Cause diagnosis of welding defects based on adaptive PSO-BP neural network with dynamic weighting

GAO Changlin; SONG Yanli; ZUO Hongzhou; ZHANG Cheng

doi:10.12073/j.hjxb.20210515001

TRANSACTIONS OF THE CHINA WELDING INSTITUTION > 2022 > 43(1): 98-106. > DOI: 10.12073/j.hjxb.20210515001

GAO Changlin, SONG Yanli, ZUO Hongzhou, ZHANG Cheng. Cause diagnosis of welding defects based on adaptive PSO-BP neural network with dynamic weighting[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2022, 43(1): 98-106. DOI: 10.12073/j.hjxb.20210515001

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Cause diagnosis of welding defects based on adaptive PSO-BP neural network with dynamic weighting

GAO Changlin^1,,
SONG Yanli^{1, 2, 3, ,},
ZUO Hongzhou⁴,
ZHANG Cheng¹

1.
Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan, 430070, China
2.
Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan, 430070, China
3.
Hubei Engineering Research Center for Green & Precision Material Forming, Wuhan University of Technology, Wuhan, 430070, China
4.
Hubei Qixing Automobile Body Co., Ltd., Suizhou, 441300, China

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Received Date: May 14, 2021
Accepted Date: February 14, 2022
Available Online: February 18, 2022

Graphical Abstract

Abstract

Abstract

Considering the complex causes and various impact factors for welding defects, diagnosis methods based on artificial intelligence algorithms are regarded as one of the directions for the development of intelligentizing welding. In this study, an improved diagnosis method for welding defect based on PSO-BP neural network is proposed. Connection learning mechanism of neural network is used instead of the rule reasoning mechanism of traditional expert systems. It also makes adaptive adjustments to the PSO algorithm, introduced dynamic weight factors, and builds an adaptive PSO-BP neural network model. Compared with the traditional PSO-BP neural network model, the number of iterations required to train the improved PSO-BP neural network model reduced by 13.1%, the accuracy of diagnostic results increased from 93.3% to 96.7%, the precision increased from 91.3% to 98.3%, and the comprehensive performance index increased from 91.7% to 96.9%. The results show that the improved algorithm can significantly improve the efficiency and accuracy of welding defect diagnosis, and has good engineering application value.
- cause of welding defects,
- neural network,
- dynamic weighting,
- adaptive

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References (17)

References

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Cause diagnosis of welding defects based on adaptive PSO-BP neural network with dynamic weighting