Analysis of post-weld heat treatments of AISI 2205 duplex stainless steel
Bibliographic record
Abstract
Duplex stainless steels are essential for many industries. Regularly used in highly aggressive environments, they often undergo a welding process, whether for joining components or repair operations. This process can modify the ferrite/austenite ratio and form secondary phases, impairing its properties. As a result, in some cases, it is recommended that post-welding heat treatment be carried out to restore the mechanical and metallurgical properties of the welded joint. In this work, the effect of solubilization heat treatment on the mechanical properties of duplex stainless steel welded joints by the autogenous Tungsten Inert Gas (TIG) process is studied. A systematic review was performed in the Scopus database to understand the effects of post-welding heat treatments on microstructure, hardness, tensile and impact behavior. Two solubilization temperatures were chosen through this review: 1050 °C and 1150 °C during 15 min. These thermal treatments were carried out in 3 tensile test specimens of each condition studied, including the welded joint without any heat treatment, called as-received condition. The heat treatments resulted in higher elongation and lower yield stress and stress strength. The one-way ANOVA showed no significant difference between yield strength, tensile strength and elongation between heat-treated conditions specimens. Although, the region where the specimens fractured varied. In the future, a microstructural characterization will be performed to understand the mechanical behavior observed
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| 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".