焊缝X射线实时成象自动分析系统

以射线实时成象检测系统为研究对象,根据射线实时成象的特点,对焊缝缺陷的提取及识别技术进行了研究,采用ＧＦＯ方法进行焊缝缺陷提取,取得了孤效果。制定了一套用于特征描述的参数,给出了具体计算方法,设计了其皇反向传播神经网络进行焊缝缺陷识别的方法,给出了相应的结果。实践证明,该方法比现有真有更好的可靠性和适应性。另旬,对图象的承处理及撮华析修正等均进行了介绍。本文所述方法已用于实际焊接缺陷的检测并取得了     

Real-time automatic detection of weld defects in steel pipe

In order to detect weld defects in steel tube, a real-time imaging and detecting system is setup. In the extracted weld seam, based on spatial characteristics near defects—variance and contrast, defects such as slags, blowholes and incomplete penetration are automatically detected using the method of fuzzy pattern recognition, and the system will automatically alarm if the defect exceeds the national standard. Compared with other methods, it is simple and fast, and has fewer misinterpretations. It can detect weld defects in real-time.     

铝合金焊缝图像的焊接区域提取与缺陷尺寸形状保真

针对焊缝图像的特点,探讨了焊接缺陷自动识别中焊接区域提取的问题,提出了三角拟合法焊道分割、焊道边界滤波修正等方法,并在此基础上,针对焊接缺陷尺寸与形状修正的问题,定义了缺陷矩形与终矩形的概念,设计了缺陷矩形生成策略、缺陷矩形的分割与生长循环、终矩形内缘提取等方法,在焊道区域提取、缺陷的尺寸与形状的保真方面取得了较好的效果。     

Pattern recognition of weld defects detected by radiographic test

In recent years there has been a marked advance in the research for the development of an automatized system to analyze weld defects detected by radiographs. This work describes a study of nonlinear pattern classifiers, implemented by artificial neural networks, to classify weld defects existent in radiographic weld beads, aiming principally to increase the percentage of defect recognition success obtained with the linear classifiers. Radiographic patterns from International Institute of Welding (IIW) were used. Geometric features of defect classes were used as input data of the classifiers. Using a novel approach for this area of research, a criterion of neural relevance was applied to evaluate the discrimination capacity of the classes studied by the features used, aiming to prove that the quality of the features is more important than the quantity of features used. Well known for other applications, but still not exploited in weld defect recognition, the analytical techniques of the principal nonlinear discrimination components, also developed by neural networks, are presented to show the classification problem in two dimensions, as well as evaluating the classification performance obtained with these techniques. The results prove the efficiency of the techniques for the data used.     

激光焊接质量诊断技术的最新进展

激光焊接质量诊断技术对于保证焊接质量、满足生产需要具有重要意义。近年来,多传感器融合、现代模式识别、同轴信号采集等新技术被广泛应用于这一领域,技术的进步使得系统正从简单的过程稳定性监测向质量诊断方向发展。简要介绍了几种典型的激光焊接质量诊断系统,并分析了现有系统的不足及发展方向。     

X射线焊缝图像的缺陷检测与识别技术

针对目前无损检测主要采用人工方式存在的主观不一致、检测效率低、操作复杂等问题,设计了一套焊缝缺陷自动检测系统。提出基于Otsu双阈值分割的缺陷区域自动提取、图像的降噪和灰度增强的图像预处理方法;通过SUSAN算法检测焊缝缺陷目标,并结合形态学孔洞填充算法修正缺陷目标;计算焊缝缺陷目标特征参数,并结合所设计的深度为4的二叉树分类识别逻辑流程,实现了较好的焊缝缺陷的检测结果。     

Estimated accuracy of classification of defects detected in welded joints by radiographic tests

Over the last two decades, there has been a considerably increase in the number of publications of research projects for the detection and classification of welding defects in radiographs using image processing and pattern recognition tools. All these research projects aim to set up an automatic or semi-automatic classification system for weld joint defects detected by the radiographic method. A classification system as such would allow a reduction in some inherent inexactnesses that occur in the conventional method, consequently increasing the precision of the report. This work is a study to estimate the accuracy of classification of the main classes of weld defects, such as: undercut, lack of penetration, porosity, slag inclusion, crack or lack of fusion. To carry out this work nonlinear pattern classifiers were developed, using neural networks. Also the largest number of radiographic patterns as possible was used as well as statistical inference techniques of random selection of data with (Bootstrap) and without repositioning in order to estimate the accuracy of the classification. The results pointed to an estimated accuracy of around 80% for the classes of defects analyzed.     

焊缝表面缺陷激光视觉传感HOG-SVM的检测方法

为了实现对焊缝表面缺陷的自动检测与分类,研究一种有效识别焊缝表面缺陷的激光视觉检测方法.通过激光视觉传感器采集焊缝图像并进行预处理,包括图像分割,灰度化,平滑去噪以及焊缝轮廓提取.采用方向梯度直方图(Histogram of Oriented Gradient, HOG)提取焊缝激光条纹轮廓图像的特征向量.其次,基于5折-交叉验证网格搜索方法进行模型参数寻优,最终建立了支持向量机(Support Vector Machine, SVM)智能模型识别与分类焊缝表面缺陷.通过调整焊缝轮廓提取算法、HOG特征维度得到不同特征数据并进行对比、分析焊缝缺陷的识别效果.在相同试验条件下,发现支持向量机比随机森林分类器、K最近邻分类器以及朴素贝叶斯分类器的识别率更高,达到97.86%.基于HOG-SVM的焊缝表面缺陷智能识别方法可有效提高焊缝缺陷(气孔、凹陷、咬边)及无缺陷的分类精度.     

An adaptive-network-based fuzzy inference system for classification of welding defects

In this paper, we describe an adaptive-network-based fuzzy inference system to recognise welding defects in radiographic images. In a first stage, image processing techniques, including noise reduction, contrast enhancement, thresholding and labelling, were implemented to help in the recognition of weld regions and the detection of weld defects. In a second stage, a set of 12 geometrical features which characterise the defect shape and orientation was proposed and extracted between defect candidates. In a third stage, an adaptive-network-based fuzzy inference system (ANFIS) for weld defect classification was used. With the aim of obtaining the best performance to automate the process of the classification of defects, of all possible combinations without repetition of the 12 features chosen, four were used as input for the ANFIS. The results were compared with the aim to know the features that allow the best classification. The correlation coefficients were determined obtaining a minimum value of 0.84. The accuracy or the proportion of the total number of predictions that were correct was determined obtaining a value of 82.6%.     

焊接缺陷磁光成像磁场分布数值模拟与试验分析

以激光对接焊的焊接缺陷为对象,研究基于数值模拟的焊接缺陷漏磁场的分析方法. 建立对接焊焊接缺陷检测的三维模型,利用漏磁场理论对比分析不同几何缺陷与漏磁场信号之间的关系规律,并用试验进行验证. 结果表明,裂纹的深度越深,磁感应强度越大,未熔合、凹坑分别随着角度、宽度的增大而磁感应强度减小,并且验证漏磁场信号可以作为焊接缺陷检测的依据. 采用RGB分割法对磁光图像进行分割并提取几何特征,用模糊C-均值聚类（FCM）对不同焊接缺陷进行识别,有良好的识别率.