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赵大伟, 王元勋, 梁东杰, YuriyBezgans. 基于功率信号动态特征的钛合金电阻点焊熔核直径预测[J]. 焊接学报, 2022, 43(1): 55-59. DOI: 10.12073/j.hjxb.20210828001
引用本文: 赵大伟, 王元勋, 梁东杰, YuriyBezgans. 基于功率信号动态特征的钛合金电阻点焊熔核直径预测[J]. 焊接学报, 2022, 43(1): 55-59. DOI: 10.12073/j.hjxb.20210828001
ZHAO Dawei, WANG Yuanxun, LIANG Dongjie, Yuriy Bezgans. Prediction of nugget diameter in resistance spot welding of titanium alloys based on dynamic characteristics of power signals[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2022, 43(1): 55-59. DOI: 10.12073/j.hjxb.20210828001
Citation: ZHAO Dawei, WANG Yuanxun, LIANG Dongjie, Yuriy Bezgans. Prediction of nugget diameter in resistance spot welding of titanium alloys based on dynamic characteristics of power signals[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2022, 43(1): 55-59. DOI: 10.12073/j.hjxb.20210828001

基于功率信号动态特征的钛合金电阻点焊熔核直径预测

Prediction of nugget diameter in resistance spot welding of titanium alloys based on dynamic characteristics of power signals

  • 摘要: 为了实时预测0.4 mm厚度的TC2钛合金电阻点焊焊接接头熔核直径,采用罗氏线圈获取焊接过程中的焊接电流的变化情况,利用上、下电极间导线测量焊接过程中电压曲线,在此基础上获取电阻点焊过程中的焊接功率曲线. 分析焊接功率曲线在焊接过程中的变化情况,并从曲线中提取表征焊接质量的特征值. 结果表明,焊接功率曲线不仅与动态电阻曲线的变化相一致,而且与焊接热输入的大小更为相关. 利用焊接功率曲线提取的更为精确的焊接热输入与熔核直径之间的相关系数高达0.9. 通过克里金算法强大的非线性映射能力可以得到用动态功率曲线特征值预测熔核直径的数学模型. 该模型的预测值与实际值之间的最大相对误差约为8.7%,能有效地预测0.4 mm厚度的TC2电阻点焊焊接质量.

     

    Abstract: The power signal in the welding process was employed for the thickness of 0.4 mm TC2 titanium alloy in order to monitor and predict the nugget diameter. The voltage between the upper and lower electrode was obtained using the twisted pair cable and the welding current was acquired based on the Rogowski coil. On this basis, the welding power curve during the resistance spot welding process was obtained. The changes of the welding power curve during the welding process were analyzed, and the characteristic values that characterize the welding quality were extracted. The results show that the welding power curve was not only consistent with the change of the dynamic resistance curve, but also more related to the input of welding heat input. The correlation coefficient between welding heat input, accurately extracted from welding power curve, and nugget diameter is as high as 0.9. A mathematical model for predicting the nugget diameter with the eigenvalues of the dynamic power curve can be obtained through the powerful nonlinear mapping ability of the kriging algorithm. The maximum relative error between the predicted value and the actual value of the model was about 8.7%, which can effectively predict and monitor the welding quality of TC2 resistance spot welding with a thickness of 0.4 mm in real time.

     

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