Welding Deviation Detection Algorithm Based on Extremum of Molten Pool Image Contour

The welding deviation detection is the basis of robotic tracking welding, but the on-line real-time measurement of welding deviation is still not well solved by the existing methods. There is plenty of information in the gas metal arc welding（GMAW） molten pool images that is very important for the control of welding seam tracking. The physical meaning for the curvature extremum of molten pool contour is revealed by researching the molten pool images, that is, the deviation information points of welding wire center and the molten tip center are the maxima and the local maxima of the contour curvature, and the horizontal welding deviation is the position difference of these two extremum points. A new method of weld deviation detection is presented, including the process of preprocessing molten pool images, extracting and segmenting the contours, obtaining the contour extremum points, and calculating the welding deviation, etc. Extracting the contours is the premise, segmenting the contour lines is the foundation, and obtaining the contour extremum points is the key. The contour images can be extracted with the method of discrete dyadic wavelet transform, which is divided into two sub contours including welding wire and molten tip separately. The curvature value of each point of the two sub contour lines is calculated based on the approximate curvature formula of multi-points for plane curve, and the two points of the curvature extremum are the characteristics needed for the welding deviation calculation. The results of the tests and analyses show that the maximum error of the obtained on-line welding deviation is 2 pixels（0.16 ram）, and the algorithm is stable enough to meet the requirements of the pipeline in real-time control at a speed of less than 500 mm/min. The method can be applied to the on-line automatic welding deviation detection.

基于改进ViT的熔池识别与焊接偏差在线检测方法

焊接偏差的精确检测是实现焊接机器人焊缝轨迹自动跟踪及智能化焊接的前提。提出了一种基于改进视觉转换器(Vision Transformer,ViT)的熔池识别与焊接偏差在线检测方法。首先,采用轻量级ViT模型Segformer作为基线模型,在其掩码分割前嵌入置换注意力(Shuffle Attention,SA)机制,以更好地捕获特征信息在空间和通道这2个维度中的依赖关系,从而提高模型的分割精度;其次,在多层感知机(Multilayer Perceptron,MLP)中加入上下文广播(Context Broadcasting,CB)模块,在保证模型低参数量的前提下提高泛化能力;最后,基于模型分割结果,提出一种焊接偏差计算方法来定量描述偏差检测精度。实验结果表明,相较于基线模型,所提出模型的平均交并比和平均像素准确率分别提高了2.67%和2.12%,且对于不同预设焊枪偏移情况均具有良好的泛化性,焊接偏差精度控制在±0.021 mm之内,为实现精密焊接焊缝跟踪提供基础。

基于熔池图像尖端特征规律的焊接偏差测定方法

通过对管道全位置熔化极气体保护焊(gas metal arc welding,GMAW)熔池图像的研究分析,发现并验证了其根焊熔池图像尖端与焊缝坡口中心位置重合的现象,依据此规律性特征,提出了一种基于熔池图像尖端信息的焊接偏差测定方法.该方法的基本原理是,在对焊接过程熔池CCD图像进行中值滤波、小波变换、连通区域分割等图像处理后,以搜索算法测得的根焊熔池图像尖端位置信息作为焊缝坡口中心位置的坐标值,此值与焊丝中心坐标值之差即为焊接偏差量.试验证明,此方法能从根焊熔池图像中实时测定焊接偏差量,为实现机器人自动焊缝跟踪控制提供了可靠依据.

基于焊枪轮廓特征提取的焊接偏差测定方法

在熔化极气体保护焊(gas metal welding,GMAW)焊接过程中,由于弧光干扰严重,视觉系统难以同时准确提取焊缝和焊丝尖端,从而影响焊缝跟踪的精度.针对这个问题,提出一种定位焊枪中心来替代定位焊丝尖端的焊接偏差测定方法,并对该方法进行了可行性论证.首先,在增强熔池图像中的焊缝、焊枪边缘轮廓信息后,设置矩形窗获得边缘采样点.然后,使用聚类算法筛选出正确的边缘采样点,根据采样点利用最小二乘法拟合出焊缝直线和焊枪椭圆方程.最后,计算当前图像焊枪中心与焊缝直线的距离,与基准图像中的对应距离进行比较,测定出焊枪位置偏差量和焊枪摆幅偏差量.实际验证结果表明,焊枪中心与焊丝尖端的替代误差在0.2 mm以内,满足跟踪精度要求,具有较强的工程实际意义.

活炉底转炉熔池接缝处溅渣层高效清除的生产实践

针对马鞍山钢铁股份有限公司(以下简称“马钢”)65 t顶底复吹转炉在炉役检修时,熔池接缝处溅渣层清除不彻底,炉底与炉身不能有效分离,二次分离作业又存在较大安全风险的问题,提出了具体的解决措施。生产实践表明:利用预留在转炉内炉渣中铁元素与氧气反应,通过对氧枪枪位、供氧强度、氧气累计量以及浸泡侵蚀时间的合理控制,达到了熔池接缝处溅渣层高效清除的目的,洗炉作业时间可有效控制在30 min以内,为转炉后续拆炉作业提供安全保障。

一种基于三维空间的焊缝跟踪精度评价分析方法

为了提高弧焊机器人的焊缝跟踪精度,提出了一种新的焊缝跟踪精度的评价方法,建立了基于三维空间焊缝的精度评价模型,该模型建立了焊接电流、焊接速度和焊枪高度等多个焊接工艺参数与焊缝跟踪精度之间的模型关系.基于该模型,通过MATLAB对该模型进行仿真分析,验证了所提出精度分析方法和精度模型的正确性及有效性.结果表明,在焊缝跟踪过程中焊接电流和焊接速度对焊缝跟踪精度影响明显.该精度分析方法对于焊缝跟踪精度的评价具有一定的参照意义,并且在焊接工艺参数的合理选择时对焊缝跟踪精度具有重要影响.