Tuning the mechanical properties and toughness of TiAlN coatings deposited by low duty cycle pulsed magnetron sputtering from a rotating cylindrical target
Bibliographic record
Abstract
Despite the promising capabilities, rotating cylindrical magnetron sputtering (CMS) has been relatively rarely described in the literature, especially regarding hard protective coating applications. In this study, we investigate the use of CMS in the pulsed dc mode with very low duty cycle (˂10 %) to prepare model TiAlN coatings at relatively high deposition rates (4 μm/h). We examine the impact of key parameters, specifically, substrate bias and substrate temperature on the coating microstructure and properties. Particularly, we focus on the residual stress in the coatings and their nanoindentation toughness, as these characteristics are crucial for the understanding of the film behavior and optimizing coating architectures. TiAlN coatings prepared with a substrate bias of about −100 V exhibit dense morphology, and high hardness (28–30 GPa), while the level of compressive stress in the coatings can be significantly reduced by increasing substrate temperature (from −5.6 GPa to −2.3 GPa at room temperature and 400 °C, respectively). Furthermore, we show a linear relationship between the residual stress in the TiAlN coatings and their nanoindentation toughness (1.1 MPa·m 1/2 to 5.1 MPa·m 1/2 ). Our findings demonstrate that both substrate bias and temperature can be effectively used to control the microstructure, mechanical properties, toughness and compressive stress level in the coatings, thereby opening a possibility to mitigate loading stress in various applications.
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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".