A Novel 3D Infrared Tomographic Technology Based on Undersampling and Line-Scanned Structured Heating
Bibliographic record
Abstract
Traditional infrared thermography (IRT) techniques can only provide two-dimensional (2D) projections of surface temperatures, and it is difficult to intuitively present the surface profile of the three-dimensional (3D) structure and the spatial distribution of the internal defects. In this paper, a low-cost, high efficiency and high precision photothermal 3D tomography technology was proposed by combing 3D thermography and infrared tomography for the first time. Specifically, this paper abandoned additional 3D sensors and complex motion systems such as robotic arms and scanning platforms. Differently, it utilized a galvanometer to deflect laser for line structure light scanning, and captured the thermal stripes generated on the structural surface using an infrared camera, and then realized the highly efficient 3D reconstruction; moreover, this work introduced undersampling strategy into photothermal coherence tomography (PCT) technology to enhance the ranging depth, and the ranging results were transmitted to the 3D coordinate system, so as to realize the photothermal 3D tomography. Through the detection experiments of metal additive manufacturing parts, it was shown that the proposed method could reconstruct the 3D contour of the specimen, and identified the first heterogeneous interface below the surface of the specimen.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".