Features of the Oxidation of Multilayer (TiAlCrSiY)N/(TiAlCr)N Nanolaminated PVD Coating during Temperature Annealing
Bibliographic record
Abstract
A nano-multilayer Ti0.2Al0.55Cr0.2Si0.03Y0.02N/Ti0.25Al0.65Cr0.1N PVD coating was deposited on Kennametal carbide K 313 inserts. These coatings are widely used to protect cutting tools under severe exploitation conditions. Under equilibrium conditions, it was found that the Al2O3 oxide possessed better adhesive properties than the TiO2. The addition of chromium further enhanced the oxidation resistance of the coatings. Silicon significantly increased the oxidation resistance of this type of coating. The properties of the diffusion process in this coating have not been sufficiently investigated, despite the considerable number of articles published on this topic. For the purpose of this study, a multilayer ion-plasma (TiAlCrSiY)N/(TiAlCr)N coating was oxidized under equilibrium conditions; its chemical inhomogeneity was studied by time-of-flight mass spectroscopy using a TOF SIMS5-100 instrument. The data was collected from an area of 100 × 100 µ. A D-300 profilometer (KLA-Tencor Corp., Milpitas, California 95035, USA) was used to determine the rate of ion etching. It was found that oxidation commenced at the surface nanolayer of a TiAlCrN nitride, forming loose films of Cr2O3, TiO2, and Al2O3 oxides. This passivating film had a thickness of around 140 nm. For the first time, the interlayer diffusion coefficients of Si and Y were determined in multilayer coatings based on Ti0.2Al0.55Cr0.2Si0.03Y0.02N/Ti0.25Al0.65Cr0.1N, under open air annealing at 700 °C. The physical nature of the differences in the diffusion of these elements is discussed. The diffusion rate in the near-surface volumes was lower than in the deep layers of the multilayer coating, most likely due to the formation of passivating oxide films on the surface.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".