基于视觉传感的药芯焊丝水下焊接焊缝自动跟踪系统
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摘要
随着海洋工程的建设规模越来越大,水下焊接的应用也越来越频繁。出于开发深海资源的需要,水下焊接的施工深度不断加深,因而对水下焊接的自动化提出了迫切的需求。焊缝自动跟踪是焊接自动化的重要内容,论文以实现药芯焊丝水下焊接的焊缝自动跟踪为目标,建立了一套基于视觉传感的药芯焊丝水下焊接焊缝自动跟踪系统,该系统包含视觉传感系统、图像处理及焊缝偏差识别系统、模糊控制器以及执行机构。
在明弧焊的条件下,采用视觉传感器进行焊缝自动跟踪的困难在于如何获取清晰的焊缝图像。通过对药芯焊丝水下焊接电弧光热辐射的分析,结合大量的试验,设计了一套视觉传感系统,该系统采用卤钨灯作辅助光源照射焊接电弧前方一定距离内的待焊焊缝,配合复合滤波片或中性滤光片进行滤光,获取了清晰的焊缝图像。
水下焊接过程中存在着强烈的弧光,气泡的不规则扰动、烟尘的聚集等不利因素,使焊接图象中存在着噪声的干扰。在进行焊缝识别以前需要进行图像的滤波及其它处理。采用中值滤波法对焊缝图像进行滤波可基本消除噪声。许多识别方法是基于二值图像的,本文对焊缝图像的二值化进行了研究,并提出了一种自适应的阈值调整方法。基于二值图像空间阈的灰度值分布的分析,提出了一种识别待焊焊缝偏差的方法。该方法对于图像状况较好的图像(电弧稳定、待焊焊缝边缘平滑、辅助光源照亮区面积较大)识别的准确性和精度高,但当图像状况变差时,识别准确性下降。待焊焊缝在图像中的特征是其边缘的灰度突变,采用Canny 算子进行边缘检测的效果高于传统算子( Sobel、LOG 算子等)。采用Visual C++ 6.0 开发了一套图像处理软件,对焊缝图像进行了上述的各种处理。
采用神经网络对图像中待焊焊缝的偏差进行了识别,提出了两种基于不同策略的神经网络焊缝偏差识别方法: (1) 基于二值图像细化的焊缝偏差识别法; (2) 基于焊缝图像宽度方向灰度值分布的焊缝偏差识别
The use of underwater welding is on the increase as more and moreoffshore structures are being fabricated. In the need of exploring theresources in the deep sea, underwater welding is also used in that deepunder sea. It is strongly expected to weld automatically under that condition.Automatic seam tracking is an important part of weld automation. To trackseam automatically during underwater flux-cored arc welding, avision-based seam tracking system has been fulfilled. This seam trackingsystem concludes a vision sensing system, an image processing and seamrecognizing system, a fuzzy controller and an executive unit.
Under the condition of arc welding, it is difficult to capture idea seamimage. By the analyzing of welding arc spectrum and based on theexperiments, a vision sensing system was built. In this system, ahalogen-tungsten lamp is used to lighting the groove in the front of the arcand a suitable filter is selected. Good seam image can be captured by thisvision sensing system.
Due to the intensive arc light, randomly breaking of the bubble andgathering of the gas in the welding area, the seam images captured concludemany noises and they need to be processed before the seam is recognized.Noises in the seam can be filtered by a median filter. Many imagerecognition methods are based on the binary images. The binarization ofseam image are studied and an adaptive threshold adjusting method areworked out. A seam recognition principle based on the gray leveldistribution in spacial domain of binary image is showed in the paper. Whenthe seam image is in good condition, the recognition is accuracy by thisprinciple. While the seam image is in bad condition, the recognitionaccuracy decreases. The gray gradient at the edge of the seam is a great
在明弧焊的条件下,采用视觉传感器进行焊缝自动跟踪的困难在于如何获取清晰的焊缝图像。通过对药芯焊丝水下焊接电弧光热辐射的分析,结合大量的试验,设计了一套视觉传感系统,该系统采用卤钨灯作辅助光源照射焊接电弧前方一定距离内的待焊焊缝,配合复合滤波片或中性滤光片进行滤光,获取了清晰的焊缝图像。
水下焊接过程中存在着强烈的弧光,气泡的不规则扰动、烟尘的聚集等不利因素,使焊接图象中存在着噪声的干扰。在进行焊缝识别以前需要进行图像的滤波及其它处理。采用中值滤波法对焊缝图像进行滤波可基本消除噪声。许多识别方法是基于二值图像的,本文对焊缝图像的二值化进行了研究,并提出了一种自适应的阈值调整方法。基于二值图像空间阈的灰度值分布的分析,提出了一种识别待焊焊缝偏差的方法。该方法对于图像状况较好的图像(电弧稳定、待焊焊缝边缘平滑、辅助光源照亮区面积较大)识别的准确性和精度高,但当图像状况变差时,识别准确性下降。待焊焊缝在图像中的特征是其边缘的灰度突变,采用Canny 算子进行边缘检测的效果高于传统算子( Sobel、LOG 算子等)。采用Visual C++ 6.0 开发了一套图像处理软件,对焊缝图像进行了上述的各种处理。
采用神经网络对图像中待焊焊缝的偏差进行了识别,提出了两种基于不同策略的神经网络焊缝偏差识别方法: (1) 基于二值图像细化的焊缝偏差识别法; (2) 基于焊缝图像宽度方向灰度值分布的焊缝偏差识别
The use of underwater welding is on the increase as more and moreoffshore structures are being fabricated. In the need of exploring theresources in the deep sea, underwater welding is also used in that deepunder sea. It is strongly expected to weld automatically under that condition.Automatic seam tracking is an important part of weld automation. To trackseam automatically during underwater flux-cored arc welding, avision-based seam tracking system has been fulfilled. This seam trackingsystem concludes a vision sensing system, an image processing and seamrecognizing system, a fuzzy controller and an executive unit.
Under the condition of arc welding, it is difficult to capture idea seamimage. By the analyzing of welding arc spectrum and based on theexperiments, a vision sensing system was built. In this system, ahalogen-tungsten lamp is used to lighting the groove in the front of the arcand a suitable filter is selected. Good seam image can be captured by thisvision sensing system.
Due to the intensive arc light, randomly breaking of the bubble andgathering of the gas in the welding area, the seam images captured concludemany noises and they need to be processed before the seam is recognized.Noises in the seam can be filtered by a median filter. Many imagerecognition methods are based on the binary images. The binarization ofseam image are studied and an adaptive threshold adjusting method areworked out. A seam recognition principle based on the gray leveldistribution in spacial domain of binary image is showed in the paper. Whenthe seam image is in good condition, the recognition is accuracy by thisprinciple. While the seam image is in bad condition, the recognitionaccuracy decreases. The gray gradient at the edge of the seam is a great
引文
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