Automotive · Production · Vendor case study
MVTec HALCON's deep learning helps Panasonic Energy to propel automotive battery production
| Company / plant | Panasonic Energy, Kansas plant (US) |
|---|---|
| Defect type | Fiber waste adhesion, welding spatter on laser weld marks |
| Part | Electrode tabs of cylindrical Li-ion cells (EV batteries) |
| Material | Metal foil |
| Vendor | MVTec HALCON |
| Camera / lighting | Rule-based inspection combined with deep learning judgment |
| Training data | Not stated in source |
| Reported result (quoted) | "resulted in a 57.3% reduction in the number of over-detected discharges"; headline: "Zero defective outflow achieved while halving the number of over-detections" |
| Notes | 57.3% figure is from the domestic (Japan) verification test. |
| Defect categories | Weld & spatter |
| Source | https://www.mvtec.com/application-areas/success-stories/article/mvtec-halcons-deep-learning-helps-panasonic-energy-to-propel-automotive-battery-production |
Results are quoted from the source. Vendor-published results are vendor claims and were not independently verified. Spot an error? Contact us.