Corrosion resistance of hot-dip Zn-6%Al-3%Mg alloy coated steel sheet used in automotive parts
Bibliographic record
Abstract
For the purpose of applying hot-dip Zn-6%Al-3%Mg alloycoated steel sheet (“Zn-Al-Mg”) to automotive parts, wecompared and investigated the corrosion resistance of Zn-Al-Mg and ordinary materials treated using a conventionalrustproofing method (“post-Zn-coated material”) exposedto accelerated corrosion test environments. We alsocollected automotive parts made from Zn-Al-Mg fromvehicles that had been driven for three to five years inCanada to examine corrosion resistance capabilities whenexposed to actual vehicle environment conditions. Wefound that Zn-Al-Mg exhibited better corrosion resistancethan post-Zn-coated material, even at portions where thesteel substrate was exposed (along cut edges and in bentor spot-welded portions). Such Portions of the Zn-Al-Mgwere observed as being covered by fine and dense Zncorrosion products containing Mg, which suppressescathode reactions (dissolved oxygen reduction reactions).As a result, elution of the coating layer around such portionswas suppressed and favorable corrosion resistancemaintained. The flat and bent portions of automotive partsmade of Zn-Al-Mg collected from actual vehicles werecovered by fine and smooth corrosion products, with onlylittle corrosion being observed. It was thus confirmed thatZn-Al-Mg also exhibits excellent corrosion resistance in anactual vehicle environment.
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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) | 0.000 | 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".