She Kun, Li Donghui, Yang Kaisong, Li Mingyu, Wu Beile, Yang Lijun, Huang Yiming
School of Electrical and Information Engineering, Tianjin 300350, China.
Tianjin Key Laboratory of Advanced Joining Technology, School of Materials Science and Engineering, Tianjin University, Tianjin 300350, China.
Materials (Basel). 2024 Mar 29;17(7):1580. doi: 10.3390/ma17071580.
The accurate online detection of laser welding penetration depth has been a critical problem to which the industry has paid the most attention. Aiming at the laser welding process of TC4 titanium alloy, a multi-sensor monitoring system that obtained the keyhole/molten pool images and laser-induced plasma spectrum was built. The influences of laser power on the keyhole/molten pool morphologies and plasma thermo-mechanical characteristics were investigated. The results showed that there were significant correlations among the variations of the keyhole-molten pool, plasma spectrum, and penetration depth. The image features and spectral features were extracted by image processing and dimension-reduction methods, respectively. Moreover, several penetration depth prediction models based on single-sensor features and multi-sensor features were established. The mean square error of the neural network model built by multi-sensor features was 0.0162, which was smaller than that of the model built by single-sensor features. The established high-precision model provided a theoretical basis for real-time feedback control of the penetration depth in the laser welding process.
激光焊接熔深的精确在线检测一直是该行业最为关注的关键问题。针对TC4钛合金的激光焊接过程，构建了一个获取小孔/熔池图像和激光诱导等离子体光谱的多传感器监测系统。研究了激光功率对小孔/熔池形貌以及等离子体热机械特性的影响。结果表明，小孔-熔池、等离子体光谱和熔深的变化之间存在显著的相关性。分别通过图像处理和降维方法提取了图像特征和光谱特征。此外，还建立了基于单传感器特征和多传感器特征的多个熔深预测模型。由多传感器特征构建的神经网络模型的均方误差为0.0162，小于由单传感器特征构建的模型。所建立的高精度模型为激光焊接过程中熔深的实时反馈控制提供了理论依据。
