Time of Flight Measurements in Real-Time Ultrasound Signatures of Aluminum Spot Welds: An Image Processing Approach
Bibliographic record
Abstract
Ultrasonic testing is one of the most popular non-destructive methods to determine the quality in spot welds. The next step of this technology is to be able to determine the quality while the weld is being made, this advance can have a great impact on the quality assurance of joints in materials like aluminum, where the spot welding process is a difficult task. In our approach, a state-of-the-art ultrasound transducer assembly is installed in one of the welding electrodes. Working in pulse-echo mode, the system collects A-scans of the waves passing through the welded plates in the direction perpendicular to the plate’s surface. A-scans are gated to allow stack-up front and back wall reflections to be recorded. Such setup has allowed the acquisition of real-time ultrasonic signatures composed of multiple A-scans for each spot weld. Eventually the signature should be processed to extract information about the weld quality. During the welding process temperature gradually increases within the aluminum plates; as sound speed in metals is inversely proportional to the temperature, the time of flight will increase during welding. The change in time of flight of the ultrasonic wave during the whole welding process is a good indicator of the heating rate in the weld and can be a good judge of weld quality. Unfortunately, electrode deterioration, industrial noises and pulse changes due to frequency dependent attenuation make signal detection a challenging task. To overcome this difficulty the system treats B-scan as an image instead of a set of separate A-scans. With such approach, the weak signal detection (in
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".