Cellular Automaton simulation of grain and sub-grain evolution with eutectic growth in laser scanned and rescanned Al–10Si, with experimental validation
Bibliographic record
Abstract
This study presents a three-dimensional Cellular Automaton (3D CA) model incorporating eutectic growth mechanisms to simulate grain and sub-grain structure evolution in an additive manufacturing (AM) scenario, with surface laser rescanning of Al–10Si alloy as a case study. Unlike conventional CA models in AM, this work introduces a eutectic solidification framework within the CA approach, allowing dynamical transitions between dendritic and eutectic growth modes based on local thermal and solute conditions. The CA model integrates finite element analysis (FEA)-derived thermal data, solute redistribution tracking, nucleation behaviors, and growth kinetics under rapid solidification conditions in laser scanning and rescanning. The simulation results predict key microstructural phenomena, including dendritic-to-eutectic transitions, grain refinement resulting from laser rescanning, and the formation of submicron-scale eutectic cellular structures. These findings have been rigorously validated against specimens fabricated via laser scanning AM. Overall, the developed CA model provides a robust predictive tool for understanding and optimizing microstructural evolution in AM processes, offering valuable insights for tailoring processing parameters and alloy compositions to achieve desirable mechanical properties in Al–10Si and other alloy systems. • Developed a 3D CA model for dendritic–eutectic transitions in Al–10Si under AM conditions. • Coupled CA with FEA thermal data for grain growth simulation and solute tracking in solidification. • Laser rescanning promotes grain refinement and reduces crystallographic texture intensity. • Model predicts sub-grain eutectic structures in Al-10Si consistent with experiments in AM conditions. • Experimental results validate grain refinement and morphology predictions by the model.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".