Comparison of microstructure, crystallographic texture, and mechanical properties in Ti–15Mo–5Zr–3Al alloys fabricated via electron and laser beam powder bed fusion technologies
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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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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) | 0.001 | 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 it