Adhesion Enhancement of Diamond Coating on WC-Co Substrates Through Interlayer Design
Bibliographic record
Abstract
Diamond coating with sufficient adhesion on WC-Co cutting tools is expected to significantly increase their cutting performance. However, the adhesion is always limited by the formation of graphitic soot in the interface due to the catalytic effect of Co on graphite formation. Moreover, the low nucleation density and the high thermal stress in the coatings also result in poor adhesion. The introduction of interlayer is one of the available approaches to enhance the coating-substrate interfacial adhesion. The goal of this project is to improve the adhesion through the optimization of interlayer design. The Al2O3 and Ta mono-interlayer, Al-Al2O3, Al-AlN, Al2O3-Ta and Al-Ta duplex interlayer systems have been developed in this study. These interlayer materials were prepared using a magnetron sputtering method, and diamond coating were deposited on them using microwave plasma enhanced chemical vapor deposition. In addition, different diamond seeding conditions have been studied to increase the diamond nucleation density. Grazing incident X-ray diffraction was carried out to determine the phase components in the Al-Al2O3 and Al-AlN interlayers. Raman spectroscopy and scanning electron microscopy were used to evaluate the quality, morphology and microstructure of the deposited diamond coatings. Rockwell C indentation testing was performed to evaluate the adhesion of the coatings. To elucidate the coating failure mechanism, the compositions around the delaminated spots of diamond coatings after indentation were identified by Energy-dispersive X-ray spectroscopy. To evaluate the tribological properties of the diamond coatings, the diamond coated WC-Co sheets were rubbed against steel and alumina balls respectively. The results show that continuous diamond coatings were achieved on Al2O3, Al-Al2O3, Al-AlN and Al-Ta interlayered substrates, whereas a graphite layer was still formed with the Ta monolayer or Al2O3-Ta duplex layer accompanied by an easy spallation of diamond coatings. The Al interlayer has played an important role in obtaining high purity diamond by in-situ forming an alumina barrier layer. Especially, the diamond coating deposited with an Al-AlN interlayer exhibits superior interfacial adhesion in comparison with all the other interlayers. Meanwhile, seeding with nano-diamond particles is more efficient than micro-diamond particles for improving the diamond nucleation density on Al-AlN interlayered substrates. Furthermore, the diamond coated WC-Co sheets possess lower coefficient of friction and wear rate than bare sheets when rubbing against either steel or alumina balls.
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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".