Fretting wear of lubricated DLC coating systems
Bibliographic record
Abstract
Diamond-like Carbon (DLC) coatings are well known for their use in protection against fretting wear due to their low friction and wear properties. DLCs are metastable, allowing them to graphitise under applied load to create a graphitic transfer layer which reduces friction. Their high intrinsic residual stresses also enable them to resist cracking effectively under fretting. Few studies have analysed the lubricated fretting performance of DLC coating systems. This work focuses on a series of lubricants with different additives and friction modifiers to explore their effects. This study analyses the performance of a DLC coating system (a-C:H) applied to hardened M2 tool steel and 316L stainless steel under loads of 20 and 40 N under dry fretting and lubricated fretting conditions. Lubricated uncoated substrates were also analysed for comparison. The counterfaces used were 10 mm diameter 52,100 steel balls. The lubricants tested included a base oil and a fully formulated oil, with and without the addition of MoDTC. Gross slip fretting was achieved using a bespoke electrodynamic shaker unit. Nanoindentation was employed to measure the mechanical properties of the coatings and substrates. Contact pressure and lubricant type had significant effects on the running-in behaviour of the coatings. Increased contact pressure led to instability in the running-in period. Lubrication reduced the dissipated energy in the contact, thereby decreasing wear. However, fully formulated oils and those containing MoDTC performed worse due to their higher viscosity, which impacted oil entrainment in the contact area. This study provides insights into the lubricated fretting performance of DLC coatings showing that these coatings can perform well, with the potential for further improvements with optimisations to the lubricant to the system. Performance improvements can be gained in automotive components such as high pressure bearings and gears.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".