Competing roles of microstructure and defects on the mechanical properties of laser-powder bed fused Ti-6Al-2Sn-4Zr-2Mo alloy
Bibliographic record
Abstract
Abstract Using different volumetric energy densities ( $${E}_{\text{v}}$$ E v ), the microstructure, texture, and defect evolution in laser-powder bed fused (PBF-LB/M) Ti-6Al-2Sn-4Zr-2Mo (Ti-6242) alloy is studied. PBF-LB/M Ti-6242 rods were manufactured using different $${E}_{\text{v}}$$ E v ranging from 41.67 to 66.67 J/mm 3 . The $${E}_{\text{v}}$$ E v is varied by setting the scan speed to 1000 mm/s, 1200 mm/s, 1400 mm/s, and 1600 mm/s. The mechanical properties (yield strength, tensile strength, and strain at fracture) were then studied under quasi-static loading conditions. It is observed that the strength of the sample printed using the lowest $${E}_{\text{v}}$$ E v is lower than the other conditions due to the formation of the lack of fusion defects. In addition, the sample printed with the highest $${E}_{\text{v}}$$ E v consists of redeposited process by-products that result in the lowest ductility. The microstructure and texture of the samples were studied using electron backscatter diffraction. The results show that microstructural features including α′ lath width, dislocation density, and lath orientation (texture) were almost identical under different $${E}_{\text{v}}$$ E v . Therefore, the variations in mechanical properties may not controlled completely by the microstructure. The defect analysis is conducted employing X-ray computed tomography. The defect characteristics change from keyhole to lack of fusion by varying the $${E}_{\text{v}}$$ E v . The volume fraction of defects in the samples is in the range of 0.0005–0.007%, which seems to be negligible. However, the fractography analysis shows the dominance of defects in controlling the mechanical properties. This study proves the sensitivity of PBF-LB/M Ti-6242 to defects as the mechanical properties were defect-driven rather than microstructure-driven.
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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".