Characterization of Electroless Ni-P and Ni-P/Al<sub>2</sub>O<sub>3</sub> Composite Coating on Al6061 Alloy
Bibliographic record
Abstract
Aluminum and its alloy have a light density with good properties of stiffness and corrosion resistance. Although it has a good property, aluminum alloy still vulnerable to wear and corroded. A possible solution to protect the aluminum product from wear and corrosion is by applying coating such as nickel-phosphorus (Ni-P) coating onto its surface. Co-deposition of alumina (Al 2 O 3 ) particles into Ni-P coating is expected to improve the wear resistance of the Ni-P coating layer. This study investigated the Ni-P and Ni-P/Al 2 O 3 composite coating on Al 6061 alloy via electroless coating by varying the agitation speed and amount of Al 2 O 3 . The characterization of coatings includes morphology, coating thickness, hardness and wear properties were investigated by using field emission scanning electron microscopy (FESEM), optical microscope, Vicker microhardness and pin-on-disc tribometer. The agitation improved the adherence of alumina particles to the substrate. The increment amount of alumina in Ni-P/Al 2 O 3 composite coating showed a decrement in percentage of nickel and phosphorus present in the coating but improved the hardness of the coating. The wear rate and coefficient of friction of the Ni-P/Al 2 O 3 composite coating were lower than Ni-P coating.
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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".