高速GMAW驼峰焊道形成过程熔池图像识别

针对高速驼峰焊道形成过程中熔池的变化规律，采用CCD视觉传感系统跟踪采集. 提出了一种基于模糊C-均值聚类(fuzzy C-means，FCM)协作主动轮廓(chan-vese，CV)模型的熔池图像分割方法，对高速焊接过程中的熔池图像进行图像分割. 结果表明，驼峰的形成过程中，熔池长度的阶跃变化是反映驼峰形成的主要图像特征. 将熔池长度序列拟合成波形，采用Symlets2号小波进行分解，发现d₂级小波分解能更好地识别熔池长度的阶跃变化. 对d₂级小波细节能量设定阈值，获取反映熔池长度阶跃变化的尖峰突起特征信号，能很好地识别驼峰缺陷的形成，初步实现了驼峰焊道的监测控制.

Weld pool image recognition of humping formation process in high speed GMAW

Aiming at the change rule of weld pool during the humping formation process in high speed GTAW, CCD vision system was used to track and collect. A segmentation method based on Fuzzy C-Means collaborative active contour model was proposed to segment the weld pool image. The results show that the step change of the weld length was the main image feature reflecting the humping formation. The weld length sequence was fitted into waveform and decomposed by Symlets2 wavelet. It was found that d₂ wavelet decomposition can identify the step change of weld length. Threshold was set on the d₂ wavelet to obtain the spike characteristic signal which reflected the step change of the well weld length. The formation of the hump defect can be recognized well and the monitoring and control of the humping bead can be realized preliminarily.