Three-dimensional super-resolution crack imaging in industrial manufactured components: A truncated correlation photothermal coherence tomography approach
Why this work is in the frame
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Bibliographic record
Abstract
The challenge of detecting cracks in powder metallurgy (PM) components has been a topic of interest for many years, with no practical commercial solution currently in place. This study employs enhanced truncation-correlation photothermal coherence tomography (eTC-PCT) to visualize 3D cracks within PM automotive parts. Through the application of effective diffusion-wave reversal techniques, blurred infrared thermophotonic images of subsurface defects (cracks) in (“green”) compressed metal PM components were restored to their original geometric resolution over the entire depth range. This approach enabled the creation of 3-dimensional depth-resolved photothermal tomographic images and cross-sectional mappings of cracks. The developed technique reveals the precise spatial dimensions of surface and subsurface cracks, reaching depths of down to 3 mm (not an upper limit) that conventional thermal imaging cannot access due to limitations imposed by the depth-integrated nature of conventional thermal-wave imaging, the properties of PM materials, and the physics of spreading diffusion. The super-resolution method was further applied to a sintered automotive part, specifically validating the efficacy of eTC-PCT for non-destructive imaging (NDI) in manufactured automotive components. Diffusion reversal imaging shows promise as a non-destructive testing (NDT) tool, with potential applications in a wide range of manufactured products, including both the green and sintered stages of automotive component production.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it