An electrode misalignment inspection system based on image processing technology for use in resistance spot welding
Bibliographic record
Abstract
Abstract Weld quality in resistance spot welding (RSW) is greatly affected by any misalignment of the upper and lower electrodes. This work presents a non-contacting system for measuring electrode misalignment. It uses two industrial CCD cameras, oriented perpendicular to one another in the same plane. The electrode cap images taken by the CCD cameras are sent to a computer through an Ethernet switch. The original red, green and blue (RGB) images are converted to greyscale images. The left and right edge points of the cap images are determined by computing the first derivative of the grey level gradient of the images; the edge points are the locations of local maxima in the horizontal direction. Using the edge points, linear boundaries are fitted via the least squares method, and the noise points are eliminated. The axes of the electrode caps are calculated from the left and right linear boundaries of the images, and then the spatial and angular offsets of the two electrode axes can be determined by the two axis functions. The results of comparison experiments indicate that the abovementioned system can rapidly and accurately measure the misalignment of electrodes during the process of RSW. The measurement results will provide critical input for equipment adjustment and maintenance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".