Cutting Performance of Low Stress Thick TiAlN PVD Coatings during Machining of Compacted Graphite Cast Iron (CGI)
Bibliographic record
Abstract
A new family of physical vapor deposited (PVD) coatings is presented in this paper. These coatings are deposited by a superfine cathode (SFC) using the arc method. They combine a smooth surface, high hardness, and low residual stresses. This allows the production of PVD coatings as thick as 15 µm. In some applications, in particular for machining of such hard to cut material as compacted graphite iron (CGI), such coatings have shown better tool life compared to the conventional PVD coatings that have a lower thickness in the range of up to 5 μm. Finite element modeling of the temperature/stress profiles was done for the SFC coatings to present the temperature/stress profiles during cutting. Comprehensive characterization of the coatings was performed using XRD, TEM, SEM/EDS studies, nano-hardness, nano-impact measurements, and residual stress measurements. Application of the coating with this set of characteristics reduces the intensity of buildup edge formation during turning of CGI, leading to longer tool life. Optimization of the TiAlN-based coatings composition (Ti/Al ratio), architecture (mono vs. multilayer), and thickness were performed. Application of the optimized coating resulted in a 40–60% improvement in the cutting tool life under finishing turning of CGI.
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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) | 0.000 | 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".