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基于GA-BP神经网络的316L多层多道焊残余应力和变形预测

李成文, 吉海标, 闫朝辉, 刘志宏, 马建国, 王锐, 吴杰峰

李成文, 吉海标, 闫朝辉, 刘志宏, 马建国, 王锐, 吴杰峰. 基于GA-BP神经网络的316L多层多道焊残余应力和变形预测[J]. 焊接学报, 2024, 45(5): 20-28. DOI: 10.12073/j.hjxb.20230520002
引用本文: 李成文, 吉海标, 闫朝辉, 刘志宏, 马建国, 王锐, 吴杰峰. 基于GA-BP神经网络的316L多层多道焊残余应力和变形预测[J]. 焊接学报, 2024, 45(5): 20-28. DOI: 10.12073/j.hjxb.20230520002
LI Chengwen, JI Haibiao, YAN Zhaohui, LIU Zhihong, MA Jianguo, WANG Rui, WU Jiefeng. Prediction of residual stress and deformation of 316L multi-layer multi-pass welding based on GA-BP neural network[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(5): 20-28. DOI: 10.12073/j.hjxb.20230520002
Citation: LI Chengwen, JI Haibiao, YAN Zhaohui, LIU Zhihong, MA Jianguo, WANG Rui, WU Jiefeng. Prediction of residual stress and deformation of 316L multi-layer multi-pass welding based on GA-BP neural network[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(5): 20-28. DOI: 10.12073/j.hjxb.20230520002

基于GA-BP神经网络的316L多层多道焊残余应力和变形预测

基金项目: 国家重大科技基础设施建设项目(No.2018-000052-73-01-001228);安徽省自然科学基金资助项目(2108085ME142);中国科学院青年促进会资助项目(2019433);国家自然科学青年基金项目(12105185)
详细信息
    作者简介:

    李成文,硕士研究生;主要从事基于机器学习的焊缝缺陷检测研究;Email: chengwen.li@ipp.ac.cn

    通讯作者:

    闫朝辉,博士,副研究员;Email: yanzhaohui@ie.ah.cn

  • 中图分类号: TG 407

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

  • 摘要:

    残余应力与变形是焊接过程中普遍存在的现象,对焊接结构的性能及使用寿命产生严重影响,是焊接结构发生开裂和失效的主要原因之一. 传统的多层多道焊残余应力和变形预测主要采用有限元分析法,该方法存在预测精度差,数值模拟结果可靠性低等缺点. 针对预测20 mm厚316L平板对接焊的残余应力和变形,提出一种基于遗传算法(genetic algorithm, GA)优化的反向传播(back propagation,BP)神经网络预测模型GA-BP,选取最主要的4个焊接工艺参数作为输入,包括焊接电流、电弧电压、焊接速度和层间温度,以焊后最大横、纵向残余应力和变形作为输出. 结果表明,BP神经网络模型的预测误差在15%以内,优化后的GA-BP网络模型预测误差小于3%,故GA-BP神经网络模型的预测更精准,该方法可为多层多道焊的焊接工艺参数优化以及焊后残余应力与变形的预测和控制提供思路与理论基础,具有一定的指导意义和实际应用价值.

    Abstract:

    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.

  • 图  1   焊接试板反变形示意图

    Figure  1.   Welding indication. (a) Sketch of striking arc, ending arc and spot welding position; (b) Sketch of the anti-deformation of the welding test plate

    图  2   试板的残余应力测试取点示意图(mm)

    Figure  2.   Sketch of the point selection for residual stress testing of the test plate

    图  3   盲孔法测量残余应力示意图

    Figure  3.   Schematic diagram of the blind hole method for measuring residual stress

    图  4   神经网络结构示意图

    Figure  4.   Schematic diagram of a neural network structure

    图  5   GA-BP神经网络算法流程图

    Figure  5.   Flowchart of the GA-BP neural network algorithm

    图  6   BP和GA-BP神经网络训练和预测结果

    Figure  6.   Training and prediction results of BP and GA-BP neural networks. (a) maximum lateral residual stress of the training sample; (b) maximum longitudinal residual stress of training sample; (c) training sample deformation angle; (d) test the maximum lateral residual stress of the sample; (e) longitudinal maximum residual stress of the sample is tested; (f) test sample deformation angle

    图  7   适应度变化曲线

    Figure  7.   Curves of fitness changes. (a) BP neural network; (b) GA-BP neural network

    表  1   因素水平表L16(44)

    Table  1   Factor level table for L16(44)

    水平
    编号
    焊接电流
    I/A
    电弧电压
    U/V
    焊接速度
    v/(mm·min−1)
    层间温度
    T/℃
    1A1(140)B1(14)C1(50)D1(80)
    2A2(165)B2(16)C2(90)D2(120)
    3A3(190)B3(18)C3(120)D3(160)
    4A4(215)B4(20)C4(150)D4(200)
    下载: 导出CSV

    表  2   正交试验结果

    Table  2   Results of the orthogonal experiment

    序号焊接电流
    I/A
    电弧电压
    U/V
    焊接速度
    v/(mm·min−1)
    层间温度
    T/℃
    横向最大残余应力
    σx-max/MPa
    纵向最大残余应力
    σy-max/MPa
    角变形
    θ/(°)
    1140145080124.4386.76.18
    21401690120287.6275.87.56
    314018120160237.3241.96.93
    414020150200413.3187.67.42
    51651490200397.6284.37.12
    61651650160444.5365.87.88
    716518120120315.3397.29.33
    81652015080274.3426.38.52
    919014120120356.1468.48.78
    101901615080333.1494.38.81
    111901850200297.6412.69.24
    121902090160183.7471.88.38
    1321514150160244.2356.19.56
    1421516120200174.3337.19.84
    152151890120211.3387.59.36
    16215205080193.5434.89.21
    171631712088163.1136.78.41
    1817518110165123.9256.88.65
    191851510095263.4313.78.93
    2021019135145309.8351.89.43
    下载: 导出CSV

    表  3   神经网络预测平均相对误差

    Table  3   Average relative error of neural network prediction

    输出结果BP GA-BP
    训练样本测试样本训练样本测试样本
    横向最大残余应力12.15%14.56% 2.51%2.38%
    纵向最大残余应力12.52%14.35%2.01%2.84%
    角变形6.8%8.53%1.28%1.32%
    下载: 导出CSV
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  • 收稿日期:  2023-05-19
  • 网络出版日期:  2024-03-08

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