A Comprehensive Review of Cathodic Arc Evaporation Physical Vapour Deposition (CAE-PVD) Coatings for Enhanced Tribological Performance
Bibliographic record
Abstract
In the realm of industries focused on tribology, such as the machining industry, among others, the primary objective has been tribological performance enhancement, given its substantial impact on production cost. Amid the variety of tribological enhancement techniques, cathodic arc evaporation physical vapour deposition (CAE-PVD) coatings have emerged as a promising solution offering both tribological performance enhancement and cost-effectiveness. This review article aims to systematically present the subject of CAE-PVD coatings in light of the tribological performance enhancement. It commences with a comprehensive discussion on substrate preparation, emphasizing the significant effect of substrate roughness on the coating properties and the ensuing tribological performance. The literature analysis conducted revealed that optimum tribological performance could be achieved with an average roughness (Ra) of 0.1 µm. Subsequently, the article explores the CAE-PVD process and the coating’s microstructural evolution with emphasis on advances in macroparticles (MPs) formation and reduction. Further discussions are provided on the characterization of the coatings’ microstructural, mechanical, electrochemical and tribological properties. Most importantly, crucial analytical discussions highlighting the impact of deposition parameters namely: arc current, temperature and substrate bias on the coating properties are also provided. The examination of the analyzed literature revealed that the optimum tribological performance can be attained with a 70 to 100 A arc current, a substrate bias ranging from −100 to −200 V and a deposition temperature exceeding 300 °C. The article further explores advancements in coating doping, monolayer and multilayer coating architectures of CAE-PVD coatings. Finally, invaluable recommendations for future exploration by prospective researchers to further enrich the field of study are also provided.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".