Self-adaptive Methods with Flexible Detection Criteria of the Battery Cell Dent Defect

For the battery cell production lines using automatic defect detection equipment, it becomes necessary to control the fluctuation of the yield rate of the production line, and adapt to the difference among the cell replacements and the incoming material processes of each batch. So the defect detection algorithm needs to adjust the detection criteria according to the target preferential rate range, which is a new challenge for artificial intelligent manufacturing in the battery industry. Taking the dent defect on the side of the battery cell as an example, based on both the traditional algorithm and the deep learning algorithm, two kinds of self-adaptive adjustment method of the defect detection criteria are proposed. The traditional algorithm employs the linear interpolation method to classify good and bad products based on depth and area information, and calculates the optimal critical values of depth and area that meet the target yield rate. The deep learning algorithm combines the convolutional neural network and the support vector machine classifiers, with the histogram of oriented gradient feature as the classifier input, so as to classify different degrees of defective products. The test results show that the detection criteria can be adjusted flexibly and automatically for the side dent defects, which could realize the self-adaption of algorithm to the target yield rate, save the manual operation time, and improve the production efficiency.