Abstract
The battery industry has been growing fast because of strong demand from electric vehicle and power storage applications.Laser welding is a key process in battery manufacturing. To control the production quality, the industry has a great desire for defect inspection of automated laser welding. Recently, Convolutional Neural Networks (CNNs) have been applied with great success for detection, recognition, and classification. In this paper, using transfer learning theory and pre-training approach in Visual Geometry Group (VGG) model, we proposed the optimized VGG model to improve the efficiency of defect classification. Our model was applied on an industrial computer with images taken from a battery manufacturing production line and achieved a testing accuracy of 99.87%. The main contributions of this study are as follows: (1) Proved that the optimized VGG model, which was trained on a large image database, can be used for the defect classification of laser welding. (2) Demonstrated that the pre-trained VGG model has small model size, lower fault positive rate, shorter training time, and prediction time; so, it is more suitable for quality inspection in an industrial environment. Additionally, we visualized the convolutional layer and max-pooling layer to make it easy to view and optimize the model.
Highlights
With the rapid development of battery electric vehicles (BEVs), laser welding technology has been widely used in the assembling process of lithium-ion batteries
To meet the desired power and capacity demand for BEVs, a lithium-ion battery pack is assembled from lots of battery cells, sometimes several hundred or even thousands, which depends on the cell configuration and pack size [1]
Focusing on the requirement of laser welding quality inspection, we proposed an optimized Convolutional Neural Networks (CNNs) model to achieve the defect classification of the surface welding area of a safety vent based on a Visual Geometry Group (VGG)-16 conv_base, which was trained on a large database
Summary
With the rapid development of battery electric vehicles (BEVs), laser welding technology has been widely used in the assembling process of lithium-ion batteries. If the features of numerous components are manually set from the feature extraction region, the efficiency of the AOI process will decrease greatly These methods are influenced by the illumination environment. The welding defect inspection of battery’s safety vent, the main difficulty encountered by visual inspection algorithms is that the quality of the photographs is seriously affected by the illumination environment [8]. In our lithium-ion battery laser welding system, the accuracy of safety vent’s welding defect inspection and classification directly affects the value of AOI, which aims to replace human inspection. We developed a deep learning algorithm to improve the recognition accuracy of welding quality inspection and defect classification. The image of a safety vent was visualized in the convolution layer and max-pooling layer to make it easy to view and optimize the model
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