Links between surface morphology changes and damage in a toughened epoxy adhesive
Bibliographic record
Abstract
With the increased use of toughened epoxy adhesives in current transportation lightweighting efforts, it is critical that damage mechanisms, such as strain whitening, are understood and quantified. Damage quantification is needed for the constitutive models used in structural design; however, thin bond lines in adhesive joints limit direct observation. In this study, microscope observations of bulk toughened epoxy adhesive specimens subjected to tensile loading were linked to damage. Cracks on the surface opened during loading, leading to strain whitening at the crack tips and the initiation and propagation of shear bands. The stresses approximated at the crack tips suggested that particle cavitation could be occurring in these regions. Changes in specimen stiffness were linked to crack growth and the formation of shear bands. Material damage calculated using traditional load-unload stiffness (D ~ 35%) was higher than other methods such as change in material strength (D ~ 18%) and damage from changes in stiffness during load-reload (D ~ 19%). The differences were attributed to short-term viscoelastic effects. A new approach calculated damage from the strain whitening on the free surface (D ~ 21%). Values were in agreement with damage figures from other methods. The technique can quantify damage over the loading history and identify areas of damage localization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".