Blister Defect Detection Based on Convolutional Neural Network for Polymer Lithium-Ion Battery

Abstract

To ensure the quality and reliability of polymer lithium-ion battery (PLB), automatic blister defect detection instead of manual detection is developed in the production of PLB cell sheets. A convolutional neural network (CNN) based detection method is proposed to detect blister in cell sheets employing cell sheet images. An improved architecture for dense block and a learning method based on optimization of learning rate are discussed. The proposed method was superior to other machine learning based methods when the classification performance and confusion matrix were compared in experiments. The proposed CNN method had the best defect detection performance and real-time performance for industry field application. Keywords: defect detection; polymer lithium-ion battery; convolutional neural network; deep learning; blister defect; flower pollination algorithm IntroductionThe application of lithium-ion batteries has changed consumer electronic products, which greatly reduce the weight and volume of mobile phones, notebooks, and other portable products. Lithium-ion batteries have been widely studied, including model, optimal design, and so on [1][2][3][4][5][6]. At present, the most commonly used lithium-ion battery is polymer lithium-ion battery (PLB). The cathode materials of PLB are commonly lithium cobalt acid, lithium manganese acid, ternary materials or lithium iron phosphate. The anode materials usually use carbon materials, such as artificial graphite, natural graphite, intermediate phase carbon microspheres, petcoke, carbon fiber, pyrolysis resin carbon and so on. C-rate is the measurement of the charge and discharge current with respect to its nominal capacity. At present, most PLB use polymer gel electrolyte instead of liquid electrolyte, which makes PLB have the advantages of thinning, arbitrary area and arbitrary shape. These characteristics improve the capacity of PLB, and PLB has the characteristics of miniaturization, thinning, and light quantification. PLB has been widely used in portable electronic equipment, and it is gradually being applied to more fields. With the application of PLB in more and more electronic products, the quality of PLB has more and more influence on the quality of electronic products. The quality of PLB is critical to the quality and reliability of electronic products.Recently, an automatic line of PLB has been developed in Dongsheng Energy Corporation, Weihai, China. The PLB has a voltage of 48 V, a charging current of 2-3 A, and nominal capacity of 16 AH. The anode of PLB is a conductive high molecular polymer, the catrode is graphite, and a colloidal polymer electrolyte is used. During the production process of the PLB, several cathode pieces, anode pieces, and separator pieces are combined to produce a cell sheet. Great battery capacity can be provided with more cell sheets combined together. Thus, the quality inspection of cell sheets in automated production lines is essential to the quality of the final battery product. The cell sheet defect needs to be detected to ens...

A Two-Stage Multiscale Residual Attention Network for Light Guide Plate Defect Detection

“…In recent years, deep learning techniques have found wide application in product surface defect detection [1][2][3][4][5], indicating that the defect detection method based on deep learning can achieve excellent results and can accommodate different products. Chen [6] utilized the OverFeat [7] network trained on 1.2 million images of general visual objects from the ILSVRC2013 dataset to detect five different types of surface anomalies.…”

Modeling and Experimental Study for Online Measurement of Hydraulic Cylinder Micro Leakage Based on Convolutional Neural Network

“…CNN performs well in a variety of visual recognition tasks, especially in the field of image classification [30]. In order to make full use of the virtue of CNN in image recognition, the input layer is 2D matrix converted by 1D sequence.…”

Arching Detection Method of Slab Track in High-Speed Railway Based on Track Geometry Data

“…In recent years, deep learning has received extensive attention [39]. It does not need to set quantitative indexes manually and can learn deep potential features autonomously.…”