Remelting-based microstructure engineering in laser powder bed fusion: A case study in 316L stainless steel
Bibliographic record
Abstract
In laser powder bed fusion (LPBF), the formation of bulky columnar grains often results in undesirable mechanical anisotropy. Here, we demonstrate a new strategy to control the microstructure in LPBF through tuning melt pool overlaps without changing energy densities and scan patterns. Using 316L stainless steel as an example, we generate a wide range of grain sizes and morphologies. The underlying mechanism is associated with the retainment or elimination of newly nucleated grains at a melt pool during the formation of subsequent melt pools. The propensity of retainment or elimination of grains is largely dependent on the extent of melt pool overlaps because the grains are prone to nucleate at the free-surfaces of melt pool boundaries. This facile strategy could be applicable to a wide range of metallic alloys, paving a new way for microstructure engineering in additive manufacturing.
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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 | 0.001 | 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 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".