Influence of Post-Processing Conditions on the Microstructure, Static, and Fatigue Resistance of Laser Powder Bed Fused Ti-6Al-4V Components
Bibliographic record
Abstract
The microstructure, static, and fatigue mechanical properties of laser powder bed fused (LPBF) Ti-6Al-4V components subjected to three different post-processing treatments (PTs) are compared. The first treatment includes stress relief (SR) and beta-phase annealing (BA) heat treatments, the second one includes SR, beta-solution (BST) and over aging (OA) heat treatments, and the third procedure is a combination of hot isostatic pressing (HIP) and BST + OA. It was demonstrated that the three PTs led to the decomposition of α’ martensite inherited from the LPBF process and the formation of variable α + β structures. The SR + BA treatment forms a basket weave structure having an average α lamellae width of ~3 µm and surrounded by ~1 µm-sized zones of segregated β phase (4.6–5.2% β phase content) and globalized α phase (~10 µm in size) inside prior columnar β grains (~100 µm in width). The SR + BST + OA treatment forms semi-equiaxed α grains (~300 µm) containing colonies (~50 µm) of parallel-oriented α plates (~6 µm), and β phase (5.8–7.5%) in the interplate spacing. The HIP + BST + OA treatment leads to the formation of large grains (~500 µm) with both basket weave and colony (~40 µm) α structures containing α plates (1.1–4.2 µm) and β phase (5–7.1%). To compare the impact of these PTs on the mechanical properties of LPBF components, they were subjected to static and fatigue tensile testing at room temperature. The best combination of mechanical properties (yield strength ~920 MPa, ultimate strength ~1000 MPa, elongation to break ~22.5%, and fatigue strength ~600 MPa, 107 cycles) was obtained in the case of SR + BA specimens. These results demonstrate that an adequate thermal treatment, such as SR + BA, of the LPBF Ti64 components, could be a valuable and less expensive alternative to the established HIP + BST + OA treatment procedure when fatigue life is the main concern.
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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 | 0.001 |
| 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".