Effect of Interlayer Thickness on Nano-Multilayer Coating Performance during High Speed Dry Milling of H13 Tool Steel
Bibliographic record
Abstract
The TiAlCrSiYN-based family of physical vapor deposition (PVD) coatings were systematically designed through the incorporation of TiAlCrN interlayer to increase coating adhesion and consequently the tool life for extreme conditions that arise during dry high-speed milling of hardened tool steels. The investigation in the present paper intends to explain the effect of TiAlCrN interlayer thickness on the overall coating properties and cutting performance. A comprehensive characterization of the structure and properties of the coatings has been performed using focused ion beam (FIB), scanning electron microscope (SEM), X-ray powder diffraction (XRD), nanoindentation, ramped load scratch test, repetitive load wear test, and nano-impact test. The wear test at a subcritical load of 1.5 N showed that there was a gradual improvement in coating adhesion to the substrate as the interlayer thickness increased from 100 to 500 nm. However, the wear performance, being related to the ability of the coating layer to exhibit minimal surface damage under operation, was found to be associated with micro-mechanical characteristics (such as hardness, elastic modulus). Around a 40% increase in the cutting performance with 300 nm interlayer exhibited that a substantial increase in tool life can be achieved through interlayer thickness variation, by obtaining a balance between mechanical and tribological properties of the studied coatings.
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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".