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Shearography / ESPI技术的激光无损检测系统，用于复合材料与结构的非破坏性强度和缺陷检测。
Created by Elisha Byrne, Last modified by Micah Simonsen on 19 June 2017 11:14 AM
Vic 2D Applications
In order for a setup to be a good 2D application (with just one camera), the specimen must be:
Since 2D is only working with one camera, the software must assume that the specimen in the image moves only in-plane and is completely flat and planar within that image. If the test does not fall within these guidelines, then erroneous strains will be produced. For example, in a tensile test, if the specimen necks and a region moves away from the camera, there will be a compression bias. Any motion away from the sensor will be reported as a compressive strain. Any motion towards the sensor will be reported as a tensile strain.
Tips for 2D applications:
Using a longer focal length will minimize bias due to out-of-plane motion. The false strain produced by out of plane motion is equal to the amount of out-of-plane motion divided by the standoff distances between the lens and the specimen.
2D does not involve a calibration (other than a simple scale calibration), so low distortion lenses are preferred. Since 2D is not calibrated the same way that 3D is, viewing through windows and mediums are more problematic in 2D. Although, there is an inverse mapping method in 2D to remove these distortions. This method involves translation stages, a high quality and flat speckle pattern that is larger than the field of view. Procedurally, it is not as simple as standard 2d applications and sometimes not even logistically possible.
说明文档 SEM Drift Correction
练习文件 Sample Files
Summary and Overview
Here we will discuss subset size, step size and strain filter selection. In short, you want your subset to be larger than your speckle sizes and for most applications your step size to be roughly 1/4 of the subset size. As a rule of thumb, if you use a small step size then you'll want to use a larger strain filter and if you use a large step size then you'll want to use a smaller strain filter.
This article will detail the search/tracking function and why the subsets are speckle size/quality dependent, and will also discuss the subset size effect on holes within a plot, noise and edge data. This article will also discuss the implications of a high subset suggestion, and will then address how the step size and filter size affect strain calculations and virtual strain gauge sizes. Finally, we will discuss how subset size, step size and filter size affect the actual run time of the software.
These subsets allow us to track points on the speckle pattern. Digital image correlation requires that the specimen is properly and densely speckled. This provides us markers/fingerprints to search for and track. We need speckles that are at least 5 pixels in size with at least a 5 pixel spacing in order to resolve the speckles in the images. It's also important that these speckles are consistent in size and spacing. However, we don't track the actual speckles; the way our software works is that we assign a mesh of "subsets" or windows across the image. We need to have a unique speckle pattern within each subset in order to find a unique point to track for each subset. So the subset size is user-defined and depends on the speckle size. For example, if you have small, dense pattern of 5 pixel speckles, you can use a small subset (the smallest our software can track is 9x9 pixels). However, it's hard to get a pattern small enough and dense enough for a subset of 9, so we allow the user to look at the pattern and adjust the subset size accordingly. For current software versions, the default subset size is 29 and default step size is 7 (in previous versions the default subset size ranges from 21-29). So this means that we are tracking a 29x29 pixel area for every 7 pixels. The visual grid that is displayed in Vic-3D is a nice display of what the selected subset size looks like and you can use that visual tool to compare to your speckle sizes. However, that tool can be misleading because the mesh of data is actually much denser than the grid you see. We overlap the subsets and track for every step size. In the default case, we are obtaining data points for every 7 pixels. The overlapping subsets won't be independent of each other, so that's why we don't default to a step size of 1 (which would significantly increase processing time while typically providing little to no gain). To get independent and non-repetitive data, we typically choose a step size about 1/4 of the size of the subset.
Why Does My Contour Plot Contain Holes?
Holes in your data can be attributed to several things. If your reference image has a lot of holes throughout the contour plot, it's likely that you need to have a bigger subset size. Data will be dropped if there is no speckle information within the subset. You need black AND white information within each subset. So if you have a subset size of 29 and if you have some speckles that are larger than 29 pixels, each 29x29 pixel area that is all black will be dropped out of the data and you'll get a hole there in your contour plot. Similarly, if your pattern isn't dense enough then those areas of white (or the areas in between the speckles) that are larger than 29 pixels will be dropped as well. As a side note, this description assumes a black-on-white speckle pattern, but you may have a white-on-black speckle pattern too. Other reasons for holes can be areas of glare/reflection (if there's glare, then each camera will see the light reflected off that point differently and won't be able to make the match or if it does match then you will see a spike in the data), blur, poor contrast, poor speckle pattern (too sparse or inconsistent speckle size), or de-focus. If you see a hole start to occur in the deformed images, then it's likely a crack (either of the pattern or the sample itself), but can also be de-focus (often due to moving out of the depth-of-field), glare, or shrapnel. Adjusting your thresholds the Run menu can help you bring data back in. However, remember that if data is dropped, then it is most likely for a reason. If the data is dropped due to issues such as cracks, glare or shrapnel, it's best to leave that data out of the analysis. It is always better to have an absence of data, than to include erroneous data points that can contribute to artificial displacements/strains.
Subset size and sigma
The larger a subset is, the more information it'll contain. Therefore, the larger the subset, the more unique each subset is. The more unique the subsets are from subset to subset, the better our confidence will be.
How Speckle Pattern Quality Affects Tracking Function
We want each subset throughout the speckle pattern to have nice, unique information within it. For this reason a pattern with uniformly sized subsets, 50% coverage, and high contrast will result in the most traceable features and the lowest noise levels in our data. We want bright whites and dark blacks. Grey areas are hard to track. Areas with big "blobs" and then grey mists of small speckles are particularly hard to track. For more information on speckle pattern quality and how it affects noise, please refer to our Minimizing Bias and Noise Presentation in the Downloads section of our Support site (http://www.correlatedsolutions.com/supportcontent/dic-noise-bias.pdf).
Why Don't I See Edge Data?
We have one data point for every subset. We report the data in the center of the subset. For this reason, the closest we can report data to the edge is one half of the subset size. If you draw the area of interest right up to the edge, then the software is tracking all of that data in the drawn area of interest, but the edge data will be reported in the center of the edge subset. To get the contour plot closer to the edge you can use a smaller subset, which means you'll need a small speckle pattern. With an ideal speckle pattern of 5 pixel speckles that are densely speckled 5 pixels apart, you can likely use a subset size of 9 pixels. This means that in the ideal case, you can get your contour plot within 4-5 pixels of the edge. Also, physically zooming in on the edge will enable you to use a smaller speckle pattern and that 9x9 subset will also be physically smaller, so the center of that subset will be physically closer to the edge. Again, if you zoom in on the sample, you’ll need to adjust your speckle size so that you’ll be able to track a small 9x9 subset. One a side note, since subsets and filters report data in the center of the subset/filter, we need that center point. This is the reason that we must always use odd numbers for subsets sizes and filters. Even numbers do not provide the center point that we need in order to report the values.
Why is My Suggested Subset So High?
In the Vic-3D AOI (Area of interest) tools, you may click the ? for a suggested subset size. If your suggested subset is much larger than what it looks like your pattern should dictate, then that might be an indication that there are some other aspects of the image and/or experimental setup that could likely be improved. The suggested subset function is based off of an estimated sigma (one standard deviation confidence interval) for the subset tracking function. If the subsets are hard to track, this drives up the sigma and thus the suggested subset size. In this case, it's likely an issue of poor contrast, defocus, diffraction limit (aperture is too far closed; hard to avoid in high magnification situations), or poor speckle pattern quality (meaning the speckles are not a consistent size or the speckles are not dense enough).
Step Size and Strain Calculations
When selecting the filter size for strain, keep in mind that this is in terms of data points, which are separated by the step size. So if your filter size is 15, and your step is 5, the total smoothing area is 15*5 = 75 pixels. This is your virtual strain gauge size. If you reduce the step to 1, and use a 15 filter size, you will only be smoothing by 15*1 = 15 pixels, so the strain will be noisier. So as a rule of thumb, if you use a small step size then you'll want to use a larger strain filter and if you use a large step size then you'll want to use a smaller strain filter. Also note that this strain filter is also center weighted, so the edge values will be worth 10% of the center values.
One more thing to consider when selecting your step size is your specimen geometry. For most applications, geometries are flat enough that you do not need to consider this, but for the instances of complicated geometries, we need to consider how many points we sample along a curved surface. For each data point, the strain is calculated using 3 neighboring data points (similar to FEA models). The spacing of the 3 data points are determined by the step size. So the step size must always be tangent along the curved surface in order to ensure that we are calculating the surface strain along the surface and not cutting through the surface, which would produce erroneous strains. For geometries with sharp radii, we’ll need to use a small step size. For more on the strain calculation, please refer to Strain Calculations in Vic-3D in our Downloads section of the Support site (http://www.correlatedsolutions.com/supportcontent/strain.pdf).
How Subset Size, Step Size and Strain Filter Size Affect Run Time?
The subset size determines how large each data point is that you are tracking. A larger subset will take longer to track than a smaller subset. The step size determines how many data points you are tracking. A smaller step size (which means more data points) will take longer to track than a larger step size. It will also take longer to filter over more data points. So a larger strain filter will take longer to process than a smaller strain filter.
Posted by Elisha Byrne on 22 August 2017 05:50 PM
Strain filters in Vic-2D and Vic-3D are user-defined so that users can select how localized or how averaged the strains will be presented in the data. The strain filter helps determine the strain gauge size for all of the individual points on the contour plot. Small strain filters provide better resolution and more localized data. However, large strain filters increase accuracy because they contain more data which will result in less uncertainty. This document provides the stain calculation background, explains the effects of different strain gauge filter sizes, and provides strain filter selection advice for different instances.
Vic-Snap automatically names the images with the correct naming conventions for Vic-3D. So other than remembering not to use and underscore in the filename, there is no actions necessary for naming files correctly when using Vic-Snap.
When using acquisition software other than Vic-Snap, however, the user must follow the naming convention needed for Vic-3D. File names and file paths should not contain any underscores except for the _0 or _1 camera designation (which again, is automatically generated in Vic-3D). The _0 and _1 indicates to Vic-3D which camera the image corresponds to, so an underscore elsewhere in the file name or file path is incompatible with Vic-3D.
The file name for camera 0 and camera 1 must be identical except for the _0 or _1 indicator.
Vic-Snap automatically names both the images with the same user-defined prefix, assigns the proper sequential numbering system, and designates each image with the _0 and _1 suffix. Only the prefix must be entered (so in the example below, only "test-image" was entered as the file name and Vic-Snap assigned the rest of the name).
test-image-000_0.tif is the image for camera 0 and
test-image-000_1.tif is the image for camera 1
The next image pair will be test-image-001_0.tif and test-image-001_1.tif.
It is preferred that the camera indicator (_0 and _1) is at the end of the file name. However, some software packages (Photron's PFV software, for example) don't allow for that. So if the camera designation is the middle of the filename, it should be surrounded by underscores (_1_ and _0). For example:
When files are loaded into Vic-2D and Vic-3D they will be sorted in alphanumerical order. This means that if you have a lot of images, you should make sure that you have enough digits in the file numbering so that the images will be ordered correctly. For example, if there are only three file number digits, then the images will be numbered 000-999 and then jump to 1000 and so on. The problem here is that image 1000 will not be after 999, rather it'll be between 100 and 101. In that case you should check to see that the assigned file number digits parameter is at least 4, so the first image will be 0000. This can be set in File>Advanced Options>System Settings>File Number Digits.
Blank Images: When importing an image into Vic-3D, if the images come in blank or all white, then it's likely a naming issue. Also if you see that images for both camera 0 and camera 1 are listed separately in the image tab, it is also most likely a naming issue. In these cases, check there there are no underscores in both the file name and also even in the file path/folder name except for the _0 and _1 at the end, which must be present.
Missing Images: If it looks like not all of your images were imported into the software, check to see that you had enough digits for all of the images (so if you have over 9999 images, your first image should be 00000 and not 0000). If you have the incorrect number of digits for the number of files that you have then it's likely that all of the images loaded into the software correctly and that they are just hard to find because it would be ordered incorrectly. Also, with a large number of files it is best to load the images by group, rather than selecting all of them. So go to File>Speckle Images By Group and select the speckle image group prefix. It will import all images with that prefix.
Calibrating for a high magnification setup can present a few challenges. This article will discuss techniques for getting the best calibration result at small fields of view.
A high magnification test is one where the lens magnification is roughly in the range of 1-4x; for example, with a standard 2/3" sensor, this would be fields of view between about 8mm and about 30mm.
At small fields of view, the depth of field can become very limited. It may be difficult or impossible to achieve enough depth to allow for good tilting of the calibration grid. This will result in calibrations which have very poorly estimated, unrealistic values for "Center (X)" and "Center (Y)". In extreme cases you may see a "Sync Error" warning caused by these poor estimates.
Calibrating for Reduced Resolution
Posted by Micah Simonsen, Last modified by Micah Simonsen on 28 February 2018 09:41 AM
Many high-speed cameras allow speed increases by reducing (cropping) image resolution. However, calibration can be difficult or impossible at the reduced resolution; in most cases calibrating at the full sensor resolution is easier and will also give a more accurate result.
Problems with calibrating at reduced resolution
A typical high speed camera may have a resolution of 1024 x 1024 and at this resolution a standard 14x10 calibration grid, chosen to fill the field of view, will calibrate well - all coding and target dots should be recognized.
As the resolution decreases much below 1024 x 1024, the smallest dots on the grid - the two coding dots - will no longer be rendered as recognizable ellipses, instead looking more like this (greatly zoomed in):
In this case, you can still manually select the correct grid and proceed to calibrate. However, once the resolution starts to decrease more, towards 512 x 512*, the small circles concentric with the three orientation dots will also become poorly resolved.
In this case, calibration will be impossible. To avoid this, you can calibrate at full resolution, and then perform a simple adjustment to correct for cropping.
Even if calibration is slightly possible at reduced resolution, we can often get a better result with the full field, because we can get better estimates of parameters like distortion by using data from the corners of the sensor. Because of this, it is always recommended to calibrate at full resolution, especially for critical tests.
Note: For cameras with a limited maximum resolution (IR cameras, ultra high speed cameras) we can also use special grids with sparser but larger dots - i.e., an 8 x 6 grid with very large dots. These can be generated with the target generator or you can contact Correlated Solutions to inquire about purchasing a finished grid.
Calibrating at full resolution
For full resolution calibration, set your camera for its max resolution. Speed and exposure time can be set as necessary - these will not change the calibration parameters - but aperture must not be adjusted. Black reference the cameras, if necessary; choose a grid which fills the full field of view of the sensor, and take a good calibration set.
You can then return to the reduced resolution and set up for your test - do not move the cameras or change the aperture, but lighting, FPS, and exposure time adjustment are all allowable.
Software procedure and theory
Two of the parameters we calibrate for are Center (X) and Center (Y). These are the coordinates of the pinhole center of the sensor; they tend to be roughly in the geometric center of the sensor, but never exactly, because of real-world manufacturing variation. This variation does not harm accuracy but must be calibrated for.
Vic-3D represents this as a pixel coordinate referenced to the top left of the sensor.
When we reduce the resolution - for our example, to 512 x 384 - the camera crops the image to the center of the sensor. (512, 512) is no longer the center of this reduced image - we must offset it.
To calculate this offset automatically, add both the calibration images as well as at least one speckle image (at reduced resolution) to the project in Vic-3D. Calibrate as usual, and then click File... Adjust for cropping.
Assuming the image was cropped to the center, the correct values will be filled in. Click Ok and the Center (X) and Center (Y) values will be offset as necessary. You should do this once and only once - if you click through again, the values will be offset again. Check the Calibration tab in your project - the Center (X) and Center (Y) values should be roughly in the center of your reduced resolution image.
If the image was not cropped to the optical center, you must manually enter the necessary offset values.
This correction does not affect accuracy in any way - the digital nature of sensors means that the offset is an exact, knowable integer value.
Note: If you fail to correct for cropping, or the values are incorrect, you will most likely see a very high Projection Error in your analysis. In this case, check through the steps above and try again.
Save the project at this point, and run as usual.
*All numerical values in this application note are examples and will not apply to every cases.