Friction Stir Welding of Super Duplex Stainless Steel of UNS S32750, Its Microstructure Evaluation, Mechanical and Corrosion Properties
Bibliographic record
Abstract
Super duplex stainless steel is a kind of duplex stainless steel that has pitting resistant equivalent number over than 40. Unified Numbering System (UNS) S32750 is a common super duplex stainless stee, that is mostly applied in oil and gas refinery industries, because of its proper corrosion-resistant properties . Therefore, joining of these steels by welding is very important, but the greatest problem in this regard is the corrosion and decrease in mechanical properties after welding.. In this research, UNS S32750has been joined by friction stir welding method. The tool being used in this research was a WC with 16mm shoulder diameter, 5 mm pin diameter, and 1.9 mm height. X-ray diffraction showed that harmful phases, such as sigma or chi have not been formed. Microstructure study indicated that grain size in the stir zone has decreased. Vickers Hardness Test Method has been applied on welded samples. Moderate microhardness of base metal was 285 Vickers but, the microhardness increased in the stir zone to 360 Vickers, because of decreasing the grain size. The cyclic polarization determined that potential and corrosion current of joint metal by friction stir welding method was similar to base metal. Also, it was revealed that ferrite percentage in the stir zone doesn't decrease very much because the friction stir welding heat input is very low and the colding rate is very high.
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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".