THERMOSETTING MATRIX COMPOSITE CHARACTERIZATION AND CURE CONTROL VIA A SCALE MODEL MOULD
Bibliographic record
Abstract
The main restriction for the widespread use of composites in many practical applications remains their prohibitive manufacturing costs. However, the development of new liquid composite moulding (LCM) processes brought an opportunity to significantly reduce these manufacturing costs. In order to successfully control composite manufacturing, an accurate material characterization and a close curing control of the composite are required. However, traditional characterization tools such as differential scanning calorimeter (DSC) didn’t succeed really well in generating a macroscopic modeling that reflects the real resin-reinforcement behaviour, in an industrial context of LCM manufacturing. Moreover, this equipment is usually too costly and out of reach for small businesses that only wish to characterize their resin batches before production and to monitor their ageing. Thus this article described a low-cost characterization and control tool for the curing of composites which is robust and non-intrusive. This tool, using thermal heat flow sensors, is able to emulate, on a reduced scale model, the thermal phenomena which take place during the curing stage of composites, inside a real industrial mould. Furthermore, this characterization tool can provide good insights on resin and composite thermo-physical and thermo-kinetic properties. First of all, a description of the experimental set-up employed will be carried out. Then, the selected characterization and temperature control strategies will be detailed. Finally, a numerical validation of these strategies, using the finite volume method, will be presented.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".