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.

Due to the tremendous expectations placed on batteries to produce a reliable and secure product, fault detection has become a critical part of the manufacturing process. Manually, it takes much labor and effort to test each battery individually for manufacturing faults including burning, welding that is too high, missing welds, shifting, welding holes, and so forth. Additionally, manual battery fault detection takes too much time and is extremely expensive. We solved this issue by using image processing and machine learning techniques to automatically detect faults in the battery manufacturing process. Our approach will reduce the need for human intervention, save time, and be easy to implement. A CMOS camera was used to collect a large number of images belonging to eight common battery manufacturing faults. The welding area of the batteries’ positive and negative terminals was captured from different distances, between 40 and 50 cm. Before deploying the learning models, first, we used the CNN for feature extraction from the image data. To over-sample the dataset, we used the Synthetic Minority Over-sampling Technique (SMOTE) since the dataset was highly imbalanced, resulting in over-fitting of the learning model. Several machine learning and deep learning models were deployed on the CNN-extracted features and over-sampled data. Random forest achieved a significant 84% accuracy with our proposed approach. Additionally, we applied K-fold cross-validation with the proposed approach to validate the significance of the approach, and the logistic regression achieved an 81.897% mean accuracy score and a +/− 0.0255 standard deviation.

At present, in order to improve the safety performance of power battery, a safety vent is welded on the battery cover to avoid unpredictable explosions. It is vital to detect the laser welding defects on safety vent effectively for product quality. In this paper, a lightweight multiscale attention semantic segmentation algorithm with high accuracy and efficiency was proposed. We built an experimental dataset of safety vent welding defects with a total of 7263 original images, which were collected from a battery manufacturing production line. The main framework of the proposed model consists of four modules: the improved Res2Net serving as the feature extraction sub-module, an attention mechanism, a localization block and a boundary anti-aliasing module. This architecture can segment defects of different sizes and shapes in real-time and get more refined segmentation results simultaneously. To evaluate the method, experiments concerning mean IOU and pixel accuracy were conducted, and an average validation accuracy of 99.4% and the mean IOU of 84.67% were achieved respectively. Furthermore, comparison experiments using some outstanding algorithms on safety vent’s welding defects test dataset were performed. It proves that our method achieved the best performance in terms of model size, computational complexity, efficiency and detection accuracy. Specifically, the model size is only 3.8 MB, and the frames per second (FPS) is 132.3. In brief, the proposed model is suitable for laser welding quality detection on safety vent in an industrial environment. Additionally, our study can provide a reference for designing relevant defect detection tasks using semantic segmentation method.

Focus on the requirement for detecting laser welding defects of lithium battery pole, a new model based on the improved YOLOv5 algorithm was proposed in this paper. First, all the 3 × 3 convolutional kernels in the backbone network were replaced by 6 × 6 convolutional kernels to improve the model’s detection capability of a small defect; second, the last layer of the backbone network was replaced by our designed SPPSE module to enhance the detection accuracy of the model; then the improved RepVGG module was introduced in the head network, which can help to improve the inference speed of the model and enhance the feature extraction capability of the network; finally, SIOU was used as the bounding box regression loss function to improve the accuracy and training speed of the model. The experimental results show that our improved YOLOv5 model achieved 97% mAP and 270 fps on our dataset. Compared with conventional methods, ours had the best results. The ablation experiments were conducted on the publicly available datasets PASCAL VOC and MS COCO, and their mAP@0.5 was improved by 2.4% and 3%, respectively. Additionally, our model improved the average detection rate for small targets on the MS COCO dataset by 2.4%, showing that it can effectively detect small target defects.

Plasmonic waveguides, as a competitive candidate, have been widely studied in rapid developing photonic integrated circuits (PICs) and optical interconnection fields. However, crosstalk between plasmonic waveguides is a critical issue that has to be considered in practice. Actually, crosstalk dominates the ultimate integration density of the planar photonic circuits. This paper reviews the recent research work on evaluation methods and crosstalk suppression approaches of plasmonic waveguides. Three crosstalk evaluation methods based on comparison of specific parameters of waveguides have been summarized. Furthermore, four specific approaches to reduce crosstalk have been illustrated as two categories according to their impacts on waveguide performances and the whole circuit. One means of crosstalk suppression is changing the placement of waveguides, which could maintain the transmission characteristics of the original waveguide. The other means is inserting medium, which has the advantage of occupying smaller space compared to the first method. Consequently, to suppress crosstalk between plasmonic waveguides, one should choose suitable approach.