Effect of a helium gas atmosphere on the mechanical properties of Ti-6Al-4V alloy built with laser powder bed fusion: A comparative study with argon gas
Bibliographic record
Abstract
In metal additive manufacturing, the microstructures and associated mechanical properties of metal specimens can be controlled over a wide range. Although process parameters are considered important in the fabrication of functional parts, the effect of atmospheric gas has not been comprehensively documented. In laser powder bed fusion (LPBF), gas flow is used to eliminate fumes generated by laser irradiation. Simultaneously, the gas removes heat from the laser-irradiated part, which is exposed to high temperature. In this study, we investigated the capacity of helium as an alternative to argon, which is conventionally used as the LPBF atmosphere gas. He has a higher thermal conductivity and lower gas density than Ar, which may result in enhanced heat removal from the Ti-6Al-4V alloy during fabrication. Numerical simulations suggest a greater cooling rate under He flow. Further, the material built under He flow contained finer α' martensite grains and showed improved mechanical properties compared to those fabricated under Ar flow, despite the identical laser irradiation conditions. Thus, He gas is advantageous in LPBF for fabricating products with superior mechanical performance through microstructural refinement, and this is a result of its capacity for cooling and fume generation inhibition. Therefore, this study reveals the importance of the choice of atmospheric gas because of its effects on the characteristics of metallic specimens fabricated using LPBF.
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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".