A novel cohesive zone modelling approach to represent mixed mode loading and bond line thickness effects
Bibliographic record
Abstract
Accurate representation of the traction–separation response for mixed mode loading in a cohesive zone model (CZM) is critical to predicting the response of adhesive joints in a number of applications, including transportation and vehicle crashworthiness. Traditionally, the Mode I and Mode II responses are treated independently, with mixed mode response determined by relationships between the degree of mode mixity and separation, potentially leading to overprediction of the plateau traction and underprediction of the plateau length in mixed mode loading. This poor fit is due to the indirect relationship between mixity and traction and having minimal fitting options for separation-to-plateau and softening. To address this limitation, a mixed mode CZM approach is proposed, based on measured mixed-mode traction–separation results for a toughened epoxy adhesive. The effects of bond-line thickness were considered, to examine the ability of the proposed approach to include additional effects (beyond mode mixity) that are known to affect the traction–separation response. The CZM implementation was assessed using the original test data and was shown to capture the measured experimental traction–separation response across a range of mixed mode loading and bond line thickness more accurately compared to traditional CZM treatments.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".