An alternative model for predicting the cure kinetics of a high temperature cure epoxy adhesive
Bibliographic record
Abstract
Abstract The purpose of this work was to develop a cure kinetics model for a commercially available high temperature cure epoxy adhesive commonly used in the aerospace industry. While there are several phenomenological cure kinetic models commonly used in the literature for describing the rate of conversion of thermosetting epoxy adhesives as a function of degree of conversion, none of these models adequately depicts the adhesive used in this work over the entire range of conversion. Hence, by curve fitting empirical data collected using differential scanning calorimetry and refinement of existing models, an alternative model is proposed. The form of the present model suggests that chemical curing is the result of the combination of two autocatalytic reactions. The model is able to account for both the chemically controlled and diffusion controlled regimes of the cure. This paper also describes a novel iterative approach for predicting kinetics parameters as a function of isothermal cure temperature. Excellent agreement between experimental measurements and model predictions has been demonstrated over the entire range of conversion.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".