A 3D simulation of grain structure evolution during powder bed fusion additive manufacturing and subsequent laser rescanning process
Bibliographic record
Abstract
Laser rescanning is often used as a post-process treatment during Laser Powder Bed Fusion (LPBF) processes to improve product quality. Taking AlSi10Mg material as a case, this study presents a 3D mesoscopic Cellular Automaton (CA) model coupled with Finite Element Analysis (FEA) to simulate grain structure evolution during the Laser Powder Bed Fusion process and its subsequent laser rescanning treatments incorporating non-equilibrium effects under rapid solidification conditions. A key focus of our investigation centers on exploring the potential origins of grain refinement during the laser rescanning process, and the subsequent impact on the resultant grain structure. Our model introduces two key innovations: (i) a diffusion-based grain growth function that tracks composition redistribution during solidification, enhancing the accuracy of grain structure prediction, and (ii) a novel fusion boundary nucleation model that accounts for local composition variations, providing deeper insights into grain refinement mechanisms. By incorporating epitaxial growth, bulk nucleation and fusion boundary nucleation models, we have observed a mixed grain structure in the melt pool, mirroring experimental findings in other studies, delineated into three zones: fine grains at the melt pool boundary (Zone I), long columnar grains (Zone II), and fine equiaxed grains (Zone III). Two factors contributing to grain refinement in our model are presented: (i) Columnar to equiaxed transition (CET) and elevated cooling rate within the rescan melt pool; (ii) Extending volume of fine grains near the rescan melt pool boundary due to fusion boundary nucleation. As a result, laser rescanning treatments, notably, yielded a refined grain structure with approximately 20% reduction in grain dimensions and a pronounced texture under current process parameters. The implications of these findings hold potential for optimized Laser Powder Bed Fusion processes and grain refinement control in future applications. • Development of a 3D CA model with FEA integration for LPBF simulation and rescanning. • Simulation of microstructure evolution and composition redistribution. • Incorporation of bulk, fusion boundary nucleation, and epitaxial growth for accuracy. • Investigation of laser rescanning effects on LPBF grain structure and refinement. • Quantitative grain refinement analysis using Principal Component Analysis.
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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 | 0.001 | 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".