Fretting wear behaviour of Zn-Ni alloy coatings
Bibliographic record
Abstract
Cadmium coatings are used in the aerospace industry primarily as a corrosion resistant plating, but also in applications where tribological properties are also important.Due to the carcinogenic nature of Cd, many coatings have been proposed as replacements, with Zn-Ni being the leading candidate.In this study, we examine two Zn-Ni coatings with differences in their surface roughness and their content of through thickness defects, including pores and cracks.The morphological differences between the coatings had a noticeable effect on their fretting wear behaviour.A customized tribometer, with a reciprocating rounded pin (AISI 440C steel) on flat (Zn-Ni) geometry, was used to perform fretting wear tests.The two morphologically different Zn-Ni coatings were tested at room temperature using ±70, 100 and 150 μm displacements and 133 N and 447 N constant normal loads.The surface of the wear scar was analysed using scanning electron microscopy coupled with energy dispersive X-ray spectroscopy for changes in morphology and chemistry.Wear volume was measured from surface profiles obtained using confocal microscopy.Hysteresis fretting loops of the tests showed that for both coatings, at ±70 μm displacement remains in no slip condition, at ±100 μm in the mixed slip condition, and at ±150 μm displacement remains in gross slip condition.Although the coatings had similar stick-slip behaviour, the smoother coating has a slower progression of wear from the no slip to gross slip conditions than the rougher coatings.Also, differences in the wear scar morphologies are attributed to the differences in the coating morphologies, which resulted in different wear and velocity accommodation mechanisms.
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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 | 0.000 | 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) | 0.002 | 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".