Solidification microstructure selection maps for laser powder bed fusion of multicomponent alloys
Bibliographic record
Abstract
Abstract Solidification Microstructure Selection (SMS) maps provide a simple yet effective approach to predict the non-equilibrium solidification microstructure and grain morphology during Additive Manufacturing. In this study, SMS maps have been created for the Inconel 625 (IN625) alloy processed by Laser Powder Bed Fusion (LPBF). Toward this end, theoretical solid growth models, a model of the Columnar to Equiaxed Transition (CET), interface response theory, thermal simulation results and computational thermodynamics are utilized. The predicted microstructures are compared both qualitatively and quantitatively to experimentally-obtained micrographs. The theoretical analysis was also compared to the earlier analytical calculation for Al-10Si-0.5Mg alloy to show how differences in thermophysical properties affect the microstructural predictions. The theoretical predictions are shown to be in good agreement with the experimental results in terms of the resulting microstructure and dendrite arm spacings. A discussion on the use of SMS maps, formed over a broad range of thermophysical conditions, to help guide industry in improving LPBF microstructure, is provided.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".