Microstructure simulation of maraging steel 1.2709 processed by powder bed fusion of metals using a laser beam: A cellular automata approach with varying process parameters
Bibliographic record
Abstract
Additive manufacturing via powder bed fusion of metals using a laser beam (PBF-LB/M) enables the fabrication of parts from metal powder with mechanical properties surpassing those of conventional processes. In this manufacturing process, a grain structure that is very sensitive to process parameter modifications and the resulting change in the temperature field is formed. Simulating the solidification microstructure is crucial for understanding process-microstructure relationships and creating digital twins for microstructure engineering and tailoring. This work introduces a cellular automata (CA) methodology for the microstructure simulation of maraging steel 1.2709 processed by PBF-LB/M. A high-resolution moving heat source model, based on the finite element method, was set up to capture the temperature field. The solidification microstructure was simulated by a two-dimensional CA model. Ten sets of process parameters have been used to produce single tracks experimentally to validate the CA model. The microstructure of these sets has been characterized by optical and scanning electron microscopy. The CA model successfully captures the essential solidification characteristics. The grain sizes have demonstrated a significant sensitivity to initial conditions. In-depth analysis has revealed that process parameters and thermal conditions, rather than the energy density, critically influence the grain size and the aspect ratio. Meanwhile, the grain alignment angle has shown no explicit dependency on process parameters, underscoring the complex dynamics governing microstructural evolution. This methodology paves the way for advancing material design in various industries by enabling a precise control over mechanical properties through microstructure tailoring.
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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) | 0.001 | 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".