DLC and DLC-WS2 Coatings for Machining of Aluminium Alloys
Bibliographic record
Abstract
Machine-tool life is one limiting factor affecting productivity. The requirement for wear-resistant materials for cutting tools to increase their longevity is therefore critical. Titanium diboride (TiB2) coated cutting tools have been successfully employed for machining of AlSi alloys widely used in the automotive industry. This paper presents a methodological approach to improving the self-lubricating properties within the cutting zone of a tungsten carbide milling insert precoated with TiB2, thereby increasing the operational life of the tool. A unique hybrid Physical Vapor Deposition (PVD) system was used in this study, allowing diamond-like carbon (DLC) to be deposited by filtered cathodic vacuum arc (FCVA) while PVD magnetron sputtering was employed to deposit WS2. A series of ~100-nm monolayer DLC coatings were prepared at a negative bias voltage ranging between −50 and −200 V, along with multilayered DLC-WS2 coatings (total thickness ~500 nm) with varying number of layers (two to 24 in total). The wear rate of the coated milling inserts was investigated by measuring the flank wear during face milling of an Al-10Si. It was ascertained that employing monolayer DLC coating reduced the coated tool wear rate by ~85% compared to a TiB2 benchmark. Combining DLC with WS2 as a multilayered coating further improved tool life. The best tribological properties were found for a two-layer DLC-WS2 coating which decreased wear rate by ~75% compared to TiB2, with a measured coefficient of friction of 0.05.
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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".