Effect of process parameters and grain refinement on hot tearing susceptibility of high strength aluminum alloy 2139 in laser powder bed fusion
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Bibliographic record
Abstract
Abstract High strength aluminum alloys, especially those that are age-hardenable, such as 2xxx series, 6xxx series, and 7xxx series, are widely used as structural materials in transport and aerospace industries due to their good mechanical properties. However, additive manufacturing of high strength aluminum alloys is challenging due to their susceptibility to hot tearing. In this work, a systematic study has been conducted in an attempt to eliminate hot tearing in laser powder bed fusion (PBF-LB/M) manufacturing of Al2139 alloy through selecting process parameters and the addition of an AlTiB grain refiner. It was found that hot tearing in Al2139 during PBF-LB/M can be reduced or eliminated by increasing volumetric energy density. Furthermore, grain refinement by AlTiB addition shows a clear effect in reducing hot tearing, even though the refined grains remain predominantly columnar rather than equiaxed. The effect of increasing volumetric energy density on hot tearing of Al2139 during PBF-LB/M was analyzed by thermomechanical finite-element simulation, which showed that the reduction in hot tearing with increasing energy density is associated with a decrease in the thermal residual stress. However, it was also shown that there can be a substantive loss of Mg due to evaporation at high energy densities. From a hot tearing model based on solidification thermodynamics, the loss of Mg on hot tearing in Al2139 was estimated to reduce hot tearing by up to 10%, compared to the initial powder composition. With the selected PBF-LB/M parameters, crack-free tensile specimens were fabricated, with and without the addition of AlTiB. Compared with their wrought or cast counterparts, the PBF-LB/M Al2139 and Al2139-AlTiB specimens show lower yield strength but better ductility, which can be attributed to the loss of Mg during PBF-LB/M.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it