Influence of steel substrate behavior on the deformation and cracking of Zn–Al–Mg coatings on galvanized steel sheets
Bibliographic record
Abstract
The objective of this study is twofold: (i) to comprehensively describe the deformation modes of zinc coatings depending on their crystallographic texture and (ii) to investigate the effect of Lüders banding occurring in the steel substrate on the coating deformation and cracking modes. Microscopic characterization and mechanical tests were conducted on three types of galvanized steel: a mild steel substrate and a high-strength low-alloy (HSLA) steel substrate known to exhibit the Lüders banding phenomenon, and a dual-phase steel substrate. The results reveal a direct correlation between the coating texture, plastic deformation modes, and the mechanical behavior of the respective substrates. Digital Image Correlation (DIC) was employed to measure the strain fields and characterize strain localization phenomena resulting from Lüders banding. The strain field measured on the galvanized substrate specimens showed that Lüders band propagation leads to accelerated plastic deformation and cracking in the Zn–Al–Mg coating. Finite element simulations were performed by considering the real coating microstructure and incorporating the macroscopic behavior of the substrate. The simulation results demonstrate that slip and twinning activities of the coating grains are strongly influenced by the underlying substrate behavior.
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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".