Image analysis – Applications – Manufacturing or product inspection
Reexamination Certificate
2002-04-15
2004-06-15
Ahmed, Samir (Department: 2623)
Image analysis
Applications
Manufacturing or product inspection
C250S332000, C374S010000
Reexamination Certificate
active
06751342
ABSTRACT:
TECHNICAL FIELD
The present invention generally relates to thermal imaging and more particularly relates to non-destructive detection of defects in a sample using thermographic image data.
BACKGROUND OF THE INVENTION
Active thermography is used to nondestructively evaluate samples for sub-surface defects. It is effective for uncovering internal bond discontinuities, delaminations, voids, inclusions, and other structural defects that are not detectable by visual inspection of the sample. Generally, active thermography involves heating or cooling the sample to create a difference between the sample temperature and the ambient temperature and then observing the infrared thermal signature that emanates from the sample as its temperature returns to ambient temperature. An infrared camera is used because it is capable of detecting any anomalies in the cooling behavior, which would be caused by sub-surface defects blocking the diffusion of heat from the sample surface to the sample's interior. More particularly, these defects cause the surface immediately above the defect to cool at a different rate that the surrounding defect-free areas.
As the sample cools, the infrared camera monitors and records an image time sequence indicating the surface temperature, thereby creating a record of the changes in the surface temperature over time. It is the current practice to use a human operator to view the record of these changes and to look for “hot spots” in the image record. In many instances, this analysis is purely visual (i.e. the human inspector views a display of the image output on a monitor and identifies regions that appear “hot” compared to surrounding areas. More sophisticated methods attempt to use numerical processing of the data by generating contrast curves relative to a reference specimen of known quality and composition (a so-called “gold standard”). This reference specimen, which is known to be defect free, is typically placed in the field of view of the imaging camera. In other instances, the “gold standard” is not a reference specimen at all, but rather it is an image that has been derived from a physical model. However, in general, the time history of the cooling of the sample is not viewed as a whole (i.e. a contiguous sequence), but rather as a collection of individual frames acquired from the infrared camera. These methods work adequately for large, or near surface, defects. However, as manufacturing processes and safety standards requirements place higher demands regarding smaller/more subtle defect detection, these traditional methods become less effective because the small signal levels associated with subtle defects are lost in the noise, drift, and instability that is inherent to infrared cameras. Also, visual defect identification methods tend to be subjective, and they do not readily and easily lend themselves to the automatic defect detection process. Further, it is not possible to measure the depth of the defects simply by viewing the infrared images.
There have been attempts to determine the depth of a defect via processing and analysis of the data from the infrared camera and also to automate the defect detection process. In some cases, the data from the infrared camera is transferred to a computer for processing and analysis to detect variations in the cooling behavior or to perform mathematical operations on the data to determine the depth of the sub-surface defect or other defect properties. These types of calculations, however, often require expensive low noise, high-speed digital infrared cameras. Further, the cumbersome nature of having a computer attached to the camera for conducting calculations makes the combination impractical for applications outside of a laboratory, such as field inspections.
Also, infrared data sequences of thermal decay typically used in non-destructive testing tend to be difficult to manipulate mathematically due to their low signal-to-noise ratios and large dynamic range and also require a great deal of computer processing power, memory and storage space.
One attempt at automating the defect detection process involves analyzing the contrast between each pixel in the image and a reference to generate a curve representing the amount of contrast between each pixel and the reference. The reference can be established any number of ways including using a reference pixel (from the sample image), a pixel group (from the sample image). If a pixel, or a pixel group is used, a reference point or reference area of the sample must be defined. The reference can be a defect-free area of the sample, or the mean of the entire field of view of the camera (when viewing the sample). The temperature-time history of this reference pixel or pixel group is subtracted from the time history of each pixel in the image to generate a contrast vs. time plot. Any significant temperature difference between any given pixel and the reference indicates the presence of a defect which will exhibit itself as a peak in the contrast vs. time plot. The contrast vs. time plot can be measured with respect to the time at which the peak occurs, the time at which a maximum ascending slope occurs, and/or a moment of the curve for each pixel. Other options, such as generating and displaying the contrast vs. time plot with a reference plot and checking the point at which the two plots separate, have also been applied.
Such contrast-based methods tend to have significant shortcomings, however. In addition to the data storage, memory and processing problems noted above due to the large size of the infrared image data files, contrast-based methods require the identification of a defect-free region on the sample as a reference point. This requirement is often not realistic for some samples if, for example, the size of the defect is larger than the infrared camera's field of view. In such a case, there is no defect-free area available that can act as a reference for a given region. Further, if the entire sample exhibits a defect (e.g., a large delamination running underneath the entire surface of the sample), there is no contrast between any region of the sample because the whole sample is equally or nearly equally defective.
Contrast-based methods that rely on the mean of the entire field of view as a reference have also been used, but this method assumes that the defect area in the field is small enough so that it will not appreciably influence the mean. If a defect (or group of defects) occupies a large portion of the field of view, the contrast method is ineffective because a significant portion of the mean value result is composed of data derived from defective sample points which acts to reduce any appreciable difference between the defect area and the mean when the contrast value is calculated.
Regardless of the specific reference value used in detecting defects, the results obtained using contrast-based methods depend strongly on the choice of reference region on the sample. More particularly, the results obtained in contrast-based methods can be altered by simply changing the location of the reference region.
Further, in evaluating the results from both the contrast-based methods and the data obtained directly from the infrared camera, identifying the time at which a maximum peak slope occurs (indicating the presence of a defect) is often difficult because the signals are often inherently noisy, thus the contrast based method must be capable of discriminating between pixels associated with defects and pixels associated with noise. Although the peak slope (of the temperature vs. time relationship) is a useful indicator of defect depth, the peak slope inherently must occur earlier than the peak contrast and may be obscured by the heating event, or by lingering heat from the equipment after flash heating the sample. The peak slope may also be obscured if the instantaneous temperature of the sample exceeds the camera's peak temperature detection capabilities, causing an initial, highly nonlinear response from the camera due to camera saturation.
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Ahmed Samir
Rader & Fishman & Grauer, PLLC
Thermal Wave Imaging, Inc.
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