Non-destructive evaluation of laser welds in tailor-welded blanks using magnetic flux leakage
Bibliographic record
Abstract
The tailor-welded blank (TWB) industry, which uses laser welding exclusively, is facing higher demands on quality assurance, both internally to raise efficiency of production lines and externally from their customers (predominantly auto makers). This paper introduces a novel, economical approach to TWB inspection, which employs the principle of magnetic flux leakage (MFL). The development of a laboratory-based MFL inspection tool for TWBs is presented. The effects of inspection system operating parameters are quantified to allow for optimized and robust performance. The operating parameters examined include applied magnetic field strength, scanning velocity, and sampling resolution. The ability of the MFL technique to detect defects that occur in the production environment from CO2 and Nd:YAG welds is clearly demonstrated. Defects include porosity, missed weld, pinholes, mismatch, concavity, and convexity. The issues of robustness and reliability surrounding the implementation of an industrial MFL inspection tool are addressed and suitable recommendations for further development are made.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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 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".