PVD coating strategies: Developing a combination of AlCrN and AlTiSiN for enhanced surface performance during threading of super duplex stainless steel
Bibliographic record
Abstract
Super duplex stainless steel (SDSS) is one of the difficult-to-machine materials due to its high tendency to work-harden and low thermal conductivity. According to recent findings, PVD hard coatings based on Al, Cr, and Ti are recommended for SDSS machining. In this work, three different PVD coating systems were applied for the threading process of SDSS. Monolayer Al50Cr50N, Al60Cr40N, and multilayer Al60Cr40N/Al50Ti45Si5N were deposited on cemented carbide inserts. This paper highlighted the effect of alloying and coating architecture design on the cutting tools' mechanical properties, tribological characteristics, and wear performance. A novel balanced combination of AlCrN/AlTiSiN multilayer coating with a Si content of 5 at. % was proposed, and it exhibited improved adhesion, beneficial mechanical properties, and superior cutting tool life. Furthermore, tribological characteristics under extreme environments were analyzed through a heavy-load, high-temperature tribometer, as well as XPS and AES measurements. The surface integrity of workpiece material machined by various coatings was also examined through microstructure, microhardness, and residual stress measurements. The depth profiles of residual stress reveal that the machining process significantly impacts the outcome, influenced by the tribological properties of the coatings. Specifically, the AlCrN/AlTiSiN coating exhibits a minimal effect on the surface. These investigations enhance the comprehension of the mechanisms that cause material mechanical surface transformation in threading operations, contributing to a deeper understanding of machining processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".