Multiaxial elastic, yield and failure behaviour of bonded joints using a hot-curing epoxy film adhesive: analytical and experimental investigation
Bibliographic record
Abstract
Film adhesives provide an excellent method for joint manufacturing with minimum waste and highly accurate thickness. Here, the multiaxial behaviour in terms of elasticity, yielding and failure of a hot curing epoxy film adhesive was analytically and experimentally investigated. Butt, scarf and thick adherend shear test joints were tested under static conditions. The variation between normal and shear stress among the different joint types allowed the variation of the multiaxiality using the same experimental set-up with a uniaxial testing machine. A consistent set of elastic constants was obtained by carefully calibrated displacement sensors, proper consideration of adherend elasticity and of lateral strain restrictions. The presentation of the von Mises equivalent stress and the hydrostatic pressure at the point of yield and failure indicated that hydrostatic stresses have a large influence on the static strength of the film adhesive. Failure and yield conditions were formulated based on linear or exponential forms of Drucker-Prager type models. A strain-based failure hypothesis was formulated using the second invariant of the strain deviator, and the first invariant of the strain tensor. Results showed that high values of volumetric strain are detrimental to the strength of the adhesive joints.
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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 | 0.000 | 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".