Laser photothermal non-destructive metrology of cracks in un-sintered powder metallurgy manufactured automotive transmission sprockets
Bibliographic record
Abstract
A non-contact and non-intrusive method of revealing crack presence in un-sintered (green) automotive transmission parts (sprockets), manufactured by means of a powder metallurgy technology based on analysis of photo-thermal radiometric (PTR) signals and their statistical analysis was developed. The inspection methodology relies on the interaction of a modulated laser generated thermal wave with the potential crack and the resulting change in amplitude and phase of the detected signal [1-5]. The crack existence at points in high stress regions of a group of green (unsintered) sprockets was evaluated through frequency scans. The results were validated by independent destructive cross-sectioning of the sprockets following sintering and polishing. Examination of the sectioned sprockets under a microscope at the locations where signal changes was used for correlation with the PTR signals. Statistical analysis confirmed the capabilities of the method to detect the presence of hairline cracks (~5 − 10 μm size) with excellent sensitivity (91%) and good accuracy (78%) and specificity (61%). This measurement technique and the associated statistical analysis can be used as a simple and reliable on-line inspection methodology of industrial powder metallurgy manufactured steel products for non-destructive quality and feedback control of the parts forming process.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".