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Improved Convolutional Neural Network for Laser Welding Defect Prediction

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Abstract

In order to predict the laser welding defects, a convolutional neural network prediction model is established. The keyhole image and plume image collected by a high-speed camera are processed to obtain visual information such as keyhole area and plume area. The rolling mean and standard deviation methods are used to calculate the fluctuation degree indicators of the visual information and the optical radiation information obtained by the photoelectric sensor. Finally, three improved one-dimensional convolutional neural network prediction models with a learning rate dynamic adjustment mechanism are established to predict welding defects. Experimental results indicate that the improved one-dimensional convolutional neural network prediction model can avoid premature convergence four times to achieve the best performance. The fluctuation degree indicators of sensor features can distinguish the welding state more easily than the sensor features. The reliability test of the new weld is carried out. The prediction accuracy of fusion detection model of sensor features and fluctuation degree indicators is 99.21%. The improved model can accurately predict laser welding defects.

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Funding

This work was supported in part by the Guangzhou Municipal Special Fund Project for Scientific and Technological Innovation and Development under Grant 202002020068, the National Natural Science Foundation of China under Grant 52275317.

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Authors and Affiliations

  1. Guangdong Provincial Welding Engineering Technology Research Center, Guangdong University of Technology, Guangzhou, 510006, China

    Weiwei Huang, Xiangdong Gao, Yuhui Huang & Yanxi Zhang

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  1. Weiwei Huang
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  2. Xiangdong Gao
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  3. Yuhui Huang
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  4. Yanxi Zhang
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All authors contributed equally to this work.

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Correspondence to Xiangdong Gao.

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Huang, W., Gao, X., Huang, Y. et al. Improved Convolutional Neural Network for Laser Welding Defect Prediction. Int. J. Precis. Eng. Manuf. 24, 33–41 (2023). https://doi.org/10.1007/s12541-022-00729-9

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