The LPBF printability and as-printed mechanical properties of a Ti6246 alloy as a function of printing parameters and microstructure
Bibliographic record
Abstract
Laser Powder Bed Fusion (LPBF) of titanium alloys has been widely investigated in the last decades and introduced in industry with concrete applications. Recently, the literature has highlighted the printability of various titanium alloys with different elemental compositions. Among them, a highly resilient Ti-6Al-2Sn-4Zr-6Mo alloy (Ti6246) was identified as a promising material for the automotive and aerospace industries, and the results of its successful printing using high-power systems ( P max = 400 W) have already been reported. In the present work, Ti6246 alloy was printed on a power-limited system ( P max = 200 W) using fifty-three printing parameter sets with variations in terms of laser power, scanning speed and hatching space, keeping the layer thickness constant at 50 μm. The as-built samples were then characterized to correlate the printing parameters to the microstructure, phases and mechanical properties. The results revealed that a volumetric energy density of ∼100 J/mm 3 combined with a hatching space of ∼150 μm was necessary to produce highly dense (> 99.9%) samples with a reduced number of processing-induced flaws (keyhole pores and lack-of-fusion defects) and with homogeneous microstructure. These samples manifested excellent mechanical resistance and hardness (UCS > 1000 MPa and HV > 450 HV0.3), but a very limited ductility, thus indicating the need for subsequent post-processing. Using printing parameters corresponding to the highest energy input (135 J/mm 3 ) resulted in a partial in-situ α’’ → α + β phase transformation. This phenomenon was attributed to a significant overlap between two subsequently melted tracks, which reduced the cooling rate in the solidified material and promoted the formation of stable α and β phases of titanium. This latter observation provided useful insight into the printing of functionally graded Ti6246 parts.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".