Ultrasound Spot Weld Profiling with Sparse Feature Recovery from Low-Fidelity A-Scans
Bibliographic record
Abstract
This work presents a custom-designed sparse deconvolution framework for ultrasound-based spot weld profiling from low-fidelity A-scan signals. Conventional systems often discard degraded signals due to low signal-to-noise ratios, resulting in diagnostic information loss. To address this, we model the A-scan as a convolution of sparse reflectors with the transducer impulse response, embedded in a Toeplitz dictionary. The impulse response is estimated via Savitzky–Golay filtering, and feature recovery is performed using multi-stage orthogonal sparse deconvolution with an ℓ<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf>-regularized objective. The proposed method enables accurate extraction of weld features such as plate surface, weld interface, and backwall, even under degraded acquisition conditions. Validated on automotive parts using a 15 MHz matrix probe with 52 active elements and 125 MHz sampling, the approach enabled extraction of features from previously unusable A-scans, achieving a feature recovery rate of up to 67%. The method is suitable for real-time deployment in industrial weld inspection environments.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".