High-Speed Videography of Welding — Part 1: Fundamentals
Bibliographic record
Abstract
This paper is the first of a three-part series comprehensively covering the field of high-speed videography in welding. This first part provides the fundamental concepts and resulting quantitative guidelines provided for minimum frame rates for several welding phenomena for maximum possible image resolution and the ability to capture thermal radiation from the welding process. Welding phenomena discussed include metal transfer, arc, and weld pool evolution with examples for gas metal arc welding (GMAW) and shielded metal arc welding (SMAW). The maximum possible image resolution for a given system is established based on the amount of time recorded, the buffer memory, the sensor resolution, the bit depth of the sensor, and the frame rate used. The application of Planck’s radiation law indicates that emission at low temperatures can be undetectable. Quantitative guidelines are also provided for filter type and critical wavelengths associated with light emitted by plasmas of different welding processes and thermal emission from the hot metal. Digital sensors, lenses, optical filters, and digital formats for processing and distribution are treated in detail. The fundamentals reviewed in this paper, together with the practical implementations for front and back lighting (Part 2) and natural radiation lighting (Part 3), will provide welding researchers with a previously inexistent compilation of criteria to select proper equipment, accessories and parameters for high-speed imaging of a vast variety of phenomena in welding, laser welding, and associated processes, such as additive manufacturing or cutting.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".