Modelling of curing kinetics of amine cured epoxy resins for vacuum assisted resin infusion molding
Bibliographic record
Abstract
Abstract Epoxy resin, along with a suitable reinforcement such as glass, is used to manufacture windmill blades by vacuum assisted resin infusion molding (VARIM) process. For the simulation of VARIM process, the sub models are required to describe the rheological and thermochemical behaviour of the epoxy resin. In the present paper, the curing kinetics of amine cured epoxy resin, which is mostly used for wind blade manufacturing, are presented. The kinetic study is performed by measuring the exothermal heat generated during the curing of the amine cured epoxy resin system, using Differential Scanning Calorimeter (DSC), at different temperatures. The range of the temperature for the study is selected between the reaction onset temperature, which is about 70°C, and the peak reaction temperature, which is nearly 120°C. The reaction exotherm, as measured by DSC, is processed to obtain the reaction kinetic data such as the degree of reaction and the rate of reaction at different times and temperatures. A suitable model is proposed to describe the reaction kinetic data obtained experimentally. The unknown parameters of the models are determined by a nonlinear regression analysis on experimental data, while the kinetic rate constants are obtained based on Arrhenius Law. The proposed model is also compared with the models available in the literature. It is found that the proposed model is the simplest model, which accurately captures both the degree of cure and rate of cure qualitatively and quantitatively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".