Enhancing fatigue resistance of activated tungsten inert gas welded UNS S32750 super duplex stainless steel by optimizing its technological properties
Bibliographic record
Abstract
In engineering industries and heavy manufacturing plants, fatigue life of joints plays a pivotal role in determining the overall life span of the welded joint. In this paper, an advanced fusion joining technique, namely activated tungsten inert gas welding, was used for joining UNS S32750 super duplex stainless steel, with ZnO as activation flux. For the enhancement of fatigue resistance of joints, important welding process parameters were fluctuated according to a developed central composite design model. Empirical relationships were developed between the process parameters and the fatigue strength of the joints, which was correlated with the number of cycles to failure (NCF). Using analysis of variance, the significance of the developed fatigue model was ascertained. Using response surface methodology, optimization of process parameters for enhancement of fatigue resistance was done. It was observed that at the optimized activated tungsten inert gas weld process parameters of travel speed of welding torch at 69.85 mm/min, weld current at 125.20 A, and shielding gas flow rate at 14.77 L/min, a high fatigue life of 7.66396 × 108 NCF was obtained and the model was validated to very high predictability. Microstructural variations in the fatigue-tested specimens were evaluated for identifying the grain modifications.
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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) | 0.000 | 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".