Tribological behaviour of the green anode paste with a steel plate at 150 °C
Bibliographic record
Abstract
Abstract In order to accurately predict the mechanical behaviour of paste during forming process, the friction law between the carbon paste and the mould wall is an important parameter to be determined. This paper presents the tribological behaviour of the lubricated paste/steel interface subjected to high stress conditions at the anode forming temperature of 150 °C. A method to characterize the tribological behaviour has been developed and an apparatus was built. The method is based on the comparison of two successive experiments. In the first experiment, the paste is in contact with the friction plate. In the second one, a layer of Teflon is placed under the paste in order to excite another parameter thereby allowing the identification of the friction coefficient between the paste and steel wall. These experiments were performed with a paste under different normal loads. The static and kinetic friction coefficients of the Teflon/steel, steel/steel and paste/steel interfaces have been estimated. The static and kinetic friction coefficients of the Teflon/steel are respectively 0.17 and 0.13. The steel/steel friction coefficients were evaluated twice which gave a static coefficient that varies between 0.22 and 0.30. The kinetic coefficient varies between 0.18 and 0.25. The static and kinetic paste/steel friction coefficients obtained from both experiments are clearly similar. Their values are 0.15 and 0.13 respectively.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".