Wear and Fatigue Behavior of PVD and MTCVD TiCN Coated Cemented Carbide Inserts in Turning Cast Iron
Bibliographic record
Abstract
TiCN coatings of the same chemical compositions were deposited on HW/K05-K20 cemented carbide inserts via physical (PVD) and medium temperature chemical vapor deposition (MTCVD) techniques. Nano-indentations coupled with appropriate FEM simulations were used for characterizing the film and substrate mechanical properties. Furthermore, uncoated cemented carbide substrates were annealed in vacuum at temperatures and durations corresponding to the related ones during the PVD and MTCVD process for recording the effect of the deposition temperature and duration on the substrate strength properties. Perpendicular and inclined impact tests at various loads were performed for checking the coating fatigue endurance and adhesion respectively. These material data were considered in FEM supported calculations for predicting the developed stress fields in the cutting edge during turning cast iron GG30 using the PVD and MTCVD TiCN coated inserts. According to the obtained result, both coatings possess the same stress-strain properties. Hereupon, the MTCVD coatings are characterized comparably to PVD ones by improved fatigue properties and adhesion strength. Although these properties contribute to an increased tool life in finishing turning, the significant reduction of the substrate strength properties, due to the elevated temperature during the MTCVD process, results in a premature coating failure and a consequent intensive wear evolution in roughing.
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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".