Plasma electrolytic oxidation (PEO) coatings on Mg-alloys for improved wear and corrosion resistance
Bibliographic record
Abstract
Due to their high chemical reactivity, relatively low melting point and low hardness, magnesium and magnesium alloys have relatively poor corrosion and wear resistance.Since both corrosion and wear are surface phenomena, a number of surface engineering techniques have been used to improve corrosion and wear performance.Whilst some surface hardening/strengthening methods have led to improvements in wear properties, they have not, in themselves, significantly improved the corrosion performance.Plasma electrolytic oxidation (PEO) has the potential to produce hard, compact oxide coatings that are well adhered to the magnesium alloy substrate.Such coatings can provide both improved wear and corrosion resistance.In this paper we describe how by changing the PEO processing parameters (substrate alloy; electrolyte; current or voltage; processing time) we can change the nature of the PEO oxide coatings (thickness; microstructure; porosity; phase content; composition) which, in turn, effects the corrosion and wear performance.All PEO-coatings have a three-layer structure with a porous outer layer, and intermediate dense layer and a thin inner dense layer.From a corrosion aspect, the performance of coatings is determined by the time taken for corrosion to initiate since this is much shorter than time taken for the coating to degrade.For PEO-coated Mg-alloys, this initiation time is primarily determined by the thickness, porosity and phase content of the inner dense layer at the coating/substrate interface.With respect to tribological properties, the coefficient of friction (COF) in dry sliding wear increases with increasing surface roughness of the PEO coatings.The wear rate is primarily determined by the thickness and hardness of the intermediate dense layer.
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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".