A thermochemical and rheological model incorporating inhibition time for highly reactive polyester resins in liquid moulding processes
Bibliographic record
Abstract
This work presents the development of a comprehensive model to describe the cure kinetics and viscosity behaviour of polyester-based resin systems used in liquid composite moulding applications. The model accounts for both inhibition and diffusion effects, providing a unified equation that simplifies the complex integral expressions often required in sequential or piecewise approaches. Thermogravimetric analysis (TGA), Differential Scanning Calorimetry (DSC), and rheological characterization were performed to assess the thermal stability, curing behaviour, and viscosity changes over a range of isothermal temperatures. Time-temperature graphs generated by the model highlight critical regions for processing, including the processability window and the rapid crosslinking region. These insights are crucial for optimizing process parameters in the large-scale manufacturing of composite parts, particularly for complex geometries. • A novel cure kinetics model that accounts for inhibition and diffusion effects. • A viscosity model coupled with the cure kinetics model and inhibition effects. • Processability maps show the available injection time vs. process temperature. • Simulation compares HPRTM of polyester with inhibitor vs. highly reactive resin.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".