基于机器视觉的膨胀阀安装孔缺陷检测方法

针对工业生产中膨胀阀安装孔缺陷自动检测的需求,提出一种基于机器视觉的安装孔缺陷检测方法。使用SURF特征匹配定位待检测区域,利用双阈值迟滞分割法去除噪声;通过Canny算子计算轮廓边缘,利用邻域生长法提取轮廓信息。为克服缺陷信息的干扰,准确定位安装孔圆心,提出基于几何矩与最小二乘拟合的圆心定位方法。根据轮廓上的点到圆心的距离分割出缺陷轮廓并在输入图像上进行标注,从而实现安装孔缺陷的检测。结果表明：所提出的算法能准确检测膨胀阀安装孔的缺陷,满足工业生产中自动检测的需求。     

阴极电泳漆膜常见缺陷产生的原因与解决方法

介绍了阴极电泳涂装中常出现的缺陷类型,主要有粗糙、针孔、缩孔、杂质和气泡等.分别分析了其出现的原因,提出了相应的解决方法.为开展阴极电泳研究及解决实际生产中出现的缺陷问题提供了参考.     

带裙座压力容器封头成形数值模拟模块的开发

为分析带裙座整体封头的成形工艺 ,在商用有限元软件 ANSYS上二次开发了专用的封头成形分析模块。该模块根据封头的尺寸参数 ,采用参数化方法建立有限元模型。根据数值模拟结果 ,分析了封头成形缺陷的产生原因及坯料形状对成形过程的影响     

Directivity analysis of ultrasonic waves on surface defects using a visualization method

Ultrasonic testing uses the directivity of the ultrasonic wave, which propagates in one direction. The directivity is expressed as the relationship between the propagation direction and its sound pressure. The directivity of an ultrasonic wave is related to the choice of probe arrangement, testing sensitivity and scanning pitch, and correct measurement of defect size and location. This paper investigated the directivity of ultrasonic waves, which are scattered from a slit defect located in simulated butt weld joint using a visualization method. When the defect size was smaller than the wavelengths, clear directivity in the reflected wave was observed. When the ratio of defect size to wavelength is greater than 1.5, measured directivities almost agree with the theoretical directivity. The directivity of shear waves scattered from the slit defect varied according to probe direction (Far defect, Near defect). The angle of reflection wave became similar to angle of incidence as the height of excess metal in welded joint increased.     

特大型零件热态成形制造技术基础研究

在中国国家自然科学基金重点项目《特大件成形制造技术基础研究》、国家科技支撑计划课题《大型轧机共性技术》、国家重点基础研究发展计划(973计划)前期研究专项《大型零件热态成形制造虚拟技术基础研究》,以及中国河北省科技攻关项目《大型轧机共性技术》和《特大件成形制造技术基础研究》共同资助下,以大型锻钢轧辊(辊身直径超过1 000 mm或质量大于100 t)为对象,通过宏观 微观强耦合建模,解决铸造缺陷信息遗传、孔洞锻合条件、形变焊合条件、材料断裂准则构建,以及热 力 微观组织耦合建模等关键技术问题,建立以淬硬层深度数值预测技术为核心的热处理工艺分析与优化系统,提出基于非均质轧辊辊间接触力学模型的轧辊强度设计方法,构建起大型锻钢轧辊热态成形制造与服役评估的多学科耦合决策支持理论体系,提出理论研究报告。针对缩孔和气孔等大型铸钢锭铸造中的孔洞缺陷控制,以大锻件内部孔洞锻造闭合过程为科学问题,以大锻件内部有效锻合区域数值预测为目标,将有限元方法与人工神经网络技术相结合,利用神经网络的非线性映射能力,描述材料变形抗力、温度、应力应变与孔洞闭合程度之间的复杂关系,将孔洞锻合过程有限元模拟结果作为神经网络训练样本,建立起温度、应力和变形等多场耦合作用下的孔洞锻合条件模型(VCCM)。针对孔洞形变焊合机理与物理模拟模型一致性问题,以消除大锻件心部残留微孔隙,实现物理闭合状态下的锻合孔洞缺陷的真正冶金结合为目标,基于临界闭合孔洞界面接触力学原型和原子高温扩散理论,以界面接触应变和高温环境作为孔洞焊合的驱动力,利用高温和大变形对闭合孔洞界面扩散焊合的有利影响,提出适用于大锻件在锻造成形阶段的内部孔洞缺陷形变的形变焊合方法,通过物理模拟实     

油管缺陷在线检测仪的研究

文中研究了油管局部缺陷和壁厚减薄缺陷的漏磁场检测原理。研制了一种新型的井口式油管缺陷检测仪,缺陷检测传感器的组合式探头具有自动张合、浮动和随动功能,能够满足油管在线检测对检测传感器的要求。仪器可用在井口对油管进行在线连续定量检测,检测结果不受管表面的油泥或其它非导磁性材料的影响。测试表明,该仪器具有精度和分辨率高,结构简单,安装方便,性能可靠等优点。     

非金属工件电镀表面缺陷现场检测技术的研究

生产过程中对表面缺陷的检测非常需要。介绍了一种为现场设计的非金属电镀工件表面缺陷检测技术 ,系统在计算机信息技术、图像处理技术和自动测控技术相结合的基础上开发而成。该技术设计了具有均匀散射照明光源的 CCD光电检测系统 ,通过图像处理和计算机自动控制 ,由执行机构自动分选出带有表面缺陷的工件。该系统有效的解决了电镀工件表面的强反射光给图像处理和检测带来的困难 ,适于生产现场自动检测应用。     

软件维护外包后的缺陷修正流程改进及分析

通过研究某公司的大型芯片测试软件维护(纠错性维护和完善性维护)跨国外包后的缺陷修正方法,总结得到了缺陷修正的基本控制流程。针对基本控制流程的缺点,从降低维护成本和提高缺陷修正的效率等方面出发,提出了一系列改进方案及新的缺陷修正流程,给出了流程改进后的软件质量保证和项目组的工作效率分析。     

Study on defect classification in multi-layer structures based on Fisher linear discriminate analysis by using pulsed eddy current technique

Pulsed eddy current (PEC) is an emerging non-destructive testing technique with wide application potential. In this study, defect parameter identification in multi-layer structures is examined by using the PEC technique, and a Fisher linear discriminate analysis (FLDA)-based defect classification method is proposed. Defect localization and shape identification are investigated, and defects on the surface and subsurface of the third layer are discriminated. The time domain characterization method is introduced and researched by using the peak time and zero-crossing time of PEC response signals, the principal component analysis algorithm and the FLDA method. The feature extraction results of the three methods are used as the input values of support vector machine for defect classification and feature extraction, and the classification methods are compared. Theoretical analysis and experimental results show that the proposed method can contribute to effective classification for defect parameter identification.     

Defect diagnostics of gas turbine engine using hybrid SVM-ANN with module system in off-design condition

A hybrid method of an artificial neural network (ANN) and a support vector machine (SVM) has been used for a health monitoring algorithm of a gas turbine engine. The method has the advantage of reducing learning data and converging time without any loss of estimation accuracy, because the SVM classifies the defect location and reduces the learning data range. In off-design condition, however, the operation region of the engine becomes wide and the nonlinearity of learning data increases considerably. Therefore, an improved hybrid method with the module system and the advanced SVM has been suggested to solve the problems. The module system divides the whole operating region into reasonably small-sized sections, and the advanced SVM has two steps of the classification. The proposed algorithm has been proven to reliably and effectively diagnose the simultaneous defects of the triple components as well as the defects of the single and dual components of the gas turbine engine in off-design condition. This paper was recommended for publication in revised form by Associate Editor Tong Seop Kim Tae-Seong Roh received his B.S. and M.S. degrees in Aeronautical Engineering from Seoul National University in 1984 and 1986. He then went on to receive his Ph.D. degree from Pennsylvania State University in 1995. Dr. Roh is currently a Professor at the department of Aerospace Engi-neering at Inha University in Incheon, Korea. His research interests are in the area of combustion instabilities, rocket and jet propulsions, interior ballistics, and gas turbine engine defect diagnostics. Dong-Whan Choi received his B.S. degree in Aeronautical Engineering from Seoul National University in 1974. He then went on to receive his M.S. and Ph.D. degrees from University of Washington in 1978 and 1983. Dr. Choi served three years as a President of Korea Aerospace Research Institute from 1999. He is currently a professor at the department of Aerospace Engineering at Inha University in Incheon, Korea. His research interests are in the area of turbulence, jet propulsions, and gas turbine defect diagnostics.