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.
-
Keywords:
- titanium alloy /
- resistance spot welding /
- dynamic curve /
- nugget size
-
-
表 1 焊接试验结果
Table 1 Experimental results for the resistance spot welding
电极压力
F/N焊接电流
I/kA焊接时间
t/ms熔核直径
D/mm抗剪力
R/N失效能量
Q/J100 1.4 6 0.85 1021.93 0.5 100 1.8 8 1.95 2426.85 3.18 75 1.6 8 1.63 2014.65 1.96 75 2.2 12 2.12 2685.03 3.92 125 1.0 8 1.22 1321.58 0.89 125 1.6 12 1.91 2240.36 3.51 125 2.2 4 1.68 1896.3 2.16 175 1.0 12 1.37 1193.1 0.51 175 1.6 4 1.28 1569.83 0.43 175 2.2 8 1.96 2883.94 4.36 表 2 特征量与熔核直径之间的相关系数
Table 2 Correlation coefficients among extracted features and nugget diameter
功率上升值
ΔP/kW功率下降值
ΔP1/kW功率下降率
Ps焊接热输入
E/J0.52 0.81 0.87 0.90 -
[1] 杨龙, 杨冰, 阳光武, 等. 不锈钢车体点焊接头疲劳特性分析[J]. 焊接学报, 2020, 41(7): 18 − 24. doi: 10.12073/j.hjxb.20191204005 Yang Long, Yang Bing, Yang Guangwu, et al. Analysis on fatigue characteristics of spot welded joints of stainless steel car body[J]. Transactions of the China Welding Institution, 2020, 41(7): 18 − 24. doi: 10.12073/j.hjxb.20191204005
[2] Xia Y J, Shen Y, Zhou L, et al. Expulsion intensity monitoring and modeling in resistance spot welding based on electrode displacement signals[J]. Journal of Manufacturing Science and Engineering, 2021, 143(3): 031008. doi: 10.1115/1.4048441
[3] Summerville C, Adams D, Compston P, et al. Nugget diameter in resistance spot welding: a comparison between a dynamic resistance based approach and ultrasound C-scan[J]. Procedia Engineering, 2017, 183: 257 − 263. doi: 10.1016/j.proeng.2017.04.033
[4] Bag S, DiGiovanni C, Han X, et al. A phenomenological model of resistance spot welding on liquid metal embrittlement severity using dynamic resistance measurement[J]. Journal of Manufacturing Science and Engineering, 2020, 142(3): 031007. doi: 10.1115/1.4046162
[5] Guan S, He X, Wang X, et al. Multiphysics simulation of the resistance spot welding detection using electromagnetic ultrasonic transverse wave[J]. The International Journal of Advanced Manufacturing Technology, 2020, 110(1): 79 − 88.
[6] 陈树君, 郝键, 李方, 等. 铝合金电阻点焊压力信号的动态特征分析[J]. 焊接学报, 2020, 41(3): 1 − 6. doi: 10.12073/j.hjxb.20190124002 Chen Shujun, Hao Jian, Li Fang, et al. Dynamic characteristics analysis of resistance spot welding pressure signal of aluminum alloy[J]. Transactions of the China Welding Institution, 2020, 41(3): 1 − 6. doi: 10.12073/j.hjxb.20190124002
[7] 曾凯, 孙晓婷, 邢保英, 等. DP780高强钢胶接点焊的工艺优化及断裂特征分析[J]. 焊接学报, 2020, 41(4): 77 − 83. doi: 10.12073/j.hjxb.20191017001 Zeng Kai, Sun Xiaoting, Xing Baoying, et al. Process optimization and fracture characteristic analysis of DP780 high strength steel weld-bonding[J]. Transactions of the China Welding Institution, 2020, 41(4): 77 − 83. doi: 10.12073/j.hjxb.20191017001
[8] Bin W, Shuili G, Li C, et al. Microstructure and mechanical properties of TC2 titanium alloy laser welded joints[J]. Rare Metal Materials and Engineering, 2013, 42: 136 − 138.
[9] Fatmahardi I, Mustapha M, Ahmad A, et al. An exploratory study on resistance spot welding of titanium alloy Ti-6Al-4V[J]. Materials, 2014, 14(9): 2336.
[10] 孔谅, 刘思源, 王敏. 先进高强钢电阻点焊接头的断裂模式分析与预测[J]. 焊接学报, 2020, 41(1): 12 − 17. Kong Liang, Liu Siyuan, Wang Min. Failure mode analysis and prediction of resistance spot welding joints of advanced high strength steel[J]. Transactions of the China Welding Institution, 2020, 41(1): 12 − 17.
[11] Zhao D, Ivanov M, Wang Y, et al. Welding quality evaluation of resistance spot welding based on a hybrid approach[J]. Journal of Intelligent Manufacturing, 2021, 32: 1819 − 1832. doi: 10.1007/s10845-020-01627-5
[12] García-Gutiérrez A, Gonzalo J, Domínguez D, et al. Stochastic optimization of high-altitude airship envelopes based on kriging method[J]. Aerospace Science and Technology, 2020, 120: 107251.
[13] 吕小青, 王旭, 徐连勇, 等. 基于组合模型的MAG焊工艺参数多目标优化[J]. 焊接学报, 2020, 41(2): 6 − 11. doi: 10.12073/j.hjxb.20190629001 Lü Xiaoqing, Wang Xu, Xu Lianyong, et al. Multi-objective optimization of MAG process parameters based on ensemble models[J]. Transactions of the China Welding Institution, 2020, 41(2): 6 − 11. doi: 10.12073/j.hjxb.20190629001