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P-GMAW起弧过程特征分析及稳定性判别方法

刘文吉, 张亚丰, 王克宽, 岳建锋, 孙勇

刘文吉, 张亚丰, 王克宽, 岳建锋, 孙勇. P-GMAW起弧过程特征分析及稳定性判别方法[J]. 焊接学报, 2024, 45(6): 53-60, 67. DOI: 10.12073/j.hjxb.20230615004
引用本文: 刘文吉, 张亚丰, 王克宽, 岳建锋, 孙勇. P-GMAW起弧过程特征分析及稳定性判别方法[J]. 焊接学报, 2024, 45(6): 53-60, 67. DOI: 10.12073/j.hjxb.20230615004
LIU Wenji, ZHANG Yafeng, WANG Kekuan, YUE Jianfeng, SUN Yong. Arc initiation process characteristics and stability evalution method in P-GMAW[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(6): 53-60, 67. DOI: 10.12073/j.hjxb.20230615004
Citation: LIU Wenji, ZHANG Yafeng, WANG Kekuan, YUE Jianfeng, SUN Yong. Arc initiation process characteristics and stability evalution method in P-GMAW[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(6): 53-60, 67. DOI: 10.12073/j.hjxb.20230615004

P-GMAW起弧过程特征分析及稳定性判别方法

基金项目: 国家自然科学基金资助项目(51975410)
详细信息
    作者简介:

    刘文吉,博士;高级实验师;主要研究方向为焊接自动化,焊接过程质量控制;Email: Liuwenji1981@126.com

  • 中图分类号: TG 409

Arc initiation process characteristics and stability evalution method in P-GMAW

  • 摘要:

    脉冲熔化极气体保护焊(pulsed gas metal arc welding,P-GMAW)起弧过程易产生不稳定现象,会严重影响电弧传感焊缝跟踪精度.针对这一问题,对摆动电弧窄间隙P-GMAW不稳定起弧过程的成因进行了研究,发现送丝速度对起弧过程稳定性具有重要影响. 通过对电弧图像与电信号特征进行对比分析,提取了表征电弧稳定性的电信号特征变量;为减小变量冗余性和过拟合,采用最大似然估计法筛选并提取了8个变量,并通过主成分分析法(principal component analysis, PCA)对变量进行融合,提取了方差贡献率最高的前两个主成分;根据因子载荷发现,相比熔滴过渡阶段和基值阶段,脉冲峰值阶段是电弧更易发生不稳定现象的阶段. 结合提取的主成分变量与二分类Logistic回归模型建立了起弧过程电弧稳定性判别模型. 通过受试者工作特征(receiver operating characteristic,ROC)曲线得到了模型的最佳阈值. 结果表明,该模型对脉冲稳定性判别准确率达到了80%以上,表明模型具有良好的判别性能.该模型对提高窄间隙高低跟踪精度、保证焊接质量具有一定应用价值.

    Abstract:

    The arc initiation process of P-GMAW is prone to instability, which can significantly affect the accuracy of arc sensing and weld seam tracking. In response to this issue, a study was conductedon on the causes of unstable arc initiation in oscillating arc narrow gap P-GMAW, revealing the significant impact of wire feed speed on the stability of the arc initiation process. By comparing and analyzing arc images and electrical signal characteristics, key signal features representing arc stability are extracted. To reduce redundancy and overfitting, a maximum likelihood estimation method is employed to select and extract 8 variables, which are then fused using principal component analysis to extract the top two components with the highest variance contribution. Based on the factor loading, it is found that the pulse peak stage is the phase where the arc is more prone to instability compared to the droplet transition stage and the baseline stage. By combining the extracted principal component variables with a binary logistic regression model, an arc stability discrimination model for the arc initiation process is established. The optimal threshold model was obtained through the ROC curve. Experimental validation shows that the model achieves an accuracy rate of over 80% in pulse stability discrimination, indicating good discriminatory performance. This model holds certain application value in improving the accuracy of narrow gap high-low tracking and ensuring welding quality.

  • 图  1   试验系统示意图(mm)

    Figure  1.   Schematic diagram of experimental system

    图  2   电信号脉冲波形

    Figure  2.   Electric signal pulse waveform

    图  3   高度基准值学习法的焊缝跟踪

    Figure  3.   Weld seam tracking using height reference value learning method. (a) welding torch correction values; (b) welding torch deviations

    图  4   起弧过程焊缝图像

    Figure  4.   Weld seams images during arc initiation processes. (a) stable arcing processes; (b) unstable arcing processes

    图  5   起弧过程电信号

    Figure  5.   Electric signal during arcing processes. (a) stable arcing processes; (b) unstable arcing processes

    图  6   弧长较短时电信号

    Figure  6.   Short arc length electrical signals

    图  7   起弧过程参数变化

    Figure  7.   Parameters change during arc initiation processes. (a) peak current; (b) pulse frequency

    图  8   弧长调节阶段的电信号

    Figure  8.   Electrical signal during arc length adjustment stage. (a) unstable signals caused by short circuits; (b) unstable signals caused by pulse based arc

    图  9   送丝速度对起弧过程稳定性的影响

    Figure  9.   The influence of wire feeding speeds on the stability of arc starting processes

    图  10   变量间相关性检验

    Figure  10.   Correlation test between variables

    图  11   主成分分析结果

    Figure  11.   Principal components analysis result

    图  12   ROC评估曲线

    Figure  12.   ROC evaluation curves

    图  13   模型预测结果

    Figure  13.   Model prediction result. (a) stable arcing processes; (b) unstable arcing processes

    图  14   不同送丝速度下的起弧稳定性概率

    Figure  14.   Probability of arc stability under different wire feeding speeds. (a) wire feeding speed 5.5m/min; (b) wire feeding speed 6m/min

    表  1   焊接工艺参数

    Table  1   Welding parameters

    摆动幅度
    ${S_{\text{w}}}$/mm
    摆动频率
    $f$/Hz
    焊接速度
    $v$/(mm·s−1)
    侧停时间
    $t$/ms
    3 1.8 4 100
    气体流量
    $Q$/(L·min−1)
    送丝速度
    ${v_{\text{f}}}$/(m·min−1)
    焊丝伸出长度
    $l$/mm
    焊丝直径
    φ/mm
    15 6 12 1
    下载: 导出CSV

    表  2   单变量最大似然估计

    Table  2   Univariate maximum likelihood estimation

    变量对数自然函数值 $\ln [L(\theta )]$自由度 ${d_{\text{f}}}$显著性 ${S_1}$
    峰值电流上升斜率 x1189.1084510
    峰值动态电阻 x2194.8986110
    峰值变异系数 x3170.7401810
    熔滴过渡变异系数 x4120.6653910
    基值变异系数 x5194.8568110
    峰值极差 x6162.2291410
    熔滴过渡极差 x7115.9170710
    基值极差 x8196.5704910
    脉冲周期 x9206.1359710
    峰值能量 x10215.5721810
    下载: 导出CSV

    表  3   因子载荷和得分系数

    Table  3   Factors load and scores coefficient

    变量因子载荷得分系数
    主成分F1主成分F2主成分F1主成分F2
    峰值电流上升斜率 ${x_1}$0.72440.47510.202580.00784
    峰值动态电阻 ${x_2}$0.89150.16510.407500.23845
    峰值变异系数 ${x_3}$0.90450.32330.354700.14980
    熔滴过渡变异系数 ${x_4}$0.46090.77590.049610.28493
    基值变异系数 ${x_5}$0.34030.86570.147020.38349
    峰值极差 ${x_6}$0.88210.36000.329090.11968
    熔滴过渡极差 ${x_7}$0.46490.68140.011860.22756
    基值极差 ${x_8}$0.17230.89870.247980.46632
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-06-14
  • 网络出版日期:  2024-05-24
  • 刊出日期:  2024-06-24

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