Autonomous visual detection of defects from battery electrode manufacturing

Abstract

The increasing global demand for high-quality and low-cost battery electrodes poses major challenges for battery cell production. As mechanical defects on the electrode sheets have an impact on the cell performance and their lifetime, inline quality control during electrode production is of high importance. Correlation of detected defects with process parameters provides the basis for optimization of the production process and thus enables long-term reduction of reject rates, shortening of the production ramp-up phase, and maximization of equipment availability. To enable automatic detection of visually detectable defects on electrode sheets passing through the process steps at a speed of 9 m/s, a You-Only-Look-Once architecture (YOLO architecture) for the identification of visual detectable defects on coated electrode sheets is demonstrated within this work. The ability of the quality assurance (QA) system developed here to detect mechanical defects in real time is validated by an exemplary integration of the architecture into the electrode manufacturing process chain at the Battery Lab Factory Braunschweig.

Research on precision visual inspection technology based on new energy battery manufacturing

“…Similarly, the first-order and second-order backward differences of the pixel point are derived, and the average of the two is brought into the discrete curvature expression to obtain the discrete defect profile curvature [13] .…”