Control over Multi-Scale Self-Organization-Based Processes under the Extreme Tribological Conditions of Cutting through the Application of Complex Adaptive Surface-Engineered Systems
Bibliographic record
Abstract
This paper features a comprehensive analysis of various multiscale selforganization processes that occur during cutting. A thorough study of entropy production during friction has uncovered several channels of its reduction that can be achieved by various selforganization processes. These processes are (1) self-organization during physical vapor deposition PVD coating deposition on the cutting tool substrates; (2) tribofilm formation caused by interactions with the environment during operation, which consist of the following compounds: thermal barriers; Magnéli phase tribo-oxides with metallic properties at elevated temperatures, tribo-oxides that transform into a liquid phase at operating temperatures, and mixed action tribo-oxides that serve as thermal barriers/lubricants, and (3) multiscale selforganization processes that occur on the surface of the tool during cutting, which include chip formation, the generation of adhesive layers, and the buildup edge formation. In-depth knowledge of these processes can be used to significantly increase the wear resistance of the coated cutting tools. This can be achieved by the application of the latest generation of complex adaptive surface-engineered systems represented by several state-of-the-art adaptive nano-multilayer PVD coatings, as well as high entropy alloy coatings (HEAC).
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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".