Synergistic Effect of Corrosion and Wear for 6061 Aluminum alloy Electroless-plated with Ni-P and Ni-P-SiC layers
Bibliographic record
Abstract
6061 aluminium alloy blocks were electroless-plated with Ni-P alloy layer and Ni-P-SiC layer.The distribution of ceramic SiC particles and the thickness of Ni-P-SiC were quite uniform which ensure the wear protection of this composite layer.Electrochemical tests in 3.5% NaCl aqueous solution show a sequence of corrosion potential: Ni-P-SiC ＞ Ni-P ＞ 6061 Al-alloy ＞ anodic alumina film.On the other hand, the corrosion current density of these specimens shows a sequence of: anodic alumina film ＜ Ni-P ＜ Ni-P-SiC ＜ 6061 Al-alloy.The hardness measurements reveal a sequence of: Ni-P-SiC ＞ anodic alumina film ＞ Ni-P ＞ 6061 Al-alloy.The results of hardness are consistent with those of weight loss sequence after dry tests: Ni-P-SiC～ anodic alumina film ＜＜ Ni-P ＜6061 Al-alloy and corrosion-wear tests for 60 min: Ni-P-SiC～ anodic alumina film ＜＜ Ni-P ＜＜6061 Al-alloy.In fact, the weight loss of electrolessplated Ni-P-SiC layer and anodic alumina film on 6061 Al-alloy are lower than 1 mg after both dry wear and corrosion-wear tests for 60 min, indicating a sound protection effect.Furthermore, the weight losses of 6061 Al alloy after corrosion-wear tests in 3.5% NaCl aqueous solution increased about 3 to 5 folds in comparison to those of dry wear tested results.An obvious synergistic effect has been observed in this case.However, the weight losses of anodic alumina film increased only 1.5 to 2 folds for the wear tests added with corrosion effect.More exciting is that only slight difference of weight losses occurred after dry wear tests and corrosion-wear tests for both electroless-plated Ni-P-SiC layer and anodic alumina film on 6061 Al-alloy, implying an ignorable influence of corrosion on wear damage.
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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".