Study on imaging techniques and quantitative detection method for internal void defects in rubber based on terahertz reflection imaging
Bibliographic record
Abstract
This paper applies the reflection mode of terahertz time-domain spectroscopy technology to conduct research on the void defects in black silicone rubber samples. Algorithms such as power spectral density (PSD) integration imaging, homomorphic filtering, and the Otsu method are innovatively integrated to construct an efficient, high-precision defect characterization system. Different from traditional research, this paper deeply explores the advantages of each algorithm and optimizes them according to the characteristics of rubber materials and terahertz signals. By combining multiple features in the time-domain and frequency-domain to reconstruct terahertz images, and with the collaborative optimization of grayscale histogram equalization and filtering algorithms on the imaging quality, the optimal combination of PSD integration imaging, homomorphic filtering, and the Otsu method is determined, achieving precise defect imaging and quantification. In spectral analysis, a method combining wavelet signal denoising and time-domain spectroscopy is proposed. A formula based on time-of-flight was then used for quantitative analysis of the defects. In 3D imaging, an innovative alignment operation of denoised time-domain spectral curves is introduced. Combined with the maximum intensity projection, the clear visualization of void defects is realized.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".