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卢晓红, 张昊天, 滕乐, 隋国川. 重型运载火箭燃料贮箱FSW核心区温度监测[J]. 焊接学报. DOI: 10.12073/j.hjxb.20230825002
引用本文: 卢晓红, 张昊天, 滕乐, 隋国川. 重型运载火箭燃料贮箱FSW核心区温度监测[J]. 焊接学报. DOI: 10.12073/j.hjxb.20230825002
LU Xiaohong, ZHANG Haotian, TENG Le, SUI Guochuan. Temperature monitoring of the FSW weld zone of the fuel tank for heavy launch vehicle[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION. DOI: 10.12073/j.hjxb.20230825002
Citation: LU Xiaohong, ZHANG Haotian, TENG Le, SUI Guochuan. Temperature monitoring of the FSW weld zone of the fuel tank for heavy launch vehicle[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION. DOI: 10.12073/j.hjxb.20230825002

重型运载火箭燃料贮箱FSW核心区温度监测

Temperature monitoring of the FSW weld zone of the fuel tank for heavy launch vehicle

  • 摘要: 重型运载火箭燃料贮箱目前通常采用2219-T8高强铝合金,贮箱壁厚高达18 mm,采用搅拌摩擦焊(friction stir welding,FSW)工艺实现筒段纵缝和环缝焊装,FSW核心区极值温度直接影响焊接质量,但由于搅拌头旋转、轴肩遮挡等原因导致FSW核心区温度获取困难. 文中提出了一种FSW核心区极值温度在位表征方法,首先,基于Deform-3D建立了18 mm厚2219-T8铝合金的FSW温度场仿真模型,实现了18 mm厚2219-T8铝合金平板和环焊温度场表征;然后,基于温度场仿真模型,提取焊件表面特征点温度与核心区极值温度的数据集,采用支持向量回归机算法(support vector regression, SVR)建立FSW核心区极值温度预测模型;最后,红外热像仪实时测得焊件表面特征点温度,结合核心区极值温度预测模型,实现贮箱FSW核心区极值温度监测. 试验结果表明,FSW核心区峰值温度监测相对误差<7.22%,核心区最低温度相对误差<9.54%,证实了FSW核心区极值温度监测方法的有效性.

     

    Abstract: At present, 2219-T8 high strength aluminum alloy is usually used in the fuel tank of heavy launch vehicle, and the thickness of the tank is up to 18 mm. Friction stir welding (FSW) is used for longitudinal and girth welding of tank shell. The extreme temperatures of the weld zone directly affect the welding quality, but it is difficult to obtain the temperatures of weld zone during the welding due to the rotation of the tool and the shielding of the shoulder. This paper proposes an in-situ characterization method of extreme temperatures in FSW weld zone. Firstly, based on Deform-3D, the FSW temperature field simulation model of 18mm thick 2219-T8 aluminum alloy is established, which realizes the temperature field characterization of 18 mm thick 2219-T8 aluminum alloy flat plates and girth welding. Then, based on the temperature field simulation model, the data sets of the surface feature point temperature and extreme temperatures of the weld zone are extracted. Combined with the support vector regression algorithm, the extreme temperatures prediction model of the weld zone is established. Finally, the surface temperature of the weldment is measured in real time by infrared thermal imager. Combined with the extreme temperatures prediction model of the weld zone, the extreme temperatures of the weld zone in the FSW process are monitored. The experimental results show that the relative error of the peak temperature detection in the FSW weld area is less than 7.22%, and the relative error of the minimum temperature in the weld area is less than 9.54%, and the results confirm the effectiveness of the extreme temperature detection method in the FSW weld area.

     

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