Facile manipulation of mechanical properties of Ti-6Al-4V through composition tailoring in laser powder bed fusion
Bibliographic record
Abstract
Ti-6Al-4 V alloy when processed by laser powder bed fusion (LPBF) is a useful material which can be used for the manufacture of complex 3D components for aerospace and medical applications. LPBF fabricated Ti-6Al-4 V typically shows high tensile strength (>1200 MPa) but poor ductility (<10%), explained by the characteristic microstructures that form under high cooling rates and multiple thermal cycles. Here, the composition of the feedstock Ti-6Al-4 V powder was modified via the addition of commercially pure Ti (CP Ti) to achieve improved ductility of LPBF parts through an in-situ modified composition, while maintaining good strength. The LPBF printed Ti alloy (with nominal composition Ti-3Al-2 V) showed a tensile strength of ∼1000 MPa, with improved ductility (∼13%), comparable to wrought Ti-6Al-4 V. These properties are thought to be explained by the decreased c/a ratio (where c and a are the lattice parameters of the titanium hexagonal close-packed phase) resulting in enhanced dislocation slip. Good relative density was also achieved using the modified composition under the same processing parameters. Some non-uniform regions were seen in the developed alloys, as well as evidence of increased martensite α′ thickness. It is proposed that with optimized parameters, further enhancements may be achieved. This work has shown that microstructural and mechanical properties can be facilely manipulated with proper CP Ti addition.
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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".