Prediction of residual stress and deformation of 316L multi-layer multi-pass welding based on GA-BP neural network
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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.
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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
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/℃1 A1(140) B1(14) C1(50) D1(80) 2 A2(165) B2(16) C2(90) D2(120) 3 A3(190) B3(18) C3(120) D3(160) 4 A4(215) B4(20) C4(150) D4(200) 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/(°)1 140 14 50 80 124.4 386.7 6.18 2 140 16 90 120 287.6 275.8 7.56 3 140 18 120 160 237.3 241.9 6.93 4 140 20 150 200 413.3 187.6 7.42 5 165 14 90 200 397.6 284.3 7.12 6 165 16 50 160 444.5 365.8 7.88 7 165 18 120 120 315.3 397.2 9.33 8 165 20 150 80 274.3 426.3 8.52 9 190 14 120 120 356.1 468.4 8.78 10 190 16 150 80 333.1 494.3 8.81 11 190 18 50 200 297.6 412.6 9.24 12 190 20 90 160 183.7 471.8 8.38 13 215 14 150 160 244.2 356.1 9.56 14 215 16 120 200 174.3 337.1 9.84 15 215 18 90 120 211.3 387.5 9.36 16 215 20 50 80 193.5 434.8 9.21 17 163 17 120 88 163.1 136.7 8.41 18 175 18 110 165 123.9 256.8 8.65 19 185 15 100 95 263.4 313.7 8.93 20 210 19 135 145 309.8 351.8 9.43 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% -
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