Numerical Simulation of Induction Thermography on a Laminated Composite Panel
Bibliographic record
Abstract
A three-dimensional finite element model was developed for simulating induction heating of a composite panel using the COMSOL multiphysics software, version 5.1. Equivalent anisotropic material thermal and electrical conductivities of the composite panel were used in the simulation. The model was validated using experimental temperatures obtained from an infrared camera. Good agreement was obtained between the experimental and numerical results for a pristine panel and a specific flawed panel. Then, this methodology was used to simulate the induction heating of a panel within different flaw scenarios. The correlation between the flaw scenario and temperature distribution was investigated. Flaws led to high gradients in the temperature distributions. The numerical results suggest that temperature variation on the panel coil side (outer) surface could be used to detect some types of flaws. In addition, due to low thermal conductivity, the induction heating period should be carefully controlled to avoid potential material degradation caused by overheating when using this thermography inspection technique.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".