西安交通大学信息机电研究所西安交通大学模具与先进成形技术研究所技术研究特色西安交通大学工业三维摄影测量技术发展路线三维全场变形技术概述面向复杂机械和新型材料运行工况下或现场使用单位简介研究生招生XTDICXTDVCXTRTXTMicroXTDIC 3D控制箱数字散斑全场应变XTDP三维光学测量坐标变换XTDCAL工业近景摄影测量XTSD静态变形XTDA大型飞机风洞大视场高速运动物体动态变形和运动轨迹XTSOXTOMXTOM INSPECTOR三维扫描仪XTFLC板料热成形三维全场应变检测试验机XTSM板料和管材胀形成形试验的三维全场变形检测系统板料成形膜结构双轴双向拉伸试验机双轴四缸电液伺服静态、动态、疲劳试验机双向对称微拉伸试验机(用于光学和电子显微镜)TOMS-汽车模具三维光学快速检测系统专用系统核心技术:复杂工况三维全场动态变形检测技术关键技术产品应用领域系列产品概述三维全尺寸快速检测解决方案:大型复杂工件产品的反求和快速质量检测其他光学体式显微镜测量板料液压胀形试验的三维全场变形检测数据动画演示泡沫铝物体内部变形测量实验板料成形极限FLC快速测定(3D-DIC)飞机风洞模型三维全场应变检测(数字图像相关法)一种基于DIC技术识别焊缝材料参数的新方法高温三维全场应变测量(3000摄氏度以内)高速拉伸变形技术发展路线高速冲击振动模态分析实验---数字散斑应用圆棒试件疲劳实验汽车车桥的静态变形和数字散斑三维全场应变实验木材压缩和弯曲性能试验----全场应变分析型号和配置------XTDIC数字散斑应变测量分析一般测量步骤 XTDIC数字散斑系统计算步骤-----XTDIC数字散斑系统显示和编辑计算结果----XTDIC散斑系统输出功能------XTDIC数字散斑系统大幅面三维全场应变测量视频----XTDIC三维数字散斑动态应变测量分析系统沙土全场变形实验-相似材料钛合金试件压缩变形三维数字散斑试验拉伸试验三维全场应变测量总体功能--XTDIC三维数字散斑动态应变测量分析系统主要功能---XTDIC三维数字散斑动态应变测量分析系统变形分析功能--XTDIC三维数字散斑动态应变测量分析分析曲线功能---XTDIC三维数字散斑动态应变测量报表功能---XTDIC三维数字散斑动态应变测量分析系统截线分析---XTDIC三维数字散斑动态应变测量分析系统等势线分析--XTDIC三维数字散斑动态应变测量分析XTDIC数字散斑系统与电子引申计比对试验XTDIC三维数字散斑动态应变测量分析系统三维全场应变测量分析重型卡车车架和车门全方位静态变形和全场应变检测发动机活塞缸体受力三维静态变形实验相似材料模型变形实验-标志点变形和全场变形两种方法复合材料节点试验---基于XTSD的三维静态变形测量大型结构件大变形三维摄影测量相似材料模型实验-光学三维变形测量变形分析应用大尺寸大变形静态测量某汽车覆盖件冲压全场应变检测步骤和流程汽车覆盖件(长到6米)板料冲压全场应变三维检测板料成形极限FLC试验板料剪切实验装置大型汽车模具制件的实际板料成形三维全场应变检测数字图像相关法(散斑应变)在板料力学性能测试中的应用板料成形网格应变测量实验快速使用说明---XTSM板料成形应变测量分析系统评估模式说明-----XTSM板料成形分析计算模式-XTSM板料成形网格应变分析系统三维点云处理---XTSM板料成形网格应变分析系统网格模式---XTSM板料成形网格应变分析系统XTSM板料成形应变测量分析系统板料成形网格变形分析楼房振动变形实验飞机风洞模型静态变形测量飞机结构件运动特性的动态视觉测量系统动态变形和运动轨迹汽车模具快速质量检测和比对分析路面构造三维扫描及三维坐标获取TOMS汽车模具摄影测量系统实现汽车模具实型数字化检测汽车模具三维光学系统应用于汽车覆盖件回弹的计算三维检测应用比对分析和质量检测焊接过程高温三维全场应变实时检测焊接失稳变形光学非接触三维检测的研究三维全场变形应变系统在焊接学科的研究和应用焊接过程三维全场应变检测实验采用XTSD静态变形系统的焊接过程三维变形检测实验采用XTOM面扫描系统进行焊接变形实验焊接变形试验--光学三维动态变形测量大尺寸无缝焊接管道三维测量和变形分析焊接变形和应变分析船用螺旋桨叶片检测大型飞机三维光学快速测量建模关键技术研究大型水轮机叶片、汽轮机叶片、船舶螺旋桨的快速检测手机零部件三维测量测量实例三维光学测量的应用领域逆向设计应用客车逆向设计快速建模案例轿车、客车、卡车、火车等车辆的组装后产品质量检测大型挖掘机铲斗模型的建模和测量测量实例 测量系统软件界面三维扫描测量实例 逆向和检测汽车模具检测案例 大型泡沫和铸件快速检测其他测量案例行业应用复杂工况三维全场动态变形 检测技术三维全场变形技术概述应变(strain)工业摄影测量光束平差(捆绑调整)自标定方法数字图像相关法(Digital Image Correlatiom,DIC)工业数字近景摄影测量与机器视觉的关系机器视觉(Machine Vision)工业数字近景摄影测量Photogrametry国内外DIC相关研究链接国内外三维检测Strain Measurement by Digital Image Correlation数字散斑全场应变分析工业近景摄影测量静态大尺寸大变形动态变形和运动轨迹三维扫描和建模板料成形网格变形分析焊接变形和应变分析比对分析和质量检测点云处理和三角化相机标定其他综述
数字图像相关法(Digital Image Correlation, DIC)

全场应变测量采用数字图像相关法(Digital Image Correlation, DIC),这是一种光测力学变形测量方法,又称为数字散斑相关法。其基本原理(如图 5所示)是通过图像匹配的方法分析试件表面变形前后的散斑图像,来跟踪试件表面上几何点的运动并得到位移场,在此基础上计算得到应变场(如图 6所示)。在数字图像相关法算法中,图像匹配时常用图像子区的相关性来表征不同图像上两个子区的相似程度,因此该图像子区常称为“相关窗”。


该方法从二十世纪80年代发展起来,国际上2000年之前,主要用于二维全场应变分析,2000年之后逐步发展出三维全场应变分析技术,近5年来快速发展并应用到机械、力学、生物、土木工程等多学科。与其它光测力学变形测量方法(如各种干涉法)相比,数字图像相关法对复杂环境的适应性更好,具有光路简单、实验准备快、数据处理自动化程度高的特点,并可与各种高速高分辨率像机、各种显微镜、X光机等配合使用。

自由容器

被测物体表面散乱图案

相关窗口(15×15像素

利用相关性跟踪变形(各个相关窗口)

计算出的应变网格

全场应变的彩色显示

Digital image correlation

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Digital Image Correlation and Tracking (DIC/DDIT) is an optical method that employs tracking & image registration techniques for accurate 2D and 3D measurements of changes in images. This is often used to measure deformation (engineering), displacement, and strain, but it is widely applied in many areas of science and engineering. One very common application is for measuring the motion of an optical mouse.

Contents· 1 Overview· 2 Differential Digital Image Tracking (DDIT)· 3 Resolution of DIC/DDIT· 4 References· 5 External links· 6 See also

Contents

· 1 Overview

· 2 Differential Digital Image Tracking (DDIT)

· 3 Resolution of DIC/DDIT

· 4 References

· 5 External links

· 6 See also


Overview

Digital image correlation (DIC) techniques have been increasing in popularity, especially in micro- and nano-scale mechanical testing applications due to its relative ease of implementation and use. Advances in computer technology and digital cameras have been the enabling technologies for this method and while white-light optics has been the predominant approach, DIC can be and has been extended to almost any imaging technology.

DIC was first conceived and developed at the University of South Carolina in the early 1980s[1][2][3] and has been optimized and improved in recent years.[4][5][6][7][8][9] [10] DIC is predicated on the maximization of a correlation coefficient that is determined by examining pixel intensity array subsets on two or more corresponding images and extracting the deformation mapping function that relates the images (Figure 1). An iterative approach is used to minimize the 2D correlation coefficient by using nonlinear optimization techniques. The cross correlation coefficient rij is defined as

Here u and v are translations of the center of the sub-image in the X and Y directions, respectively. The distances from the center of the sub-image to the point (x, y) are denoted by Δx and Δy. Thus, the correlation coefficient rij is a function of displacement components (u, v) and displacement gradients

Figure 1: Basic concept of DIC

DIC has proven to be very effective at mapping deformation in macroscopic mechanical testing, where the application of specular markers (e.g. paint, toner powder) or surface finishes from machining and polishing provide the needed contrast to correlate images well. However, these methods for applying surface contrast do not extend to the application of freestanding thin films for several reasons. First, vapor deposition at normal temperatures on semiconductor grade substrates results in mirror-finish quality films with RMS roughnesses that are typically on the order of several nanometers. No subsequent polishing or finishing steps are required, and unless electron imaging techniques are employed that can resolve microstructural features, the films do not possess enough useful surface contrast to adequately correlate images. Typically this challenge can be circumvented by applying paint that results in a random speckle pattern on the surface, although the large and turbulent forces resulting from either spraying or applying paint to the surface of a freestanding thin film are too high and would break the specimens. In addition, the sizes of individual paint particles are on the order of μms, while the film thickness is only several hundred nms, which would be analogous to supporting a large boulder on a thin sheet of paper.

Very recently, advances in pattern application and deposition at reduced length scales have exploited small-scale synthesis methods including nano-scale chemical surface restructuring and photolithography of computer-generated random specular patterns to produce suitable surface contrast for DIC. The application of very fine powder particles that electrostatically adhere to the surface of the specimen and can be digitally tracked is one approach. For Al thin films, fine alumina abrasive polishing powder was initially used since the particle sizes are relatively well controlled, although the adhesion to Al films was not very good and the particles tended to agglomerate excessively. The candidate that worked most effectively was a silica powder designed for a high temperature adhesive compound (Aremco, inc.), which was applied through a plastic syringe. A light blanket of powder would coat the gage section of the tensile sample and the larger particles could be blown away gently. The remaining particles would be those with the best adhesion to the surface, and under low-angle grazing illumination conditions, the specimen gage section would appear as shown in Figure 2. While the surface contrast present is not ideal for DIC, the high intensity ratio between the particles and the background provide a unique opportunity to track the particles between consecutive digital images taken during deformation. This can be achieved quite straightforwardly using digital image processing techniques, although the resolution is always limited to a single pixel. To attain tracking with subpixel resolution, a novel image-based tracking algorithm using MATLAB was developed, dubbed Digital Differential Image Tracking (DDIT), and will be discussed here briefly.


Differential Digital Image Tracking (DDIT)

The DDIT method exploits the shape of these powder particles when digitally imaged in the intensity domain as shown in Figure 2. The resemblance of the particles to mathematical functions that are adept at describing peak shapes with precise center locations and broadening (tails) allow them to be fit to a given function and thus tracked.

Figure 2: Intensity profile of markers for DDIT

It is perhaps coincidental that the symmetric normal (Gauss) distribution functionproficiently fits the intensity profiles of the particles, although many functions would be suitable as well (e.g., Pearson VII, Cauchy). This function can also be described in two dimensions. The quality of the Gaussian fit to a peak profile is shown in Figure 3.

Figure 3: Peak profile of marker with corresponding Gaussian fit

The DDIT script works in the following fashion as schematically shown in Figure 4 (alongside, for comparison, the DIC code, see link, that was also developed). A detailed guide that describes the inner workings of both the DDIT and DIC code can be found below. First, images are captured during the course of a mechanical test. Second, a list of image filenames is generated and the image capture times are extracted from the original images in order to synchronize the DDIT data to that of the data acquisition system. The markers are then automatically detected in the first image (after undergoing automatic background subtraction) by an image processing algorithm that labels connected components in a binary image and subsequently, information regarding the size and shape of these components are extracted (e.g. area, bounding box, centroid, major axis length, minor axis length, etc.). Particles with properties that do not conform to specifications for “ideal” shapes are thrown out, and the remaining markers in the first image are fit to a Gaussian function (in this thesis work) using a nonlinear least-squares algorithm in both the longitudinal and transverse directions. The normalized residuals of the fit of the peak to the function are calculated for every peak (typically several hundred in an image such as Figure 5) and again, fits deemed “poor” as given by the value of the residual are removed from the analysis. This process now continues for every image in the sequence, and the result includes the position of the peak center, which is then post-processed using a visualization and data analysis script that allows visualization and output of the quantities of interest. Incidentally, the DDIT technique has also been successfully applied to the testing of brittle SiO2 and ductile Au thin films.

Resolution of DIC/DDIT

The resolution that one can achieve in practice using these image-based techniques depends on a number of factors, including but not limited to camera resolution, lens optical quality, and marker size and quality. To demonstrate the achievable resolution that one can achieve using the setup and techniques described herein using both DIC and DDIT, a tensile test performed on a brittle linear elastic submicrometre (t ~ 250 nm) freestanding Al-32 at%Mo specimen was analyzed for strain using both methods. This specimen was chosen because it exhibits small strains that are difficult to resolve and also because it is amorphous and thus minimizes any microstructural inhomogeneities. The initial raster grid that was applied for the DIC method and the automatically labeled markers (“good” markers are shown as circles) are shown in Figures 6 and 7, respectively. The representative stress strain response from this film is shown in Figure 8, where both longitudinal (shown in blue) and transverse (shown in green) strains were calculated using DIC with two subset image sizes (15 and 25 pixels) and DDIT. It is apparent from these results that the peak tracking algorithm works quite effectively at resolving the response of this film, even when tracking about 50 times fewer points. The insets of Figure 8 show the typical strain variation that was achieved, where as low as 40 με was observed in the longitudinal direction using DDIT. It was concluded that either of these techniques were effective at measuring the Young’s moduli of these specimens, although the transverse strain resolution obtained using DIC is at the upper limit of what one would desire to measure Poisson’s ratio.

References

1. ^ T.C. Chu, W.F. Ranson, M.A. Sutton, W.H. Peters, Exp Mech 25 (1985) 232.

2. ^ H.A. Bruck, S.R. McNeill, M.A. Sutton, W.H. Peters III, Exp Mech 29 (1989) 261.

3. ^ W.H. Peters, W.F. Ranson, Opt Eng 21 (1982) 427.

4. ^ M.A. Sutton, J Orteu, H. Schreier, Book - Image Correlation for Shape, Motion and Deformation Measurements, Hardcover ISBN 978-0-387-78746-6.

5. ^ M.A. Sutton, S.R. McNeill, J.D. Helm, & Y.J. Chao, in: PK Rastogi (Ed.), Photomechanics, Springer-Verlag, Berlin Heidelberg, 2000, pp. 323-372.

6. ^ M.R. James, W.L. Morris, B.N. Cox, Exp Mech 30 (1990) 60.

7. ^ B.W. Smith, X. Li, W. Tong, Exp Tech 22 (1998) 19.

8. ^ Vikrant Tiwari, M.A. Sutton and S.R. McNeill, Exp Mech, (2007) 47: pp. 561–579.

9. ^ Vikrant Tiwari, M.A. Sutton, S.R. McNeill, S. Xu, X. Deng, W. L. Fourney and D. Bretall, Int. J. of Impact Engineering, Volume 36, Issue 6 June 2009, Pages 862-874.

10. ^ A.S. Kobayashi, Handbook on Experimental Mechanics, Prentice Hall / Society for Experimental Mechanics, Inc., Lebanon, Indiana, 1993.

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