Orientation solidification map through laser scan strategy Engineering for additively manufactured stainless steels
Bibliographic record
Abstract
• 3D finite element model links scan strategy to grain shape and orientation in LPBF 316L. • Melting modes (conduction, transition, keyhole) and remelting govern grain texture dynamics. • A solidification map links G × R to crystal orientation, enabling process control in LPBF. Using a three-dimensional multiphase finite element model combined with a multiscale characterization framework, this study investigates the solidification characteristics and grain orientations of 316L stainless steel (316LSS) during laser powder bed fusion (LPBF) under different laser scanning strategies. Three common patterns—meander, stripe, and chessboard—were evaluated to capture the associated thermal profiles and melt pool (MP) dynamics. While conduction dominated across all strategies, the stripe and chessboard patterns showed greater susceptibility to keyhole and transition melting modes. Thermal simulations revealed variations in remelting depth: the stripe pattern exhibited the deepest remelting (∼150 μm), while the meander pattern showed the shallowest (∼50 μm), influencing grain refinement and morphology. Crystallographic orientation analysis indicated that interactions between keyhole, transition, and conductive MPs altered preferred growth directions. The meander strategy promoted [101] and [111] orientations, whereas the stripe and chessboard strategies showed stronger alignment toward [111]. By correlating thermal parameters—temperature gradient (G) and solidification rate (R)—with local crystallographic orientation and grain structure, a solidification orientation map was developed. This map demonstrates how scan strategy–driven MP interactions control microstructural features, providing a predictive tool for grain morphology and texture design in LPBF-316LSS components.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".