Spot Weld Inspections Using Active Thermography
Bibliographic record
Abstract
Spot welds have a significant part in the creation of automotive vehicles. Since the integrity of, for example, a car, is dependent on the performance of multiple welds, it is important to ensure the quality of each spot weld. Several attempts have been made in order to determine the quality of spot welds, but most of them do not focus on the applicability in the manufacturing process. Spot weld inspections are often performed using back heating. However, during manufacturing, robotic inspections are desired, and since the bodywork of a car is a complex shape, the accessibility from the inside of the vehicle is minor. Therefore, inspections using front heating are more suitable. In this manuscript, multiple excitation methods are compared as well as different post-processing techniques. The used excitation techniques can be divided into light heating and inductive heating. Light heating is further divided in lock-in thermography and pulse thermography. The used post-processing techniques are principle component analysis and fast Fourier transform. Inductive heating turns out to be the most suitable measurement technique since it is fast and can be performed as front and back heating. Both investigated post-processing techniques deliver suitable information, such as relief images and information of the internal structure of the spot weld.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 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".