Erosion-corrosion Assessment of Cr White Irons
Bibliographic record
Abstract
Abstract Erosion-corrosion possesses a serious problem for the mining and mineral processing industries. Handling and processing of silica-based solids results in extremely severe wear conditions. Simultaneous action of erosion, corrosion and their synergistic interactions accelerates the material damage. Chrome white irons (CWI) are extensively used in the oilsands industry for slurry handling equipment. Erosion-corrosion resistance of CWI’s depends on the chemical composition, matrix microstructure, types and volume fraction of carbides. In this study, high-Cr white irons were assessed using a slurry pot erosion-corrosion testing apparatus, where the total erosion-corrosion (E-C) rate as well as the separate components of synergy were determined. It was found that corrosion resistance of white irons largely depends on the amount of dissolved chromium in the matrix. On the other hand, erosion resistance is controlled by the type and volume fraction of carbides. During erosion-corrosion, corrosion enhanced erosion dominates the synergistic component. Material degradation rate depends on the involved wear mechanisms. SEM observation revels that cutting of the metal matrix by erodent at low impingement angles, micro-fracture of the carbides and spallation are the dominant mechanisms.
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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".