Finite Element Analysis of Crack Propagation in Adhesive Joints with Notched Adherends
Bibliographic record
Abstract
The adherends notching technique has been the subject of a few recent studies and consists of tailoring the geometry of the adjoined layers to mitigate the bondline peak stresses and enhance the joint strength. In the present study, we explored the effect of the adherends notching technique on crack propagation using finite element (FE) simulations based on the cohesive zone model (CZM) of fracture. Double cantilever beam (DCB) adhesive joints subjected to quasistatic loading were considered as a model material system. An array of equally spaced notches was placed on the faying sides of the adherends, oriented perpendicularly to the direction of crack growth. A parametric investigation was carried out to ascertain the role of the notches and the input cohesive properties on various performance metrics, e.g., load-displacement response and dissipated energy. The proposed notching strategy promotes an unstable crack pinning/depinning process, which effectively delays crack growth and increases the effective work of fracture. Additionally, we found that the overall behaviour is tunable by changing geometric (i.e., notch spacing and depth) and bondline material properties.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".