Regularities of tribooxidation and damageability at the early stage of wear of single-layer (TiAlCrSiY)N and multilayer (TiAlCrSiY)N/(TiAlCr)N coatings in the case of high-speed cutting
Bibliographic record
Abstract
We report a comparative study of single-layer (TiAlCrSiY)N and multilayer (TiAl-CrSiY)N/(TiAlCr)N PVD coatings on cutting tools during the break-in stage of high-speed dry cutting. Phase and chemical composition of tribooxides forming in the coating wear area were studied by X-ray photoelectron spectroscopy. It is shown that amorphous oxide films with a thickness of several dozen angstroms contain phases with a chemical composition close to mullite, sapphire, rutile, and chromium oxide. As a result of selective wear, the contact surface of the coatings retains the most durable tribooxides carrying out protective functions. Low-cycle fatigue resistance is studied using the cyclic microindentation technique. Fractal analysis of time-resolved indenter penetration depth curves combined with scanning electron microscopy (SEM) demonstrates phenomenological regularities of coatings’ damageability at the early stage of wear. It is shown that, in comparison with single-layer (TiAlCrSiY)N, the nucleation and growth of microcracks in a multilayer (TiAlCrSiY)N/(TiAlCr)N coating is accompanied by acts of microplastic deformation providing a higher fracture toughness of the (TiAlCrSiY)N/(TiAlCr)N multilayer nanocomposite.
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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".