RESISTENCIA AL DESGASTE EROSIVO-CORROSIVO DE ACEROS AUSTENÍTICOS FERMANAL (EROSIVE-CORROSIVE WEAR RESISTANCE OF FERMANAL AUSTENITIC STEELS)
Bibliographic record
Abstract
Se obtuvieron aleaciones austeníticas del sistema Fe-Mn-Al, en el intervalo Fe-(4,9~11,0 wt% de Al)- (17,49~34,3 wt% de Mn)-(0,43~1,25 wt% de C), las cuales fueron fundidas en un horno de inducción a partir de materiales de alta pureza. Las aleaciones se evaluaron con respecto a fenómenos de corrosión, erosión en medio húmedo y corrosión-erosión, a un ángulo de impacto de 90º. Para la evaluación de la corrosión se empleó una solución compuesta por 0,5 M de NaCl y partículas de sílice con tamaño entre 210 y 300 µm, con el fin de analizar el efecto del contenido de manganeso y aluminio en la resistencia a la erosión y a la corrosión-erosión de estas aleaciones. Para la caracterización de la respuesta corrosiva se usó la técnica con curvas de polarización potenciodin·micas y la extrapolación de Tafel, la caracterización microestructural mediante microscopia electrónica de barrido (MEB) y los productos de corrosión a través de difracción de rayos X (DRX). Abstract: We obtained austenitic alloys of the Fe-Mn-Al, Fe in the range (4.9~11.0 wt% Al) - (17.49~34.3 wt% Mn) - (0, 43 ~ 1.25 wt% C), which were melted in an induction furnace from high purity materials. The alloys were evaluated with respect to corrosion, wet erosion and corrosion-erosion at an impact angle of 90°. For the evaluation of corrosion a solution composed of 0.5 M NaCl and silica particles with size between 210 to 300 microns was used in order to analyze the effect of aluminum and manganese content in the resistance to erosion and corrosion-erosion of these alloys. To characterize the corrosion, response technique was used by potentiodynamic polarization curves and using the same technique as Tafel extrapolation, the microstructural characterization by scanning electron microscopy (SEM), and the composition of corrosion products were analyzed using diffraction of X-rays (XRD).
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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".