A Visual Inspection System for Burrs on Lithium Battery Electrodes

A lithium battery electrode burr detection system based on machine vision has been developed. It employs image processing techniques such as threshold segmentation, morphological processing, least squares method, caliper tools, curve fitting, differential operation, and feature attribute analysis to calculate the size and position information of electrode foil burrs. A systematic analysis of the visual inspection system’s measurement of electrode burr height is conducted using evaluation indicators such as linear bias, correlation, measurement repeatability, and reproducibility (Gauge Repeatability and Reproducibility, GR&R) , to verify the accuracy and precision of the system. Testing indicates that the detection accuracy of the visual inspection algorithm is 96.67%, proving that the system performs well and can meet the requirements of lithium battery electrode foil production processes.