Ultrasonication accelerated curing of epoxy resin: monitoring through impedance measurement
Bibliographic record
Abstract
Carbon Fiber Reinforced Polymers (CFRP) are widely used in sectors such as aeronautics and automotive due to their high strength, stiffness, and corrosion resistance. However, the manufacturing process is complex and still poses challenges. This work focuses on two aspects: (a) continuous monitoring of epoxy resin curing through non-destructive electrical impedance measurements across a relevant frequency range, and (b) the impact of ultrasonic energy on the curing process. Unlike common thermal analysis methods, dielectric measurements offer the advantage of real-time, continuous monitoring throughout the entire cure. Changes in capacitance and resistance during curing provide insight into cure status. Recent studies using high-intensity ultrasonic fields have reported on the effect of acoustic energy on accelerating epoxy resin curing. However, a distinction needs to be made between the induced thermal effects, caused by equipment heating significantly during use, and the vibrational energy aspects. Furthermore, there is a need for an effective, non-destructive, and continuous monitoring procedure to better understand the role of ultrasound in the curing process. Tests in this study using an ultrasonic bath demonstrated the influence of mechanical vibrational energy on curing with the help of impedance analyzer measurements. The aim is to leverage ultrasonic waves to reduce curing time, prevent defect formation, and improve CFRP manufacturing productivity through enhanced curing.
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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) | 0.000 | 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 | 0.001 |
| 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".