The Relationship between Cyclic Multi-Scale Self-Organized Processes and Wear-Induced Surface Phenomena under Severe Tribological Conditions Associated with Buildup Edge Formation
Bibliographic record
Abstract
This paper presents experimental investigations of various interrelated multi-scale cyclic and temporal processes that occur on the frictional surface under severe tribological conditions during cutting with buildup edge formation. The results of the finite element modeling of the stress/temperature profiles on the friction surface are laid out. This study was performed on a multilayer coating with the top alumina ceramic layer deposited by CVD (chemical vapor deposition) on a WC/Co carbide substrate. A detailed analysis of the wear process was conducted by 3D wear evaluation, scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM/EDS) and electron backscattered diffraction (EBSD), as well as X-ray photoelectron spectroscopy (XPS) methods. The following cyclic phenomena were observed on the surface of the tribo-system during the experiments: a repetitive formation and breakage of buildups (a self-organized critical process) and a periodical increase and decrease in the amount of thermal barrier tribo-films with a sapphire structure (which is a self-organization process). These two processes are interrelated with the accompanying progression of cratering, eventually resulting in the catastrophic failure of the entire tribo-system.
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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".