脉冲激光焊接钛合金薄板的熔池深度预测

激光焊接过程产生的焊斑熔深直接影响焊接质量。激光焊接过程复杂,影响因素众多,许多参数难以量化。以TC4钛合金薄板为实验样品进行脉冲激光焊接实验。通过声波频谱减法对采样的声频信号进行降噪处理,并分析了声频信号的时域和频域特征,找到了声频信号和焊斑熔深的关系。采用径向基函数神经网络对钛合金薄板焊接过程的焊斑熔深进行预测。神经网络以声压强偏差、频带功率、激光功率和焊接速度作为输入。实验证明,在不同激光功率下该方法可准确预测焊斑熔深。

Weld Penetration Depth Prediction of Pulsed Laser Welding Titanium Alloy Thin Plate

Depth of weld penetration is very important to laser welding quality. Laser welding is a complicated process, and quantitative analysis of this process is quite difficult. A set of TC4 titanium alloy thin plate specimens are used as laboratory samples. The acoustic signals are first preprocessed by the spectral subtraction noise reduction method and analyzed in both time and frequency domains, and a valid relationship between the acoustic signals and the weld penetration depth is deduced. Radial basis function neural network (RBFNN) models are developed to predict the weld penetration depth. Sound pressure deviation, band power, laser power and welding speed are used as input variables of RBFNN. The results show that the acoustic signal can characterize and predict the depth of weld penetration well under different laser welding parameters.