Prediction of residual stress and deformation of 316L multi-layer multipass welding based on GA-BP neural network

被引：0
|
作者：
Li C. [1 ,2 ]
Ji H. [2 ]
Yan Z. [3 ]
Liu Z. [2 ,4 ]
Ma J. [2 ,3 ,4 ]
Wang R. [2 ]
Wu J. [2 ,5 ]
机构：
[1] University of Science and Technology of China, Hefei
[2] Institute of Plasma Physics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei
[3] Institute of Energy, Hefei Comprehensive National Science Center, Hefei
[4] Huainan New Energy Research Center, Anhui Province Key Laboratory of Special Welding Technology, Huinan
[5] Hefei Juneng Electro Physics High-tech Development Co., Ltd, Hefei
来源：
Hanjie Xuebao/Transactions of the China Welding Institution | 2024年 / 45卷 / 05期
关键词：
BP neural network; genetic algorithm; welding deformation; welding process parameters; welding residual stress;
D O I：
10.12073/j.hjxb.20230520002
中图分类号：
学科分类号：
摘要：
Residual stress and deformation are common phenomena in the welding process. Its existence will have a serious impact on the working performance and service life of the welded structure, and is one of the main reasons for the cracking and failure of the welded structure. Traditional methods for predicting residual stress and deformation mainly include finite element analysis. However, these methods have the disadvantages of poor prediction accuracy and low reliability of numerical simulation results. To address the problem of predicting residual stress and deformation in 316L flat plates welding with a thickness of 20 mm, this paper proposes a GA-BP neural network prediction model based on optimized back propagation (BP) by genetic algorithm (GA), which selects the four most important welding process parameters as input parameters, including welding current, electric arc voltage, welding speed, and interpas temperature. The output of the model is the maximum transverse and longitudinal residual stress and deformation after welding. The results show that the error of the BP neural network model is within 15%. The error of GA-BP is less than 3%, indicating that the GA-BP neural network model is more accurate. This method can provide ideas and theoretical basis for optimizing process parameters of multi-layer multi-pass welding, as well as predicting and controlling residual stress and deformation after welding, and has certain practical guidance and application value. © 2024 Harbin Research Institute of Welding. All rights reserved.
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页码：20 / 28
页数：8
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