> texture in the build direction is mainly derived from the vertically grown columnar cells in EB-PBF, whereas it is derived from the vertically and horizontally grown columnar cells in L-PBF. Consequently, different textures were developed via bidirectional scanning without rotation in each layer: the <110> and <100> aligned textures along the build direction in L-PBF and EB-PBF, respectively. The L-PBF-built specimen exhibited considerably better ductility, but slightly lower strength than the EB-PBF-built specimen, under the conditions of the same crystallographic texture and relative density. We attributed this to the variation in the microstructures of the specimens; the formation of the α-phase was completely absent in the L-PBF-built specimen. The results demonstrate the importance of properly selecting the two technologies according to the material and its application.
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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) | 0.000 | 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 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".