Microstructure, texture and mechanical properties of a nickel-free high nitrogen duplex stainless steel processed through friction stir spot welding
Bibliographic record
Abstract
A nickel-free high nitrogen duplex stainless steel holding high Mn-content was friction stir spot welded under the various rotational speeds. The microstructure/texture evolutions of the joints and the correlated mechanical properties were investigated in detail. The ferrite and austenite constituent massive phases were refined down to the mean grain size of 0.9 μm and 1.1 μm, respectively. The microstructure evolutions revealed the activation of continuous dynamic recrystallization as the main restoration mechanism. This was further verified through the formation of Β- and Α-fiber shear texture in the specimens processed under the various processing condition. Besides the activation of different restoration micro-mechanisms in both ferrite and austenite, dynamic phase transformation of ferrite to austenite was also contributed in grain refinement. The transformation was facilitated by providing more diffusional paths through substructure development and grain refinement. The local and bulk mechanical properties of the joints were also assessed. The outstanding tensile-shear peak load of 12.8 KN obtained for the sample welded at 400 rpm was discussed relying on the developed ultrafine-grained microstructure and the maximum peak extension of 1.7 mm achieved at 600 rpm was attributed to the role of ferrite phase in strain accommodation.
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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".