Size Effect on Strength of Bi-Material Joints of Steel With Fiber-Polymer Composite
Bibliographic record
Abstract
Metal-composite joints between steel ribs and advanced fiber-polymer composites are an effective structural system for hybrid ship hulls. Similar joints are of interest for fuel-efficient aircraft. The current designs of such joints are generally based on the strength criterion, which ignores fracture mechanics. Aimed at an efficient and reliable design, this study investigates the size effect on the strength of these joints theoretically, numerically and experimentally. The analytical formulation of the size effect is asymptotically anchored at the large-size limit in linear elastic fracture mechanics (LEFM). The bi-material corner of the joint is shown to have a singular stress field with complex singularity. The strength of the joint is determined by the energy criterion for the macrocrack initiation at the corner, from which the large-size asymptote of the size effect law has been derived. A general approximate size effect law, spanning all sizes and various joint angles, is further derived via asymptotic matching. Numerical analysis with cohesive fracture model is used to design the experiments. Experimental studies involve the testing of geometrically similar hybrid joint specimens with the size ratio of 1 : 4 : 12. The analytical, numerical and experimental studies all indicate that the strength of bimaterial metal-composite joints is subjected to a strong size effect.
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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 | 0.001 |
| 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 | 0.001 |
| 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".