基于被动光的MAG管道打底焊焊缝边缘提取方法

A new seam location extraction method for pipe-line backing welding of MAG based on passive optical vision sensor

焊缝跟踪是实现焊接自动化的前提条件.间隙的变化会影响基于熔化极气体保护焊(metal active gas,MAG)的管道自动打底焊的焊接质量,为此设计了基于电荷耦合器件(charge-coupled device,CCD)视觉传感的MAG打底焊焊接过程监控系统.但从CCD获取的焊接过程的图像通常具有很大噪声,亟需设计出有效的焊缝边缘提取方法,提出了一种新的焊缝边缘提取方法,使用预处理方法获取熔池区域,然后使用Sobel算子检测出候选左(下)焊缝位置作为贪婪Snake模型的初始轮廓控制点,使用贪婪Snake模型拟合焊缝边缘.结果表明,即使在出现噪声的图像中,该方法提取焊缝边缘也是有效的.

Seam tracking is a prerequisite of implementing automatic welding process.Gap variance has an important impact on the welding quality of MAG for pipe-line backing welding.A welding process monitoring system based on CCD is designed.However,the images obtained from CCD with MAG welding often have much noise.Therefore,accurate and high efficient image processing methods for extracting seam location are urgently needed.A new seam location extracting method is presented,the molten welding pool area is obtained after image preprocessing,and then the Sobel transformation method is adopted to choose the initial contour control point for the greedy Snake model.Finally,the greedy Snake model was used to fit the welding seam location.The experimental results show that,the proposed method combined with Sobel transformation and greedy Snake model is also effective to the images with much noise.