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。     

模糊控制在横向埋弧自动焊机焊缝跟踪系统中应用研究

对焊缝跟踪过程中遇到的周期性及偶然性干扰及焊枪摆动中心偏差补偿问题提出针对横向埋弧自动焊的智能焊缝跟踪方法.应用递推平均数字滤波技术进行了滤波处理,采用模糊控制的方法对读取的偏差量进行分析和补偿.并基于PLC,A/D转换模块、激光测距传感器、伺服系统设计了焊缝跟踪系统.经试验调试和现场应用,社会效益及经济效益良好.     

特征谐波法在焊枪姿态检测中的应用

以旋转电弧作为传感器,研究了利用特征谐波法进行焊枪空间姿态检测的若干问题。建立了焊枪空间姿态变化和电弧长度变化的数学模型。从理论上证明了焊枪偏差、焊枪倾角以及焊接方向与焊缝方向的夹角对特征谐波法检测结果的影响。发现当焊接方向与焊缝方向的夹角较小时,可以采用一次谐波的余弦分量系数表征焊枪偏差,正弦分量系数表征焊枪倾角。试验结果表明,将特征谐波法用于焊枪偏差和焊枪倾角的检测,其倾角检测误差为±7.77°,偏差检测误差为±0.69mm,满足实际焊接工程需要。     

移动机器人弯曲角焊缝跟踪预测模糊控制器设计

研究以旋转电弧作为传感器、采用轮式移动机器人对弯曲角焊缝进行跟踪焊接的控制方法.设计预测模糊控制器控制小车转弯,并在转弯过程中使车轮与横向滑块协调动作,实现对弯曲角焊缝的平滑跟踪.该控制器采用焊枪偏差和倾角信息获得车体运动方向和焊缝走向之间的角度偏差,采用线性化模型预测焊枪下一点的位置,采用加权最小二乘方法预测焊缝下一点位置,采用模糊理论实现小车转弯时车轮和横向滑块的协调控制.利用试验对常规模糊控制器和所提出的预测模糊控制器进行比较,发现所提出的控制方法具有更好的控制效果和跟踪精度.     

焊缝特征值提取与分析

在主动式视觉传感器的焊缝跟踪系统中，提出了新的接缝图像处理和特征值提取的算法，详述了直线段的调整、线段拟合算法以及接缝特征值的获取。经过误差分析，焊枪与接缝的实际偏差为0.2mm。     

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。     

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。     

焊缝位姿及焊枪位姿的模型

针对机器人弧焊条件下的特殊要求,建立了焊缝位姿和焊枪位姿模型,以坐标系的形式定量描述焊缝及焊枪的位置和方向,确定了能够准确且严格地描述焊缝的焊接位置和焊枪姿态的参数：焊缝倾角、焊缝转角及焊枪工作角和行走角,并给出了计算方法。上述参数不但计算简便,而且可以直接代表焊接位置和焊枪姿态对焊接质量的影响因素。模型对于焊接工艺的建模与仿真以及机器人焊接的建模与离线编程具有非常重要的意义。     

弧焊机器人焊枪姿态的简便示教

弧焊机器人焊接空间搭接焊缝时,对于焊枪姿态的示教往往占据了大量的示教时间。为此,提出一种焊枪姿态的简便示教方法。该方法采用根据几何约束计算焊接时焊枪姿态的思路完成焊枪姿态的示教。先以任意焊枪姿态对焊缝进行位置示教,然后根据焊缝的切向矢量和焊接工艺要求的焊枪角度计算出焊接时的焊枪姿态。对于三维曲线焊缝,只有当局部焊接平面与基坐标系的Oyz平面不垂直时,需要进行简单的焊枪姿态示教,示教时只须调整机器人绕工具坐标系一个轴转动的角度即可完成。使用该方法可以显著地减少示教难度和示教时间,提高姿态示教的精度。详细介绍焊枪姿态的描述方法、焊接方向角的计算方法和焊枪姿态的调整方法等关键技术。使用搭接曲线焊缝进行试验验证,证明了该方法的可行性。     

Weld deviation detection based on wide dynamic range vision sensor in MAG welding process

Weld deviation detection is a precondition for welding automation. Capturing high-quality welding images and extracting deviation information using image processing methods are two steps for weld deviation detection. In this paper, based on the analysis of the imaging characteristics during metal active gas (MAG) welding process, real-time welding images are acquired clearly and steadily using a wide dynamic range vision sensor. According to the connection relationship between the top of the arc and the end of the wire during MAG welding process, a method for determining the wire centreline is proposed. After extracting the precise arc region, the region of interest (ROI) is segmented along the welding direction. To detect the two edges of the V-groove, an improved Canny algorithm is developed. Furthermore, both the Hough Transform and a screening method based on prior knowledge are used to connect V-groove edges. The V-groove centreline is then determined. As a result, the weld deviation between the wire centreline and the V-groove centreline is calculated. Further experiments showed that the precision range of deviation detection can be controlled within ±0.25 mm, which can meet the requirement of real-time welding. This deviation can be used as the input variable for a welding robot, thus laying the foundation for MAG welding automation.     

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.     

图像处理在焊缝跟踪中的应用研究

随着焊接过程自动化和智能化的发展,基于图像处理技术的焊缝位置检测和焊接缺陷检测过程越来越受到国内外学者的重视。本文对焊缝自动跟踪系统中有关图像处理方面的内容作了分析。详细分析了图像处理技术在焊缝跟踪过程中的应用,其中包括图像预处理,边缘检测和特征点提取等图像处理过程;并对当前焊缝跟踪中的图像处理技术存在的问题和解决方法作了一些总结和分析,最后对其应用前景作了展望。     

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.     

Obtaining weld pool vision information during aluminium alloy TIG welding

An image sensing system for the TIG (tungsten inert-gas arc) welding process of aluminium alloy was established. The relationships between the image sensing system and the characteristic of welding current were discussed in detail. Front and back images of the weld pool were obtained with different welding parameters. In order to process the image, the characteristics of an aluminium alloy were analysed. Image processing and pattern recognition were first used to obtain information from the TIG welding process for aluminium alloy. The image of the weld pool was pre-processed using a series of methods: a weighted median filter, a statistical threshold by expectation and the projection method. A neural network method was used to extract the edge of the images of the weld pool. The result of detecting the edge with a BP neural network were excellent. The symmetry of the weld pool for aluminium alloy was studied when the welding current is large. The edge of the image of the whole weld pool is obtained in a single side image and an accurate method for measuring the weld pool geometry parameter is provided. Experiments show that using the image sensing to control the TIG weld width for aluminium alloy is an effective method.     

对接TIG焊熔池正面几何形状参数的检测

研制出连续电流对接TIG焊熔池正面形状参数实验检测装置。利用普通CCD摄像机,通过光学参数的优化和采集参数的动态调整,拍摄出了清晰度和分辨率都较高的对接TIG焊熔池正面图像。根据对接TIG焊熔池图像特点,设计出图像处理算法,检测出了焊接熔池的整个边缘,得到了不同工艺条件下对接TIG,焊熔池几何形状参数的实际尺寸。     

Visual sensing and penetration control in aluminum alloy pulsed GTA welding

In this paper, visual sensing and penetration control in aluminum (Al) alloy pulse gas tungsten arc welding were researched. Firstly, a three-optical-route visual sensor was designed. The sensor can capture the weld pool from three directions at the same time. After analyzing the influences of different factors on weld pool image, serials of clear and stable weld pool images were obtained. Then, image processing technologies were developed to compute back topside weld pool geometry parameters. Wavelet transform and Canny operator were synthesized to get all edges in the weld pool image. After noise removal and calibration, the breaking edges of weld pool were obtained, and then piecewise curve fitting based on polynomial function were used to recover the whole weld pool edge. Lastly, proportional–integral–differential and a multiplex controller were designed to control penetration in welding process. Experiments proved that visual-based penetration control can insure welding quality well from weld pool width and reinforcement.     

基于图像处理的爬壁机器人焊缝识别与跟踪

基于爬壁机器人移动平台和单目相机的图像采集系统,设计了一种焊后焊缝图像处理方法,将改进的自适应中值滤波算法与灰度形态学方法结合,实现从信噪比较高的图像中提取特征。采用基于边缘检测和Hough变换的焊缝位置提取算法,经测试识别准确率达70%,且单幅图像平均处理时间为200ms,能满足管道爬壁机器人行进过程中的实时焊缝跟踪,并提供了一种引导机器人沿焊缝前进的自主定向方案。     

基于卡尔曼滤波的焊缝检测技术研究

提出一种基于卡尔曼滤波技术的电弧焊焊缝检测新方法。利用视觉传感器获取弧焊区熔池图像,并抽取图像质心作为描述焊缝位置的特征矢量,建立图像质心状态方程和测量方程。在有色噪声模型的基础上,应用卡尔曼滤波对图像质心位置和质心位移进行状态估计,得到最小均方差条件下的焊缝位置最佳预测值,从而减小过程噪声和测量噪声引起的焊缝位置测量偏差,实现弧焊过程中焊缝位置的精确检测。计算机仿真及实际焊接试验结果验证了该方法的有效性。     

Practical method to locate the initial weld position using visual technology

The autonomous localization of initial weld position is one of the key technologies to realize intellectualized welding. This paper presented a practical system and method to guide the welding robot to the initial position of the weld seam. Using template matching and polynomial interpolation technology of pixel, the position of initial weld position is located at the sub-pixel level in the image plane. Simple and practical calibration technology avoids the using of complicated algorithms and costly measuring apparatus. Aluminium and its alloys reflect the light strongly, which make the recognition of the weld seam more difficult than steel. Taking the butt joint weld seam of a curve aluminium alloy sheet as an example, the autonomous localization function is realized accurately. For those instances with bigger errors, we advanced a method to rectify the deviation. The welding robot can be guided to the initial position of planar weld seam and meets the requirements to execute welding operation directly. The whole procedure has the characters of simple, practical, and strong anti-jamming.