Eddy Current Method for Inspecting Foil-to-Tab Weld Quality in Li-Ion Batteries
Bibliographic record
Abstract
Defective components in Li-ion batteries can lead to loss of capacity, reduced discharge rate, or thermal runaway. For electric vehicles (EVs), quality control is especially relevant. In this investigation, foil-to-tab ultrasonic welds connecting EV battery cells were examined using eddy current testing (ECT). Two types of foil-to-tab welds were investigated; aluminum foils welded to aluminum tabs, and copper foils welded to nickel-coated copper tabs, with up to 24 foils welded to a tab. An ultrasonic welder was used under standard nominal conditions, at underwelded conditions as low as 1/3 the energy, and for overwelded conditions as high as four times the energy input. The underwelds were characterized by small divot size and lack of fusion between foils, and overwelds by excessive foil penetration due to larger divots. ECT demonstrated an ability to differentiate between the various weld conditions, attributed to divot size increasing with welding energy. ECT was also shown to be sensitive to the number of either copper or aluminum foils. COMSOL modeling showing eddy current response varying with a number of foils was in good agreement with measured results. Foil-to-tab welds contaminated by plastic sheets inserted in between foils, simulating a potential error in the manufacturing process, could also be detected. These results could be used to design an automated eddy current instrument where defects, including folded or missing foils, contaminated welds, and nonnominal weld types, could be detected.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".