Heat and mass transfer in spatially oscillating laser powder bed fusion
Bibliographic record
Abstract
Spatially oscillating laser powder bed fusion (SO-LPBF) presents an attractive approach to dynamic beam shaping, fundamentally altering what is possible in terms of heat and mass transfer during laser-based metal 3D printing. This study offers a systematic process characterisation of SO-LPBF, employing in-situ multimodal imaging to capture detailed melt pool and spatter dynamics. Ex-situ profilometry, metallurgical characterisation and EBSD analysis show the effect of beam stirring on the deposited bead geometries and microstructures. We develop comprehensive process maps by correlating our observations with dimensionless parameters, effective fluence metrics, and semi-analytical modelling. Our findings reveal that properly tuned oscillation parameters create a "thermal reservoir" effect, enhancing melt pool stability and suggesting the potential of processing thicker powder layers. This leads to a potential doubling of productivity with existing technology and lays the groundwork for further scaling. The detailed insights and scaling guidelines presented here serve as a valuable resource for optimising SO-LPBF, advancing it as a highly efficient and versatile additive manufacturing technique.
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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".