正交异性钢桥肋-桥面板焊缝裂纹的三维断裂力学分析

正交异性钢桥的肋-桥面板焊缝处的疲劳裂纹是典型的三维裂纹问题,但是现在普遍采用平面应变二维裂纹模型对其进行断裂力学分析.基于Schwartz-Neuman交替法建立正交异性钢桥肋-桥面板焊缝裂纹的局部三维断裂力学分析模型;评估焊缝处表面裂纹的形状和深度对应力强度因子的影响;采用Paris公式估算等应力幅下焊缝的疲劳寿命.计算结果表明:用平面应变二维裂纹模型进行正交异性钢桥的肋-面板焊缝的断裂力学分析会严重低估其疲劳寿命;采用三维断裂力学模型进行肋-桥面板焊缝裂纹的疲劳寿命分析十分必要.     

基于X射线图像的厚钢管焊缝中气孔缺陷的自动检测

由于厚钢管X射线图像强度分布不均匀,对比度低、噪声大,且气孔缺陷的大小、形状、位置、对比度各异,使得自动检测各种类型的气孔较为困难。针对传统缺陷检测算法中手工标记缺陷数据工作量大,焊缝边缘难以准确提取等问题,提出一种新的无监督学习的各种气孔缺陷检测算法。首先,采用快速独立分量分析从钢管X射线图像集合中学习一组独立基底,并用该基底的线性组合来选择性重构带气孔缺陷的测试图像;随后,测试图像与其重构图像相减获得差异图像,通过全局阈值从差异图像中将各种气孔分割出来。实验的训练集有320幅,测试集有60幅图像,所提算法检测结果的平均敏感性和准确率为90.5%和99.7%。实验结果表明,该算法无需手工标记数据或提取焊缝边缘,可准确检测各种气孔缺陷。     

融合迁移学习的AlexNet神经网络不锈钢焊缝缺陷分类

针对不锈钢焊缝缺陷特征提取存在主观单一性和客观不充分性等问题,提出一种融合迁移学习的AlexNet卷积神经网络模型,用于不锈钢焊缝缺陷的自动分类。首先,由于不锈钢焊缝缺陷数据较为缺乏,通过采用迁移学习对网络前3层冻结,减少网络对输入数据量的要求;对后2层卷积层提取的特征信息批量归一化(batch normalization, BN),以加快网络的收敛速度;并使用带泄露线性整流(leaky rectified linear unit, LeakyReLU)函数对抑制神经元进行激活,从而提高模型的鲁棒性和特征提取能力。结果表明,该模型最终达到了95.12%的准确率, 相比原结构识别精度提高了9.8%。验证了改进后方法能够对裂纹、气孔、夹渣、未熔合和未焊透5类不锈钢焊缝缺陷实现高精度分类。相比现有方法,其识别面更广,精度更高,具有一定的工程实践意义。     

大数据环境下超声波焊缝缺陷识别方法的研究

为了解决常规超声波焊缝缺陷识别方法分类模型固定和训练集规模有限而难以体现不同缺陷的差异性和同类缺陷的多态性的问题,结合当今大数据环境下的数据分析策略和基因缺陷识别中匹配的思想,通过主成分分析和CURE聚类算法将缺陷回波信号编码转换成可进行匹配的对象,进而将当前检测缺陷特征与历史检测数据进行匹配,并利用最近邻方法实现了对缺陷历史检测数据集的扩充。通过在R上应用基于基本空位罚分的Smith-Waterman比对算法进行仿真实验验证了该缺陷识别方法是可行的,有效地识别了气孔、夹渣、裂纹、未焊透和未熔合五类常见缺陷,具有较好的识别准确率。     

基于决策树的焊缝缺陷类型识别研究

自动对射线底片图像进行分析和缺陷类型识别是无损探伤研究领域的一个热点。在对焊缝射线底片进行图像去噪和图像增强的基础上,对焊缝底片图像进行二值化处理,进而提取焊缝缺陷图像及其特征,再采用决策树方法建立焊缝缺陷特征的分类规则,并用这些规则对二值化后的焊缝缺陷图像进行分类识别。实验结果表明,基于决策树方法对焊缝缺陷图像识别的准确率高,而且所表达的知识易于理解。     

基于模糊增强算法的不规则缺陷边缘提取研究

在工业焊缝不规则气孔缺陷的无损检测过程中,由于利用×射线拍摄的数字图像对比度低、噪声大、图像灰度变化复杂,缺陷边缘信息难以提取.针对上述问题,本文首先利用自适应中值滤波消除利用×射线拍摄的数字图像无缺陷的焊缝区域和背景区域,然后利用优化的模糊增强算法对图像边缘进行增强处理,最后分离提取出缺陷区域.通过实验对比,本文采用的方法检测精度高,处理速度快,具有较好的工程应用价值.     

焊缝典型缺陷的超声相控阵检测与评析

当今社会,焊接技术广泛应用于工业设备的制作,焊接质量的好坏对设备的使用安全造成巨大的影响;超声检测是如今无损检测的重要手段之一,它能有效对接头的焊接情况进行检测,从而判断内部是否存在缺陷,检测结果是焊缝质量评价的重要依据;文章对多块坡口形状为“U”型、“X”型、“V”型的焊接试样中的裂纹、夹杂、未熔合等典型缺陷进行CIVA仿真模拟以及超声相控阵检测;首先通过仿真确定了检测工艺,扫查方式、扫查角度以获得更好的信噪比和缺陷可检测性;其次对比16块对焊接试板中多种不同典型缺陷的多次试验检测结果,分析各类典型缺陷的漏判及误判情况;最后对常规超声、相控阵超声、射线检测缺陷的测长结果进行了统计比较,分析影响相控阵超声测长结果的几个因素,从而为超声相控阵在实际焊缝检测中提供更大的可行性及可靠性。     

模式识别在缸体铸件气孔缺陷原因分析方面的应用

利用模式识别优化技术对影响汽车发动机缸体铸件气孔缺陷的工艺参数进行了分析，并找出了影响缸体铸件气孔缺陷的主要因素，给出主要因素的优化范围。本文研究结果表明模式识别优化技术可作为分析汽车发动机缸体铸件的气孔缺陷原因的一条可行途径。     

仿真分析互动社区

问与答CAE应用情况调查表(1)■在ABAQUS/CAE中建立PART时,为什么只能先在X-Y平面上建立模型,可不可以直接建立空间的点、结点线、面或者体?在平面坐标上画二维投影图,然后再生成三维模型,这是ABAQUS的建模思路。在使用CAE过程中,需要熟悉并掌握它的思路。其一般的设计步骤为:把整体结果分解为若干个小部件,然后把每个部件都简化到其平面投影图,通过拉伸(extrude)或旋转(rotate)等操作得到该部件的空间实体,最后把这些部件进行装配(instance)操作,并指定其section特性(就是其材料,厚度等参数),从而构成一个复杂的模型。同一个零件…     

基于投影图像 SURF 特征提取的三维模型配准

针对传统三维模型配准方法存在对点云初始位置有一定要求、模型配准的精度有 时不高等问题,提出了一种基于三维模型投影图像 SURF 特征提取的三维模型配准方法。首先 通过扫描三维模型数据确定投影图像的范围,判断每个投影图像像素所隶属的模型网格,并求 解从投影图像到纹理图像的映射关系,从而获取二维投影图像;然后对这两幅投影图像分别进 行 SURF 特征点的选取与特征值的计算,并按 SURF 特征值进行特征匹配,再根据投影图像像 素点与三维网格端点的映射关系计算三维特征点对;最后通过匹配的特征点对求取模型变换矩 阵完成三维模型的配准。实验结果表明,该方法在配准时间变化不大的前提下,有效提高了配 准精度,并具有较好的鲁棒性。     

Acquisition of Weld Seam Dimensional Position Information for Arc Welding Robot Based on Vision Computing

Recognition and identification of weld environment and seam dimensional position by computer vision is a key technology for developing advanced autonomous welding robot. Aiming at requirements for recognition of weld seam image characteristics, this paper first presents an improved algorithm of subpixel edge detection based on Zernike moments. Comparing with the Ghosal’s original algorithm, the improved algorithm deals with mask effect and first derivative model on edge gradient direction so that it has the strong robust to noise, self-thinning ability and higher locating precision. An algorithm based on ZMs to extract line is also proposed, the comparative results with SHT and RHT show the method has the highest calculation speed and accuracy. The stereovision technology is developed to identify dimensional position of weld seam by computing dimensional coordinates of the weld seam. According to characteristics of weld seam, view field scope model and stereovision model based on baseline are studied and a stereo matching method is presented. In order to evaluate the algorithms and models presented in this paper, a welding robot systems with single camera fixed on the weld torch end-effector has been established for the robot to identify the dimensional position of typical weld seam by one-item and two-position method. The experiment results on S-shape and saddle-shape weld seams show that the vision computing method developed in this paper can be used for acquiring weld seam dimensional position information in welding robot system. Thus the welding path is mapped before the welding operation is executed.     

A robust weld seam recognition method under heavy noise based on structured-light vision

Structured-light vision systems are widely used in robotic welding. The key to improving the robotic visual servo performance and weld quality is the weld seam recognition accuracy. Common detection algorithms are likely to be disturbed by the noise of spatter and arc during the welding process. In this paper, a weld seam recognition algorithm is proposed based on structured light vision to overcome this challenge. The core of this method is fully utilizing information of previous frames to process the current frame, which can make weld seam extraction both more robust and effective. The algorithm can be divided into three steps: initial laser center line recognition, online laser center line detection, and weld feature extraction. A Laplacian of Gaussian filter is used for recognizing the laser center line in the first frame. Afterwards, an algorithm based on the NURBS-snake model detects the laser center line online in a dynamic region of interest (abbreviated ROI). The center line obtained from first step is set as the initial contour of the NURBS-snake model. Using the line obtained from the previous step, feature points are determined by segmentation and straight-line fitting, while the position of the weld seam can be calculated according to the feature points. The accuracy, efficiency and robustness of the recognition algorithm are verified by experiments.     

基于直线段特征单元提取的隧道砼衬砌接缝去除算法

由于部分隧道砼衬砌表面图像中固有衬砌接缝与裂缝灰度值相似且线性一致,衬砌接缝处易产生起砂、空鼓、掉块及渗漏水,已有的裂缝检测算法提取单一的裂缝存在缺陷。提出基于直线段特征单元提取的隧道砼衬砌表面衬砌接缝去除算法。在裂缝聚类特征粗检测的基础上,首先通过改进的累计概率霍夫变换检测出显著的直线特征;然后利用像素点的延伸搜索计算来提取衬砌接缝可处理的最小直线段特征单元线;最后根据单元线标记信息及定区域内单元线特征去除部分衬砌接缝,并运用渗流去噪算法得到隧道砼衬砌表面真实裂缝。实验结果表明,提出的算法弥补了已有隧道砼衬砌表面裂缝检测技术的不足,能够精确、快速、有效地去除相似线性特征对单一的真实裂缝检测的干扰,具有较强的鲁棒性。     

基于小波变换的X射线焊缝图像缺陷识别方法研究

经过长时间的研究发现,在焊缝图像缺陷识别中,传统的方法具有正确识别率低的问题,为此提出了基于小波变换的图像缺陷识别方法。输入获取的X射线焊缝初始图像信息,从帧数叠加、数字形态学变换和图像增强三个方面对初始图像进行预处理,约束小波变换阈值降低图像噪声,最后通过边缘检测,提取焊缝图像当中的缺陷特征。在对比实验当中,设立两种传统识别方法作为实验的对照组,提出的X射线焊缝图像缺陷识别方法为实验组,同时对统一型号的焊接工件进行缺陷识别,实验发现提出的识别方法的正确识别率高达91.8%。     

A novel system for off-line 3D seam extraction and path planning based on point cloud segmentation for arc welding robot

Due to ever increasing precision and automation demands in robotic welding, the automatic and robust 3D seam extraction has become a research hot-spot of the welding robots. At present, most of the research work about seam extraction is aimed at butt joints. Nevertheless,too little work has been devoted to fillet joints and lap joints. Consequently,to achieve robust 3D seam extraction of different weld seams, a novel seam extraction system is proposed according to the 3D structures of welding work pieces. Firstly, a fringe projection system based on Digital Light Processing(DLP) projector is designed to measure the appearance of welding work pieces. Secondly, fusion of the shape information of welding work piece, a 3D seam extraction algorithm is proposed based on point cloud segmentation. Finally, according to the space structure of weld seams, the 3D seam path model and pose estimation are solved based on the established mathematical model of weld seams. Experiments show that the proposed algorithm could well solve different weld seams, such as fillet joints, butt joints and lap joints. Meanwhile, it could well overcome the influence of the materials of welding work pieces, scratch and rust.     

Parameter Self-Optimizing Clustering for Autonomous Extraction of the Weld Seam Based on Orientation Saliency in Robotic MAG Welding

This paper presents an effective method which needs free parameters as little as possible to autonomously extract the weld seam profile and edges from the molten background in two kinds of weld images within robotic MAG welding. First, orientation saliency detection produced by Gabor filtering nicely highlights the weld seam profile and edges from the molten background. Then, an unsupervised clustering algorithm combing a cluster validity index via an optimization rule, referred to as parameter self-optimizing clustering, is applied to discern the weld seam profile and edges from interference data after the orientation saliency detection result is given threshold segmentation. The validity index is better than the classical ones in two kinds of data sets through considerable tests. Last, two common applications of weld seam identification demonstrate the effectiveness of the proposed method.     

卡尔曼滤波焊缝跟踪控制

在视觉传感的电弧自动焊接过程中,需要根据视觉信息来控制电弧准确地跟踪焊缝.由于强烈的弧光干扰,使得从焊接区图像中直接提取电弧与焊缝的偏差信息十分困难.为此提出一种利用熔池图像质心和卡尔曼滤波来间接获取电弧与焊缝偏差的方法.选择熔池图像质心作为状态向量,建立基于图像质心的状态方程和焊缝位置测量方程.利用卡尔曼滤波消除过程噪声和测量噪声的影响,通过对熔池图像质心的状态估计,准确获取焊缝位置以及电弧与焊缝之间的偏差量,为自动焊接过程的焊缝跟踪控制提供准确信息.焊接试验结果表明,利用卡尔曼滤波方法可有效降低过程噪声和测量噪声的影响,从而提高焊缝跟踪控制精度.     

Computer vision technology for seam tracking in robotic GTAW and GMAW

Due to ever increasing demand in precision in robotic welding automation and its inherent technical difficulties, seam tracking has become the research hotspot. This paper introduces the research in application of computer vision technology for real-time seam tracking in robotic gas tungsten arc welding (GTAW) and gas metal arc welding (GMAW). The key aspect in using vision techniques to track welding seams is to acquire clear real-time weld images and to process them accurately. This is directly related to the precision of seam tracking. In order to further improve the accuracy of seam tracking, in this paper, a set of special vision system has been designed firstly, which can acquire clear and steady real-time weld images. By analyzing the features of weld images, a new and improved edge detection algorithm was proposed to detect the edges in weld images, and more accurately extract the seam and pool characteristic parameters. The image processing precision was verified through the experiments. Results showed that the precision of this vision based tracking technology can be controlled to be within ±0.17 mm and ±0.3 mm in robotic GTAW and GMAW, respectively.     

Adaptive parameter optimization approach for robotic grinding of weld seam based on laser vision sensor

Automatic robot grinding technology has been widely applied in the modern manufacturing industry. A flexible abrasive belt wheel used to grind the weld can avoid burns on the base material and improve the processing efficiency. However, when the robot grinds a weld seam, the material removal depth does not coincide with the feed depth because of the soft contact and uneven weld height, affecting the weld seam surface uniformity. Given these problems, an adaptive parameter optimization approach for the robotic grinding of a weld seam was proposed based on a laser vision sensor and a material removal model. First, the depth of weld seam removal was obtained by a laser vision sensor based on triangulation in real-time. Then, a macroscopic material removal model considering flexible deformation was established to determine the relationship between the weld height and process parameters, and the model coefficient was experimentally fitted to ensure the accuracy and reliability of the model. In addition, the data of real-time interaction structure between the robot controller and grinding system were obtained and used to unsure that the rotational speed of the belt wheel increased in the convex part and decreased in the concave part, in order to obtain a uniform weld seam surface. Comparative experiments were performed to verify the effectiveness and superiority of the method, and experiments on the surface roughness and weld seam surface height difference were conducted to verify the universality of the method. Experimental results show that the residual height of the weld after grinding can be controlled within 0.2mm, and the maximum removal height difference can be controlled within 0.05mm. The surface roughness Ra of the weld after grinding could reach 0.408 µm.     

A tactual weld seam tracking method in super narrow gap of thick plates

The contact-type displacement and angular sensors were improved and utilized in weld seam trajectory detection. A detection–compensation–tracking system was developed. The mechanical part of this system was installed and independent of the robot, which can realize the detection of right-left deviation and up–down offset of the weld path. In the experiment, the position coordinates of the detection point in weld groove were calculated and weld seam tracking was carried out simultaneously owing to its single control system. When the absolute interpolation algorithm was adopted, the average error of width deviation and depth deviation were 0.1817 mm and 0.1449 mm, respectively.