Prediction and optimization of multi-layer and multi-pass welding process parameters based on GA-BP neural network

WANG Tianqi; MENG Kaiquan; WANG Chuanrui

doi:10.12073/j.hjxb.20230523001

TRANSACTIONS OF THE CHINA WELDING INSTITUTION > 2024 > 45(5): 29-37. > DOI: 10.12073/j.hjxb.20230523001

WANG Tianqi, MENG Kaiquan, WANG Chuanrui. Prediction and optimization of multi-layer and multi-pass welding process parameters based on GA-BP neural network[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(5): 29-37. DOI: 10.12073/j.hjxb.20230523001

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Prediction and optimization of multi-layer and multi-pass welding process parameters based on GA-BP neural network

Tianjin Key Laboratory of Modern Mechanical and Electrical Equipment Technology, Tiangong University, Tianjin, 300387, China

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Received Date: May 22, 2023
Available Online: March 08, 2024

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Abstract

Abstract

To solve the selection of process parameters for multi-layer and multi-pass welding, a strategy based on GA-BP neural network is proposed to predict the weld forming and optimize welding process parameters in the multi-layer and multi-pass welding. Firstly, by analyzing welding images, a cubic spline interpolation and adaptive segmentation methods is proposed to identify the feature points in multi-layer and multi-pass welding. Then, a prediction model for the cross-sectional shape of each weld bead during the welding process is established, and an analytic method is used to predict the welding process parameters. Further combining the principles of optimizing different welding process parameters, an improved neural network is used to optimize multi-layer and multi-pass welding process parameters, the real-time model for the relationship between welding process parameters and weld formation is established. Finally, the feasibility of the multi-layer and multi-pass welding process parameter selection strategy proposed in this paper was verified through experiments. The experiment shows that this method provides effective prediction of welding process parameters for each pass in multi-layer and multi-pass welding, and the experimental results meet practical needs.
- optimizing process parameters,
- image processing,
- forecasting using analytical methods,
- optimizing neural networks

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References

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Prediction and optimization of multi-layer and multi-pass welding process parameters based on GA-BP neural network