Tribological behaviour of a continuous hot dip galvanized steel
Bibliographic record
Abstract
The aim of this work is to investigate the tribological behaviour of a continuous hot dip galvanized steel. This paper presents a fundamental study of the characteristics of zinc coating in terms of morphology, surface roughness and tribological behavior according to process parameters typical of industrial processes continuous galvanization. The morphology of the zinc coating was observed by scanning electron microscope (SEM), optical microscopy, and the mechanical properties of the coating layers were determined by nanoindentation. The tribological tests were carried out on a rotating ball-disk tribometer under loads of 1, 2, 3 N with a sliding distance of 15, 30 and 50 m. The results showed a marked increase of the coefficient of friction with increasing applied load. Under the same conditions, wear slightly increased due to the hardness of intermetallic phases. The results presented show that heating promotes the diffusion of iron in the zinc coating giving shape to a binary alloy Fe–Zn whose characteristics depend on the parameters; moreover, it is proved that the tribological characteristics of the surface of the metal blank in terms of coefficient of friction depend on the temperature of the contact pressure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".