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WU Gang, CHEN Tian, YU LiangHui, LIU Zhipeng. Research on automatic classification of spot welding joint strength based on PSO-SVM[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION. DOI: 10.12073/j.hjxb.20220829001
Citation: WU Gang, CHEN Tian, YU LiangHui, LIU Zhipeng. Research on automatic classification of spot welding joint strength based on PSO-SVM[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION. DOI: 10.12073/j.hjxb.20220829001
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Research on automatic classification of spot welding joint strength based on PSO-SVM

  • 1.

    Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges University, Yichang, 443002

  • 2.

    China Three Gorges University, Yichang, 443002

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  • Available Online: June 24, 2023
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    • Abstract
  • Spot welding is one of the main connection methods of automobile parts, and the tensile and Shear strength of its joints are important factors to evaluate the connection quality. On the basis of preparing a large number of spot welding samples, this investigation conducted ultrasonic signal detection on the joints of each sample. Using signal processing methods to obtain time-domain, frequency-domain, and wavelet packet eigenvalues, a grading standard for the tensile and shear strength of spot welded joints was established by conducting tensile and shear tests on spot welded samples. According to the test data, BP neural network and neural network classifier based on Particle swarm optimization support vector machines (PSO-SVM) are trained. Finally, the accuracy of two neural network models for spot welding strength classification was compared by inputting feature value parameters with different data set. The experimental results show that the PSO-SVM neural network combined with 9 ultrasonic signal eigenvalues has a spot welding strength classification accuracy of 95%.
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    • References (17)
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