A 3D simulation of grain structure evolution during laser rescanning process of powder bed fusion additive manufacturing
Bibliographic record
Abstract
Abstract Laser powder bed fusion (LPBF) is an extensively used additive manufacturing process that can build metal parts with complicated geometric designs. However, because of the rapid solidification conditions and the layer-by-layer building, its application is challenged by products having poor surface quality and reduced mechanical properties. The laser rescanning process is often used as a refinement method during LPBF to improve the quality of products. In this study, grain structure formation during the LPBF laser rescanning process is modelled by a 3D cellular automaton based microstructure model coupled with finite element analysis. The coupled model considers different nucleation mechanisms, including epitaxial growth, which are applicable to rapid solidification in melt pool. The model is adapted to reproduce the grain structure and evolution of an Al-Si alloy manufactured by LPBF utilizing a laser rescanning strategy. The effects of laser remelting on the characteristics of the grain structure (i.e., the grain size, aspect ratio and orientation) are evaluated. The mechanisms that enable unique grain structure (i.e., grain refinement) are discussed.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".