Artificial intelligence based defect classification for weld joints

Florence, S. Esther; Samsingh, R. Vimal; Babureddy, Vimaleswar

doi:10.1088/1757-899x/402/1/012159

Cited by 8 publications

(1 citation statement)

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“…So the efficient identification of holes, grooves, cracks, keyholes, and flash defects is realized. In 2018, traditional networks are used to classify and recognize ultrasonic signals of stainless steel weld defects by Florence et al [24] at SSN College of Engineering in India, the classification of four types of defects including porosity, cracks, incomplete penetration and incomplete fusion is realized through the Back Propagation Network. Ultrasonic phased array technology is used by Murta [25] in the United States for defect analysis, and it is found that the classification of defects by this method relies heavily on experiences.…”

Section: Introductionmentioning

confidence: 99%

Study on Improved VGGNet and SK Convolution Identification Model for Defect Classification of Single Molten Salt Battery

Zhang

et al. 2023

IEEE Access

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Aiming at the lack of large public single molten salt battery data sets, to reduce the labourconsuming, to improve the insufficient learning ability of traditional diagnostic methods in the production of single molten salt battery, an image recognition model for molten salt battery defects based on transfer learning is proposed. First, some pre-processing operations and image enhancement on the single molten salt battery image are performed. Second, the backbone of recognition model is built based on VGG16 network, and the selective kernel (SK) convolution module is adopted after the bottleneck layer, convolution kernel with an appropriate size can be selected adaptively through input feature map; Third, the FC is taken the place of a GAP layer, a dropout layer, and other fine-tuning operations are added, a simplified model called V-VGGNet is got; Finally, the weight parameters obtained from the pre-training on the ImageNet data set are transferred to the single molten salt battery image recognition model V-VGGNet. For different network structures and different training strategies, comparative experiments of performance tests are conducted. The test data manifest that the accuracy rates of V-VGGNet network for three categories of defective images (Missing Negative Electrode, Broken Tab, and Missing Current Collector) and Assembly Normal images can reach 95.14%, 98.79%, 98.21%, and 99.41%, the average accuracy can achieve 97.91%, good performance improvement of the single molten salt battery is improved, it is about 3% higher compared to other well-knows networks, which verified the feasibility of V-VGGNet model and the effectiveness of the improvement.

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Section: Introductionmentioning

confidence: 99%