一种智能型焊缝跟踪系统的研制

论述一种智能型焊缝跟踪系统,采用直接拍摄电弧式视觉传感器检测焊缝跟踪偏差,并通过一个自调整模糊控制器实现偏差的调节。整个跟踪过程可通过屏幕实时观察,所有的参数设置都通过人机对话实现。此外,还提出了一种新的焊缝图像处理方法来实时检测焊缝。试验表明,该系统能够对GTAW对接焊缝实现精确的跟踪。     

客车底盘骨架对接焊缝的视觉跟踪系统

根据客车底盘骨架对接焊缝的特点,设计了基于激光视觉传感的焊缝跟踪系统。针对图像处理运算量大的特点,为减少图像处理时间,提出了加窗处理算法。视觉传感系统采集并分析焊缝图像得出焊缝偏差,控制系统根据焊缝偏差指导机器人移动焊枪进行实时纠偏控制。实验结果表明,所设计的焊缝跟踪系统满足焊缝跟踪的精度要求,可以用于底盘骨架的焊接。     

基于机器视觉与力控的发动机弯管焊缝自动化打磨系统研究

某系列尺寸不一的弯管由2部分在中间对焊而成,目前多采用人工方式进行弯管内壁焊缝的打磨,存在打磨质量与标准不一、效率低和劳动强度大等问题。为此,设计并实现了一种基于机器视觉技术和力控的发动机弯管焊缝自动化打磨系统。利用工业相机模组正对弯管两侧端面并拍摄图像,经图像处理算法处理计算得到弯管两侧端面焊缝起始位置坐标。机械臂根据该坐标调整打磨轨迹并搭载浮动主轴,保持打磨力的恒定并对焊缝不规则区域进行补偿,实现基于位置控制和力控的打磨自适应调整。试验结果表明,系统整体打磨合格率为96.03%,相比人工打磨效率提升95.90%。系统可以在较少人工的参与和干预下完成弯管焊缝的自动化打磨,具有良好的稳定性和使用柔性,并且允许弯管存在一定的变形。     

一种视觉示教与纠偏的焊缝跟踪方法

提出一种基于结构光视觉传感器的示教及纠偏的机器人焊缝跟踪方法,探讨了图像特征提取方法和机器人跟踪控制的策略.机器人移动的同时,视觉传感器定时对焊缝上各特征点坐标进行测量记录,完成示教;再现时,基于图像模板匹配测出实际特征点位置与示教点的偏差,控制机器人根据偏差对跟踪路径实时调整,从而实现自动跟踪.     

深度视觉技术在焊接轨迹识别中的研究综述

机器视觉在机器人自动焊接领域得到广泛应用。图像识别可以对不同的焊接对象进行有效识别,而识别及跟踪焊缝轨迹是实现自动焊缝的关键问题。分析了焊接轨迹跟踪传感器的发展历程,讨论了视觉传感器在焊缝识别跟踪上的应用,对焊缝图像处理方法进行了分析,总结了视觉焊缝跟踪技术现状。分析结果表明,TOF相机更适合于焊缝跟踪应用,研究鲁棒性强的图像去噪方法和基于深度学习的焊缝图像分割方法是未来的发展方向,同时提出了稳定性好、精度高,效率好的深度视觉焊缝跟踪控制系统思路。     

焊接温度场焊缝识别及焊缝跟踪技术研究

论述焊接温度场焊缝识别方法及焊缝跟踪控制系统。系统采用焊接温度场传感器获得焊接工件背面热图像，通过改进的梯度算法对热图像的分析计算获得焊缝偏差，利用规则自校正模糊控制器实现焊缝跟踪。试验和实际生产运行表明系统能够准确识别焊缝，焊缝跟踪运行稳定可靠。     

基于视觉传感的焊缝跟踪控制系统

介绍了传感器在焊接生产中的应用和视觉传感原理。分析了视觉传感焊缝跟踪系统的原理和特点。提出焊缝图像特征提取方法和流程，以及实施焊缝跟踪控制的方法。系统采用条形光传感方法，选用CCD等光学器件组成焊缝图像传感系统，然后将获取的焊缝图形信息进行识别处理，获得焊接电弧与焊缝中心是否偏离、偏离方向和偏离程度的处理结果。根据这一结果，调整电孤与焊缝中心的相对位置，消除电弧与焊缝的偏离，达到焊接电弧准确跟踪焊缝的目的。     

基于机器视觉的焊接跟踪技术的应用研究

在传统接触式埋弧焊焊缝跟踪系统的基础上,通过摄像机进行焊接坡口复合跟踪。焊接前,对焊接坡口进行数据采集,运用计算机进行图像处理,生成虚拟焊缝识别系统。焊接时,虚拟焊缝识别系统控制焊枪,实现焊缝自动跟踪,从而达到实时和精确．保证了焊接质量。     

一种适合焊接专机的激光焊缝跟踪应用

为确保焊接过程中焊枪始终沿焊缝运动,提升焊接质量,采用基于主动视觉传感技术的新一代激光视觉传感器实时采集焊缝轮廓的图像,由传感器控制柜按在PC界面上选定的算法进行图像处理与特征识别,提取焊缝跟踪点的位置坐标,并根据标定的参考位置和预设的比例关系转化为模拟电压量输出,进而驱动十字滑台上的伺服电机带动焊枪做出相应的纠偏动作。可编程逻辑控制器(PLC)被用来实现焊枪初始定位、滑台的手动控制与自动跟踪模式切换、安全互锁等功能。最终建立了一套适用于焊接专机的焊缝自动跟踪应用系统。实验结果表明,该系统安全实用,具备了良好的实时跟踪能力。     

基于图像处理的焊缝实时跟踪技术研究

为了提高焊接产品质量及生产效率,改善工人恶劣的劳动条件,运用视觉传感器采集焊缝图像,实时传送至计算机中进行图像处理。提出了焊缝实时跟踪系统的原理,设计了合理的焊接机构。选用ABB IRB1410弧焊机器人,用RobotStudio软件建立了机器人焊接工作站。通过灰度线性拉伸法处理图像后,焊缝轮廓清晰,对比度明显提高。利用图像骨架法准确提取到焊缝中心线,设计了焊缝实时处理系统,可以快速地处理图像。在机器人焊接时,控制焊枪始终与焊缝中心对齐,即可以实现焊缝实时跟踪焊接。实验结果表明,得到的图像处理流程可靠,方法准确且适应性强,设计的焊缝实时跟踪系统应用广泛。     

焊缝磨抛机器人视觉算法实现及其试验研究

为实现大型结构件焊后焊缝自动磨抛,磨抛机器人除了具备移动能力和磨抛能力外,更需要在复杂和不规则的工业环境中对焊缝进行准确的跟踪和测量,进而为焊缝磨抛提供有效的加工参数。将两个电荷耦合器件(Charge-coupled device, CCD) 相机和激光器搭载在吸附移动机器人本体上,构成焊缝磨抛机器人视觉系统。根据结构光的亮度分布特征,提出定位图像感兴趣区域(Region of interest, ROI)的算法,减小了计算量,提高了计算效率;提出在噪声干扰条件下快速检测结构光特征线的列高斯差分算法,成功提取结构光特征线;提出差分最值粗定位-距离阈值精定位的焊缝边缘点检测算法,快速、精确提取焊后焊缝的边缘点,计算出焊后焊缝的几何信息。图像处理试验及相应的焊缝磨抛试验结果表明：该视觉系统稳定、可靠,视觉算法快速、精确并且具有很好的鲁棒性,为复杂工业环境下大型结构件焊缝磨抛自动化奠定了基础。     

视觉传感螺旋钢管埋弧焊内焊焊缝自动跟踪系统

针对螺旋钢管埋弧焊内焊,研制一种基于视觉传感的焊缝自动跟踪系统。以视频摄像机为传感器,在焊接点前方300 mm处检测递送边钝边棱边。对视频信号进行放大、整形和二值化处理,获得对应于钝边棱边的脉冲信号,以帧中心为基准,利用单片机进行偏离方向判断及偏差值计算,并将偏差信号以脉冲方式送入交流伺服电动机驱动器,由交流伺服电动机控制焊枪滑移台移动,确保焊丝实时对准焊缝。试验结果表明：采用视频摄像机识别钝边棱边实现焊缝自动跟踪的方法是可行的。系统响应周期小于50 ms,跟踪精度达±0.3 mm。     

Real-time image processing for vision-based weld seam tracking in robotic GMAW

Image capturing and processing is important in using vision sensor to effectively track the weld seam and control the weld quality in robotic gas metal arc welding (GMAW). Using vision techniques to track weld seam, the key is to acquire clear weld images and process them accurately. In this paper, a method for real-time image capturing and processing is presented for the application in robotic seam tracking. By analyzing the characteristic of robotic GMAW, the real-time weld images are captured clearly by the passive vision sensor. Utilizing the main characteristics of the gray gradient in the weld image, a new improved Canny edge detection algorithm was proposed to detect the edges of weld image and extract the seam and pool characteristic parameters. The image processing precision was further verified by using the random welding experiments. Results showed that the precision range of the image processing can be controlled to be within ±0.3 mm in robotic GMAW, which can meet the requirement of real-time seam tracking.     

薄镀锌钢板点焊超声成像分析

采用水浸超声聚焦直入射法对镀锌薄板点焊焊核进行超声C扫描成像检测,分析C扫描图像中各特征部分的超声A扫描信号。超声波C扫描成像方法能够测量点焊焊核直径,检测焊核中常见的几种缺陷如气孔、裂纹以及飞溅,此外采用该方法还能对焊核的外观进行检测。为了获得更为精确的焊核内部结构信息,进一步分析了焊核内部不同形状缺陷的C扫描图像特征及A扫描信号特征,通过这些图像特征和信号特征能够判断焊核内部缺陷的形状及类型。因此,超声C扫描成像法能够在不破坏点焊焊核的前提下全面直观地显示焊核的内外部结构。该方法不仅能够用于评价点焊质量,而且还可以作为辅助手段用于点焊的其他研究中。     

An automated weld seam tracking system for thick plate using cross mark structured light

This paper presents a weld seam tracking system using cross mark structured light. The hardware of the proposed system consists of a two degrees of freedom (DOF) welding robot, a camera with cross mark structured light, and two computers. The system has two parts namely visual sensing and motion control. In the visual sensing part, the cross mark of the structured light is utilized to set a region of interest (ROI). In the ROI, an adapted line fitting algorithm is employed to estimate the lines. Then, intersections of the lines are computed and used as the pin points for templates creating. During the matching process, a modified template matching is used to detect the edges of V-groove weld seam. By using this technique, a huge computational cost in image processing can be reduced, and therefore the tracking can be made in real time. The position based visual servoing with proportional-derivative(PD) and velocity feedback controller is designed for seam tracking. The experimental results show that the proposed method performs the real-time tracking efficiently with sufficient accuracy.     

焊缝跟踪应用的线激光视觉伺服控制系统

设计了一套由三轴直角坐标机器人、线激光传感器和工业计算机组成的焊缝跟踪系统。研究了该系统所涉及的测量原理、特征点测量方法和基于模糊自适应的控制方法。通过高斯核相关算法(KCF)在焊接过程中实时检测焊缝特征点,并根据测量原理计算获得特征点相对于相机坐标系的三维坐标值。设计了一种自适应模糊控制器,通过自适应模糊控制器计算坐标的偏差值和偏差变化率得到焊枪末端运动轨迹的控制量,同时对模糊控制器的输入输出论域、模糊规则和隶属函数进行实时动态更新。实施了焊缝跟踪实验。结果显示:采用最大焊接电流为350 A的惰性气体保护焊(MIG),在强烈弧光和飞溅的干扰下,该系统能实时跟踪焊接工件,跟踪精度为0.325 3mm,传感器测量频率为20Hz。焊接过程中焊枪末端运行平稳,焊缝轨迹跟踪准确,且抗干扰能力,能满足焊接应用要求。     

Seam Tracking Technology for Hyperbaric Underwater Welding

Automatic weld seam tracking technology to be used in hyperbaric underwater damaged pipeline repair welding is much more important, because of poor bevel preparation and severe working condition. A weld seam tracking system based on digital signal processing(DSP) passive light weld image processing technology has been established. A convenient charge coupled device(CCD) camera system was used in the high pressure environment with the help of an aperture and focus altering mechanism to guarantee overall image visibility in the scope of pressure below 0.7 MPa. The system can be used in the hyperbaric environment to pick up the real welding image of both the welding arc and the welding pool. The newly developed DSP technology was adopted to achieve the goal of system real time characteristics. An effective weld groove edge recognition technique including narrow interesting window opening, middle value wave filtering, Sobel operator weld edge detecting and edge searching in a defined narrow area was proposed to remove the guide error and system accuracy was ensured. The results of tracking simulation and real tracking application with arc striking have proved the validity and the accuracy of the mentioned system and the image processing method.     

焊缝图像处理与焊缝中心的提取

随着焊接过程自动化和智能化的发展，图像处理技术越来越受到国内外学者的重视，它在焊缝自动跟踪系统中也有广泛的应用。本文介绍了图像处理的基本过程，其中包括图像预处理，图像分割，边缘检测和特征点提取等图像处理过程，总结了一些传统和新型的图像处理算法，并着重分析了焊缝中心提取的几种方法，提出了一种求取平均值的简单有效的方法。     

A robust visual servo control system for narrow seam double head welding robot

In this paper, an image-based visual servo control system is developed and integrated into a double head welding robot for CO₂ gas shielded arc welding, which is mainly used to weld the narrow seam whose width is generally less than 0.2 mm. Firstly, a robust and reliable image processing algorithm is presented to reliably extract the seam feature during the welding. A statistic-based filter method is proposed to effectively weaken the image noises caused by various disturbances. The region of interest (ROI) of the image is determined to improve the efficiency and reliability of the image processing. The feature line of the narrow seam is extracted by combining the technique of the least-square line fitting with the Hough transform method. Secondly, a visual servo control system is designed to realize accurate tracking and initial point localization of the narrow seam. Moreover, an image error filter and output pulse verification are utilized to guarantee the reliability and stability of the control system. Finally, a series of experiments with real welding robots are conducted in the production line of a container manufacturing factory, demonstrating the satisfactory performance and high welding quality of the proposed visual servo control system.