Effect of Microstructure on Fracture Mechanisms in Galvannealed Coatings.
Bibliographic record
Abstract
Fracture mechanisms in galvannealed coatings have been studied by performing draw bead tests on galvannealed Ti stabilized interstitial free and Aluminum killed low carbon steel sheets and by investigating coating microstructures by scanning electron microscopy. Galvannealing treatments, on samples galvanized using an industrial hot-dip galvanizing process, were conducted at 450, 500 and 550°C for several time periods between 1 and 360 s in a laboratory induction furnace.In the coatings with low Fe content (up to 5 g/m2 ), the amount of powdering during the draw bead test was minimal. Growth of cracks nucleated within the δ1 phase was arrested at the steel-coating interfaces where only a limited amount of decohesion occurred. A steep increase in the amount of powdering was ob-servedin coatings with Fe contents between 6–9 g/m2 . In these coatings, cracks originating from the δ1 phase reached Γ–Γ1–δ1 phase boundaries, which provided preferential crack growth paths and thus facilitated fracture within the coating. A fracture mechanics model was proposed to account for the powdering resistance of galvannealed coatings.
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".