基于激光视觉传感的CO_2横焊焊缝图像处理研究
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摘要
随着高层建筑钢结构工程建设数量和规模的不断发展,如何在提高钢结构焊接生产质量和效率的同时降低焊接操作工人的劳动强度成为了一个需要解决的关键问题。通过选择合适的焊接方法、焊接材料、焊接电源和设备等可以在一定程度上解决这一问题,但最重要的还是要实现高层建筑钢结构焊接的机械化和自动化。
焊缝自动跟踪是实现焊接自动化,特别是焊接过程自动控制的一个重要内容。本文结合工程实际需要,以厚大板横焊的激光视觉焊缝自动跟踪为研究目标,针对CO_2焊接过程中飞溅等特殊情况,重点对激光视觉焊缝跟踪系统中传感器的设计、焊缝图像处理方法和偏差控制方法等问题进行了研究。
首先,参考国内外相关方面的研究,确定了本研究所设计激光视觉焊缝跟踪系统中焊缝图像采集及处理系统的整体硬件构成以及软件处理流程,特别是确定了激光视觉焊缝跟踪系统中传感器这一核心部件采用激光器倾斜照射,CCD摄像机垂直接收的方式进行焊缝图像采集,二者的夹角约为20°,并从一般情况出发推导出了所设计传感器的空间数学模型。
第二,重点开展了有关焊缝图像处理方面的研究。将自适应中值滤波应用在焊缝图像的去噪处理上;通过对滤波后焊缝图像进行小波变换可以得到不同尺度下不同频带的小波系数,对这些系数分别增强可以在抑制噪声的同时实现焊缝图像的对比度增强;利用最小二乘法找出了基于Otsu算法所确定阈值与焊缝图像中激光带区域像素点灰度平均值之间的关系,从而可以实现针对本研究情况的焊缝图像二值化阈值自动选取。
第三,将抗噪膨胀腐蚀形态学边缘提取算法引入到焊缝图像的边缘提取处理中,通过选取半径为5的圆形结构元素,实现了在提取焊缝图像边缘的同时有效去除图像中的飞溅以及噪声;利用形态学骨架化与轮廓平均相结合的方法实现了在提取焊缝图像中激光带中心线的同时有效去除孤立噪声点;利用斜率分析方法可以较快地提取出坡口及焊缝的特征点,从而进行偏差计算。
第四,采用模糊PID控制与直接模糊控制相结合的控制方法进行了焊缝偏差调节的研究,实现了焊接过程中焊缝的实时跟踪及焊枪位置调节。
最后,通过单层单道和多层多道CO_2横焊焊缝跟踪试验结果可以看出,所设计的焊缝图像处理方法和控制方法等可以满足实际焊缝跟踪的需要。
With constant expanding of the quantity and scale of tall steel building construction project, how to improve the quality and efficiency of steel structure welding and reduce the labor intensity of welder has become a key issue to be resolved. To some extent, the problem can be solved by choosing the appropriate welding methods, welding materials and welding power source, etc. But the most important solution is to achieve mechanization and automation of construction steel structure welding.
Seam tracking is an important element to realize welding automation, especially automatic welding process control. For the practical engineering need, the research target of this paper is to realize automatic laser vision seam tracking of horizontal position welding of large and thick plate. According to the spatter in CO2 arc welding process and other special circumstances, vision sensor design, seam image processing methods and deviation control methods of laser vision seam tracking system are mainly studied.
First, hardware composition and soft flow of seam image acquisition and processing system are determined based on the extensive reference to domestic and international research about laser vision seam tracking system. The slanted laser device and vertical CCD camera are fixed in the vision sensor which is the core component of system. The angle of laser light and CCD camera is about 20. And the models of coordinate transformation between workpiece and camera with arbitrary angle are presented.
Second, the seam image processing is emphatically researched. The adaptive median filter is used to remove most of the seam image noise. Wavelet coefficients of different scale and frequency band can be obtained by using wavelet transform of filtered seam image. Then, through the respective enhancement of these wavelet coefficients, contrast enhancement of seam image can be realized while suppressing the noise. The threshold of seam image binarization can be obtained through curve fitting of the average gray level within laser region and the result of Otsu algorithm based on least square method, thereby realizing the adaptive threshold selection according to different seam images.
Third, the algorithm of anti-noise-dilation-erosion morphological edge detection is introduced to realize edge detection of preprocessed seam image. Using a circular structure element with radius 5, the edge of seam image can be detected while the spatter and noise are effectively eliminated. Then, the centerline of laser region is effectively extracted by using the morphological skeleton and average contour with the removal of isolated noise. Subsequently, the feature points of groove and seam, which are used to calculate deviations, can be rapidly detected using slope analysis method.
Fourth, research on deviation adjustment is carried out using the dual-mode control strategy of adaptive fuzzy PID and fuzzy algorithms. With the research mentioned above, real-time seam tracking and torch position adjustment can be achieved.
Last, the seam tracking results of actual single-layer and single-pass and multi-layer and multi-pass CO2 horizontal position welding testify the effectiveness and reliability of proposed seam image processing and control methods.
焊缝自动跟踪是实现焊接自动化,特别是焊接过程自动控制的一个重要内容。本文结合工程实际需要,以厚大板横焊的激光视觉焊缝自动跟踪为研究目标,针对CO_2焊接过程中飞溅等特殊情况,重点对激光视觉焊缝跟踪系统中传感器的设计、焊缝图像处理方法和偏差控制方法等问题进行了研究。
首先,参考国内外相关方面的研究,确定了本研究所设计激光视觉焊缝跟踪系统中焊缝图像采集及处理系统的整体硬件构成以及软件处理流程,特别是确定了激光视觉焊缝跟踪系统中传感器这一核心部件采用激光器倾斜照射,CCD摄像机垂直接收的方式进行焊缝图像采集,二者的夹角约为20°,并从一般情况出发推导出了所设计传感器的空间数学模型。
第二,重点开展了有关焊缝图像处理方面的研究。将自适应中值滤波应用在焊缝图像的去噪处理上;通过对滤波后焊缝图像进行小波变换可以得到不同尺度下不同频带的小波系数,对这些系数分别增强可以在抑制噪声的同时实现焊缝图像的对比度增强;利用最小二乘法找出了基于Otsu算法所确定阈值与焊缝图像中激光带区域像素点灰度平均值之间的关系,从而可以实现针对本研究情况的焊缝图像二值化阈值自动选取。
第三,将抗噪膨胀腐蚀形态学边缘提取算法引入到焊缝图像的边缘提取处理中,通过选取半径为5的圆形结构元素,实现了在提取焊缝图像边缘的同时有效去除图像中的飞溅以及噪声;利用形态学骨架化与轮廓平均相结合的方法实现了在提取焊缝图像中激光带中心线的同时有效去除孤立噪声点;利用斜率分析方法可以较快地提取出坡口及焊缝的特征点,从而进行偏差计算。
第四,采用模糊PID控制与直接模糊控制相结合的控制方法进行了焊缝偏差调节的研究,实现了焊接过程中焊缝的实时跟踪及焊枪位置调节。
最后,通过单层单道和多层多道CO_2横焊焊缝跟踪试验结果可以看出,所设计的焊缝图像处理方法和控制方法等可以满足实际焊缝跟踪的需要。
With constant expanding of the quantity and scale of tall steel building construction project, how to improve the quality and efficiency of steel structure welding and reduce the labor intensity of welder has become a key issue to be resolved. To some extent, the problem can be solved by choosing the appropriate welding methods, welding materials and welding power source, etc. But the most important solution is to achieve mechanization and automation of construction steel structure welding.
Seam tracking is an important element to realize welding automation, especially automatic welding process control. For the practical engineering need, the research target of this paper is to realize automatic laser vision seam tracking of horizontal position welding of large and thick plate. According to the spatter in CO2 arc welding process and other special circumstances, vision sensor design, seam image processing methods and deviation control methods of laser vision seam tracking system are mainly studied.
First, hardware composition and soft flow of seam image acquisition and processing system are determined based on the extensive reference to domestic and international research about laser vision seam tracking system. The slanted laser device and vertical CCD camera are fixed in the vision sensor which is the core component of system. The angle of laser light and CCD camera is about 20. And the models of coordinate transformation between workpiece and camera with arbitrary angle are presented.
Second, the seam image processing is emphatically researched. The adaptive median filter is used to remove most of the seam image noise. Wavelet coefficients of different scale and frequency band can be obtained by using wavelet transform of filtered seam image. Then, through the respective enhancement of these wavelet coefficients, contrast enhancement of seam image can be realized while suppressing the noise. The threshold of seam image binarization can be obtained through curve fitting of the average gray level within laser region and the result of Otsu algorithm based on least square method, thereby realizing the adaptive threshold selection according to different seam images.
Third, the algorithm of anti-noise-dilation-erosion morphological edge detection is introduced to realize edge detection of preprocessed seam image. Using a circular structure element with radius 5, the edge of seam image can be detected while the spatter and noise are effectively eliminated. Then, the centerline of laser region is effectively extracted by using the morphological skeleton and average contour with the removal of isolated noise. Subsequently, the feature points of groove and seam, which are used to calculate deviations, can be rapidly detected using slope analysis method.
Fourth, research on deviation adjustment is carried out using the dual-mode control strategy of adaptive fuzzy PID and fuzzy algorithms. With the research mentioned above, real-time seam tracking and torch position adjustment can be achieved.
Last, the seam tracking results of actual single-layer and single-pass and multi-layer and multi-pass CO2 horizontal position welding testify the effectiveness and reliability of proposed seam image processing and control methods.
引文
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