Hybrid Ti-MoS2 Coatings for Dry Machining of Aluminium Alloys
Bibliographic record
Abstract
Combinatorial deposition, comprising filtered cathodic vacuum arc (FCVA) and physical vapor deposition (PVD) magnetron sputtering is employed to deposit molybdenum disulphide (MoS2) and titanium (Ti) thin films onto TiB2-coated tool inserts specifically designed for the dry machining of aluminium alloys. Titanium is deposited by FCVA while MoS2 is magnetron sputtered. The deposition set up allows several compositions of Ti-MoS2 to be deposited simultaneously, with Ti content ranging between 5 and 96 at. %, and their machining performances to be evaluated. Milling took place using a CNC Vertical Machining Center at a 877 mm/min feed rate. The effect of different coating compositional ratios on the degree of aluminium sticking when a milling insert is used to face mill an Al alloy (SAE 6061) was investigated using a combination of energy-dispersive X-ray spectroscopy (EDX) and X-ray photoelectron spectroscopy (XPS) analysis. XPS studies suggest that the greater degree of Al sticking on the rake face of the inserts is due to the formation of greater amounts of non-protective Ti-O phases. EDX mapping of the milling inserts after machining reveal that a Ti:MoS2 ratio of around 0.39 prevents Al from sticking to the tool edges. Since we prevent Al from sticking to the tool surface, the resultant machined surface finish is improved thus validating the machining performance of TiB2-coated tools using optimum compositions of Ti:MoS2 thin film 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) | 0.001 | 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".