Comparative analysis of single- and multiple-frequency thermal wave radar imaging inspection of glass fiber reinforced polymer (GFRP)
Bibliographic record
Abstract
Abstract Active infrared thermography has gained increasing popularity for nondestructive testing and evaluation in various industrial fields, especially for composite structures. In this regard, thermal wave radar (TWR) imaging is recognized as the next-generation active thermography technology to obtain great resolution and depth range over the inspected objects. A critical aspect concerns the optimal test parameter selection to guarantee reliable quality assurance required for industrial products. In this work, single- and multiple-frequency TWR was investigated in a quantitative manner with the goal of optimizing the detection parameters in terms of probing range and lateral and depth resolution. The effects of test parameters, including sampling frequency, modulation frequency, chirp duration, chirp bandwidth, etc, were investigated in detail through experiments on a glass fiber reinforced polymer specimen with multi-scale diameter-to-depth ratio defects. This paper aims to help yield a better understanding of the physical mechanism behind TWR and propose a workable scheme for testing parameter selection in practical applications.
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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 | 0.001 | 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 | 0.000 |
| 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".