Comparing the Cavitation and Slurry Erosion Wear Resistance of 16Cr-5Ni Stainless Steel With 13Cr-4Ni CA6NM Stainless Steel
Bibliographic record
Abstract
Abstract In the current work, 16Cr-5Ni stainless martensitic cast steel was evaluated in cavitation and slurry erosion tests under different thermal aging treatments (TATs) using an ultrasonic vibratory cavitation apparatus and an in-house-designed jet slurry tribometer. The steel was homogenized at 1100 °C for 40 h and then thermal ageing was performed at 475 °C, 550 °C, and 625 °C for 4 h. The cavitation test results showed a lower wear-rate was obtained under TAT at 475 °C, followed by TAT at 550 °C, and a higher wear-rate was found under TAT at 625 °C. A good correlation was established between hardness and the maximum erosion rate in the cavitation results. In the slurry tests, the jet stream contained a fixed mass fraction of 1.25 wt% sand. The evaluated impingement angles were 45 deg and 90 deg, and better performance was obtained under TAT at 475 °C and TAT at 550 °C. The results for the thermal aging of 16Cr-5Ni were compared with those of traditional CA6NM (13Cr-4Ni) steel, which is widely used in the manufacturing of turbine runners. Under every condition evaluated, 16Cr-5Ni presented a cavitation erosion resistance value higher than that of CA6NM, and the slurry erosion resistance of both steels was very similar when 16Cr-5Ni under TAT at 475 °C or 550 °C was compared with CA6NM. Therefore, 16Cr-5Ni stainless martensitic cast steel could be another alternative to the promising results obtained for the manufacturing of turbine runners.
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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 | 0.001 |
| 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".