Quantification of δ-ferrite and austenite retention in laser-deposited martensitic stainless steel
Bibliographic record
Abstract
Type 420 martensitic stainless-steel powder was clad onto a CF3M austenitic stainless steel substrate using laser direct-energy-deposition (L-DED). Through comprehensive characterization and numerical simulation, the evolution of microstructures, particularly the retention of δ-ferrite and austenite, was investigated. In the clad fusion zone, the primary phase to solidify was δ-ferrite, followed by austenite resulting from the peritectic reaction. The segregation patterns developed during rapid solidification from the L-DED process exerted a significant effect on the stability of the austenite in the room temperature microstructures. Due to the fast cooling rate in the solid state, the time available for transformation of the δ-ferrite to austenite regarding the segregation patterns was limited, and the transformation was incomplete. The room temperature microstructure was therefore comprised of δ-ferrite distributed in the dendrite core regions, surrounded by martensite, which was further surrounded by a small fraction of retained austenite in the interdendritic regions. The retained austenite region was enriched with segregated alloying elements, which pushed the martensite start temperature Ms below room temperature. The retention of both δ-ferrite and interdendritic austenite was proved to have caused a softened fusion zone with a lowered martensite fraction.
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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 | 0.000 | 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".