At present, most of the post-welding quality evaluation of power batteries is mainly carried out by manual visual inspection, which is bound to cause low detection efficiency and high labor costs, making it difficult to meet the requirements of modern welding production for high efficiency and high quality.
Reliable quality control of laser welding on power batteries is an important issue due to random interference in the production process. In this paper, a quality inspection framework based on a two-branch network and conventional image processing is proposed to predict welding quality while outputting corresponding parameter information.
Extensive experiments demonstrate that the proposed model can achieve a significant classification performance on the dataset collected on an actual production line. This study provides a valuable reference for an intelligent quality inspection system in the power battery manufacturing industry. 1. Introduction
It can be seen that the framework proposed in this paper can effectively extract the weld region parameters from the welding images on power batteries. In addition, the accuracy of the welding parameter extraction relies heavily on the results of the segmentation model in the previous section.
However, on large-scale automatic production lines, on-site uncertainties such as material inhomogeneity, residual impurities, and parameter fluctuations increase the welding instability and easily lead to welding defects, which will seriously affect the quality and performance of power batteries [ 3, 4 ].
A power battery is one of the key components of new energy vehicles, and its quality determines the reliability and safety of the vehicle to a large extent. Laser welding is widely used in power battery manufacturing due to its advantages of high energy density, high precision, and precise control over the heat input [ 1, 2 ].
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