RECIPROCATING WEAR RESPONSE OF Ti(C,N)-Ni3Al CERMETS
Bibliographic record
Abstract
Titanium carbonitride (Ti(C,N)) cermets have become more popular in recent research\ndue to their mix of high hardness, high hot hardness, good ductility, chemical stability,\nand low densities. These mechanical properties make Ti(C,N)-cermets especially\ndesirable as a replacement for current ‘hardmetals’, such as tungsten carbide cobalt (WCCo),\nas it is known that WC-Co exhibits poor mechanical behaviour at elevated\ntemperatures. Additional interest and research has been conducted in reference to binders\nwhich enhance the cermet’s capability to retain strength at high temperatures while\nremaining ductile. One such binder, Ni3Al actually increases in yield strength up to a\ntemperature of ~900°C. In this thesis, the production method utilizing melt infiltration for\nTiC, Ti(C0.7,N0.3), Ti(C0.5,N0.5), and Ti(C0.3,N0.7)-based cermets with Ni3Al binder\ncontents of 20, 30 and 40 vol. % have successfully been developed and utilized. This\nprocess produced high density samples at each nitrogen content for all binder contents,\nexcluding Ti(C0.3,N0.7). Ti(C0.3,N0.7)-Ni3Al samples at 20 and 30 vol. % suffered from\npoor infiltration and could not be tested. The reciprocating wear mechanisms were\nexamined, using a ball-on-flat test, utilizing WC-Co spheres with a diameter of 6.35 mm\nas a counter-face, and test parameters of 20 Hz, 2 hrs., and applied loads of 20, 40, 60 and\n80 N. The wear tracks were examined using optical profilometry, SEM, and EDS to\ndetermine the volumetric wear rate, and the dominant wear mechanisms. The wear\nvolume, and wear mechanisms were compared with the effect of binder content, nitrogen\ncontent, and applied load.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".