Direct observation of the influence of grain orientation on the corrosion of pipeline steels
Bibliographic record
Abstract
This study aims to correlate the crystallographic texture of pipeline steels with their corrosion resistance. Electron backscatter diffraction (EBSD) was used to investigate the impact of grain orientation on the corrosion behavior of steels in various corrosive environments. Corrosion is influenced by several microstructural features, such as phase composition, grain size, grain boundary character, dislocation density, and crystallographic texture. To analyze the influence of specific grain orientations on corrosion resistance, steel samples were subjected to planned thermomechanical treatments to obtain samples with similar microstructures but different crystallographic textures. These samples were then exposed to controlled corrosive environments, and their microstructural changes were mapped using EBSD, scanning electron microscopy, and optical 3D profilometry. The results obtained indicated that corrosion response depends on grain orientations. Notably, the steel with more 〈111〉//ND and 〈100〉//ND grains exhibited improved corrosion resistance compared to the steel with a more random texture. The resistance of grains to active dissolution in all the corrosive solutions was in this order: {110} < {111} < {100}. The corrosion products were analysed using XPS, revealing variations in the composition and nature of the surface films on the samples across the selected electrolytes. • The corrosion behavior of specific grain orientations in pipeline steels was assessed in various environments. • Steel samples with similar microstructures but different crystallographic textures were examined. • SEM, EBSD, XPS and 3D optical profilometry were employed to map texture and grain-specific corrosion behavior. • <111>//ND and < 100>//ND texture exhibited superior corrosion resistance than random texture. • Corrosion resistance followed {110} < {111} < {100}, with <100>//ND being the most resistant to dissolution.
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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".