Data-driven phase-field modeling for additively manufactured Inconel 617: Transformative insights for small modular reactors
Bibliographic record
Abstract
This study examines the microstructural evolution and thermal-fluid behavior of Inconel 617 during laser-directed energy deposition additive manufacturing, focusing on temperature distribution, surface tension, and melt pool dynamics. A monolithic phase-field model, integrated with CALPHAD-informed thermodynamic data, is developed to predict solidification processes and surface tension variations. Results indicate that laser power critically influences thermal gradients, melt pool stability, and defect formation. Higher laser power increases thermal gradients, reducing surface tension and expanding melt pools, while lower laser power results in more stable surface tension and reduced defect risks. The temperature field is analyzed along and perpendicular to the laser movement, highlighting vaporization thresholds and melt pool geometry in governing material behavior. Surface tension consistently decreases near the laser interaction region, influenced by local thermal gradients. These findings contribute to process optimization—ensuring defect-free, corrosion-resistant Inconel 617 components with optimized microstructure and minimal residual stress for high-temperature applications, including small modular reactors.
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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 | 0.001 | 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".