色噪声下卡尔曼滤波焊缝跟踪算法与试验研究

Application of Kalman filter with colored noises to the experiment of seam tracking

中文关键词  焊缝跟踪  色噪声  卡尔曼滤波  大功率光纤激光焊

英文关键词  seam tracking  colored noises  Kalman filter  high-power fiber laser welding

中文摘要

      精确控制激光束使其始终对中并跟踪焊缝是保证激光焊接质量的前提. 以大功率光纤激光焊接Type304不锈钢为试验对象, 研究一种有色噪声环境下应用卡尔曼滤波最优状态估计预测激光束与焊缝路径偏差的方法. 使用高速红外视觉传感器摄取焊接区红外热像, 提取焊缝位置参数并构成状态向量, 建立基于焊缝位置参数的系统状态方程和焊缝位置测量方程. 针对系统动态噪声为有色噪声, 通过扩展状态变量的方法建立有色噪声环境下的卡尔曼滤波算法, 对焊缝位置进行最优状态估计并得到最小均方差条件下的焊缝偏差最优预测值, 消除系统噪声对焊缝偏差测量的影响. 焊接试验结果表明新方法可有效抑制有色噪声干扰并提高焊缝跟踪精度.

英文摘要

      Accurately controlling the laser beam to follow the welding path is the prerequisite for successful laser welding quality. During laser butt-joint welding of Type 304 austenitic stainless steel plate with a high power continuous wave fiber laser, an approach is investigated to estimate the seam offset between the laser beam and the weld by using the Kalman filter(KF) optimal state estimation in the presence of colored noises. An infrared sensitive high-speed video camera is used to capture the dynamic images of molten pools. The weld position parameters from a molten pool thermal image are extracted as a state eigenvector which includes the current weld position, the variation of weld position and the weld position at previous sampling time. The state-equation based on the weld position parameters, and the measurementequation for the weld position are established. Considering that the system dynamic noises were colored noises, we develop a KF with colored noises by augmenting the state variables. Therefore, the optimal prediction of the seam offset can be obtained through the optimal state estimation of weld position under the least squares condition. Also, this KF is applied to reduce the error of weld position detection caused by the dynamic noise and the measurement noise; thus, the seam offset between the laser beam and the weld is estimated accurately. Actual welding experiments demonstrate that the seam tracking accuracy can be promoted and the disturbance influence from the colored noises can be reduced by using the Kalman filter with variable augmentation.