Effect of Coating Thickness on Wear Behaviour of Monolithic Ni-P and Ni-P-NiTi Composite Coatings
Bibliographic record
Abstract
Protective coatings can prolong the lifespan of engineering components. Electroless Ni-P coating is a very hard coating with high corrosion resistance, but low toughness. The addition of NiTi nanoparticles into the coating has shown the potential to increase the toughness of electroless Ni-P and could expand its usability as a protective coating for more applications. However, the study of the tribological behaviour and wear mechanisms of Ni-P-NiTi composite coating has been minimal. Furthermore, there is no studies on the effect of coating thickness on monolithic and composite electroless Ni-P coating wear behaviour. The wear rates of each coating were found by measuring the volume loss form multi-pass wear tests. The wear tracks were examine using a confocal microscope to observe the wear mechanisms. Each sample was tested using a spherical indenter and sharp indenter. It was found that the NiTi nanoparticle addition displayed toughening mechanisms and did improve the coating’s wear resistance. The 9 μm thick Ni-P-NiTi coating had less cracking and more uniform wear than the 9 μm thick Ni-P coating. For both the monolithic and composite coatings, their thicker version had higher wear resistance than their thinner counterpart. This was explained by the often observed trend in coatings where it has higher tensile stress near the substrate interface, which decreases and becomes compressive as thickness increases. Overall, the 9 μm thick Ni-P-NiTi coating had the highest wear resistance out of all the coatings tested.
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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 | 0.001 |
| 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".