Quality prediction of aluminum alloy resistance spot welding based on correlation analysis and SSA-BP neural network

DONG Jianwei; HU Jianming; LUO Zhen

doi:10.12073/j.hjxb.20230226001

TRANSACTIONS OF THE CHINA WELDING INSTITUTION > 2024 > 45(2): 13-18, 32. > DOI: 10.12073/j.hjxb.20230226001

DONG Jianwei, HU Jianming, LUO Zhen. Quality prediction of aluminum alloy resistance spot welding based on correlation analysis and SSA-BP neural network[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(2): 13-18, 32. DOI: 10.12073/j.hjxb.20230226001

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Quality prediction of aluminum alloy resistance spot welding based on correlation analysis and SSA-BP neural network

School of Materials Science and Engineering, Tianjin University, Tianjin, 300350, China

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Received Date: February 25, 2023
Available Online: January 04, 2024

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Abstract

Abstract

Based on the characteristics of the process signals in the resistance spot welding process, three working conditions of different spacing, different gaps and different spacing and gaps are analyzed, and correlation analysis is introduced to extract the correlation between the process signals and the diameter of nugget. A resistance spot welding quality prediction model based on Sparrow Search Algorithm-Back Propagation Neural Network (SSA-BP) was established, and power, welding current, welding voltage and dynamic resistance are used as input features of the prediction model. The results show that the BP neural network optimized by the sparrow search algorithm outperforms the BP model on the test set with R², MSE, RMSE and MAE of 0.95, 1.55, 1.24 and 0.90, respectively. It is also determined that there exists a mapping relationship between power, welding current, welding voltage and dynamic resistance and the diameter of the nugget, which provides a basis for the design of process parameters for welding.
- resistance spot welding,
- nugget diameter,
- sparrow search algorithm,
- BP neural network,
- correlation analysis

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Quality prediction of aluminum alloy resistance spot welding based on correlation analysis and SSA-BP neural network