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题名: 激光拼焊焊缝表面质量视觉检测系统研究
其他题名: Research on Visual Inspection System of Tailored Blanks Laser Welding
作者: 许敏
导师: 赵明扬
分类号: TG665
关键词: 激光拼焊 ; 结构光视觉 ; 图像处理 ; 特征点识别
索取号: TG665/X78/2011
学位专业: 机械电子工程
学位类别: 博士
答辩日期: 2011-11-28
授予单位: 中国科学院沈阳自动化研究所
学位授予地点: 中国科学院沈阳自动化研究所
作者部门: 装备制造技术研究室
中文摘要: 激光拼焊是采用激光能源,将若干不同材质、不同厚度和不同涂层的材料焊接成一块整体板材,以满足零部件对材料性能的不同要求。激光拼焊技术已经在汽车工业中获得了广泛的应用。激光拼焊过程中焊缝的成形质量直接影响焊缝的机械性能,因此有必要对焊缝质量进行检测。传统的焊缝质量检测方法主要是依靠检测人员使用量规、量尺、放大镜等工具手工来完成,其缺点是检测效率和精度都比较低,并且无法实现在线连续性检测。随着计算机技术和图像处理技术的发展,机器视觉技术逐渐成为了焊缝表面质量检测技术研究的主要方向。目前,德国、美国、加拿大、瑞士等少数西方发达国家研制成功了基于视觉的焊缝表面质量检测系统,极大地提高了焊接自动化程度,而我国目前在这方面的研究还处于实验研究阶段。 本文以中国科学院知识创新工程方向项目“全自动激光拼焊成套装备关键技术研究与示范应用”为依托,针对激光拼焊焊后焊缝表面质量视觉检测系统的关键技术进行了研究,主要研究工作如下: 1.        研究了光纹中心线提取方法,几何中心法与极值法速度快,但抗干扰能力较弱。高斯拟合法、Hessian矩阵法和方向模板法精度较高但计算量较大,难以用于实时性要求很高的激光拼焊焊缝质量检测中。实验证明灰度质心法精度可达亚像素级且速度较快,可以满足焊缝质量实时检测的需要。 2.        提出了一种基于曲率尺度空间的结构光光纹中心线特征点识别方法。针对光纹中心线特征点不明显且易受噪声干扰的特点,首先使用大尺度高斯模板进行滤波并将大于某一阈值的曲率局部极值点定义为特征点候选点,然后跟踪定位候选点到小尺度模板下,以实现特征点在小尺度模板下的精确定位。实验证明基于曲率尺度空间的结构光光纹中心线特征点识别方法能够鲁棒地识别出中心线上的特征点。 3.        提出了一种基于Laws纹理能量法的背面焊缝边界点识别方法。针对拼焊板背面焊缝结构光光纹不发生畸变或者畸变很小的特点,文中采用灰度图像处理方法对背面焊缝图像进行了处理。首先,通过大量实验确定Laws纹理能量法的卷积模板;然后,使用均值滤波器对处理后的背面焊缝图像进行滤波;最后,通过二值法分割出背面焊缝的边界点。大量的实验表明基于Laws纹理能量法的背面焊缝边界点识别方法能够快速、准确地分割出背面焊缝的边界点。 4.        建立了正面焊缝及背面焊缝视觉检测系统,设计了正面检测算法及背面检测算法。实验研究表明本文设计的视觉检测系统可以完成正面焊缝宽度、错配的测量以及背面焊缝宽度的测量。
英文摘要: Tailored blanks laser welding, which is an advanced manufacturing technology, is to weld workpieces with different material, thickness and coating. This technology can satisfy mechanical parts with different material properties and has been used widely in the auto industry. The weld quality has an important influence on mechanical properties of tailored welded blanks (TWB). Hence, it is necessary to inspect the weld quality. Traditional inspection method is to measure the weld by gauges, which is low efficiency and low precision and can not implement online inspection. Fortunately, with the development of computer technology and image processing technology, machine vision inspection technology has been widely used in the field of industrial automation, including inspection of the surface quality of the weld. Currently, some western advanced countries have developed machine vision based weld quality inspection system which significantly improves welding automation. However, domestic research in this area is still in the experimental research stage. This project, the key technologies and demonstration application of production line of automatic tailored blanks laser welding, is supported by Chinese Academy of Sciences. Based on the project, this dissertation focuses on the key technologies of weld quality visual inspection system. Main contributions of the dissertation are summarized as follows: Centerline extraction methods are studied deeply. Geometric Center Method and Extreme Value Method are faster than Grayscale Centroid Method, Gaussian Fitting Method, Hessian Matrix and Direction Template Method, but the anti-jamming performance are worse. Gaussian Fitting Method, Hessian Matrix and Direction Template Method have a higher precision, but they are too computational complexity to be used in online inspection. Experiments demonstrate that Grayscale Centroid Method can satisfy the demands of weld quality visual inspection system with both sub-pixel precision and high-speed operation. The feature points recognition algorithm based on Curvature Scale Space Method are proposed. The feature points of the centerline are inconspicuous and susceptible to noise. The dissertation first filters the centerline with large scale Gaussian template and defines curvature local extreme points greater than a threshold as candidate feature points, and then traces the candidate points to a small template to realize accurate positioning. Experiments demonstrate that the proposed algorithm can robustly recognize the feature points of the centerline. According to the characteristic of no distortion or small distortion of the structured light stripe on the reverse side of TWB, the author proposes an edge points detection method based on Laws Texture. First, the convolution mask of Laws texture energy method is determined through extensive experiments. Then, the mean filter is used to filter the processed image. Finally, the edge points of the reverse side weld are obtained by direct threshold segmentation. A large number of experiments show that this method can detect the edge points of the reverse side weld with higher speed and accuracy. Last, the experimental platforms of the weld quality visual inspection system are established and the geometric parameters, such as bead width and mismatch, are measured. Experiments show that the designed visual inspection system and the image processing method can implement the measurement of tailored blanks laser welding.
语种: 中文
产权排序: 1
内容类型: 学位论文
URI标识: http://ir.sia.cn/handle/173321/9301
Appears in Collections:装备制造技术研究室_学位论文

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Recommended Citation:
许敏.激光拼焊焊缝表面质量视觉检测系统研究.[博士学位论文].中国科学院沈阳自动化研究所.2011
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