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Application of 450 kV Computed Tomography to Engine Blocks with Steel Liners Charles R. Smith, Kevin Holt BIR, Inc. Uwe Bischoff, Bernd Georgi, Ferdinand Hansen, Frank Jeltsch Volkswagen Commercial Vehicles
ABSTRACT The most common type of Computed Tomography (CT) system deployed in the automotive industry for inspection of aluminum castings today uses a 450 kV x-ray source. These systems are quite suitable for inspection of cylinder heads, transmission cases and other medium size parts with little or no embedded steel or other dense metals. As a general rule, parts with maximum path lengths of 264 mm in aluminum or 82 mm in steel can be successfully inspected at 450 kV. Achieving excellent image quality on larger parts with longer path lengths such as engine blocks with steel liners normally requires the use of a linear accelerator producing x-rays at 2 MV or higher. Such installations are not common at industrial sites due to high cost, large size, and operational safety considerations. Recent developments in mathematical processing algorithms for correction of CT data have facilitated the imaging of larger aluminum parts with long path lengths and embedded heavy metals. Prior to these new corrections, the results from 450 kV systems were unsatisfactory due to distortion, noise, and artifacts in the images. We present “before” and “after” images showing the improvements gained from these additional processing steps.
technology to inspect parts, improve designs, monitor processes, measure dimensions, and improve quality. CT inspection has been most commonly applied to light metal castings such as aluminum cylinder heads and blocks. The most promising usage of CT systems so far is the capability to simultaneously perform dimensional checking and flaw detection in one inspection. The complex internal geometries, critical dimensional tolerances, and thin wall structures of today’s high performance engines are forcing casting suppliers to be more competitive while keeping costs in check. If raw castings with bad dimensions or critical internal defects slip through the initial inspection processes, they may fail other tests downstream after machining, labor, handling, and other similar operations have added value. The potential for CT to reduce the amount of defects, scrap, and rework is a potential source of significant cost savings. Critical defects missed by conventional inspection processes can lead to warranty claims and costs at a later date. These costs can also be reduced by CT while improving reliability. A typical automotive CT system specifically designed for in-line inspection of castings is shown in Figure 1.
Three specific correction methods are presented: global noise filtering, adaptive noise filtering, and ring artifact suppression applied to raw data. The physical basis of each correction is discussed along with the mathematical theory. A new version of software incorporating these additional correction steps has been issued to existing 450 kV systems, enabling them to expand their scope of applications beyond the original specifications and design goals.
INTRODUCTION There has been a steady growth of automotive manufacturers using CT technology from the mid 1980’s in Japan, Europe in the mid 1990’s, Korea in the past few years, and now in China. These manufacturers use the
Figure 1. A 450kV In-Line CT system for automotive casting inspection is shown..
The most prevalent type of CT system in the automotive industry uses an x-ray system operating at a maximum energy of 450 kV. The upper limit of 450 kV is a historical one imposed by the interconnection system between the high voltage power supplies and the x-ray tube. We can predict the maximum practical path lengths in aluminum or steel for CT inspection with a 450 kV x-ray tube using the equation for the basic law of radiation attenuation:
I = I 0 e − μx
(Eq. 1)
where Io is the x-ray flux entering the part, I is the x-ray flux exiting the part, μ is the attenuation coefficient of the material (in units of cm-1), and x is the path length through the part in cm. However, Eq. 1 as given is only accurate for a single x-ray photon energy, whereas practical x-ray sources produce a broad spectrum of energies up to the maximum accelerating potential of 450 kV. A reasonable approximation that works well in practice is to model the source as mono-energetic at half the maximum energy. The attenuation coefficients of aluminum and steel are therefore found for 225 kV using “Mucal on the Web,”1 a shareware program at http://www.csrri.iit.edu/mucal.html. From Mucal, the attenuation coefficients for aluminum and iron are 0.323 cm-1 and 1.033 cm-1 respectively at 225 kV. Based on practical experience with real parts and actual CT systems, good quality images are possible when the attenuation in the maximum path length is less than 5,000. Solving Eq. 1 then for the path lengths of Al and Fe that give attenuations of 5,000 (I0/I), we find the maximum practical path lengths of Al and Fe are 264 mm and 82 mm. Longer path lengths typically cause significant artifacts due to beam hardening and photon starvation. This is especially a problem for engine blocks with steel liners where path lengths in steel are often 125 mm or longer. Beam hardening is preferential absorption of the low energy portion of the x-ray beam in the first few cm while traversing a long path. The emergent beam from a long path thus has an average energy significantly higher than the assumed average of 225 kV. This higher energy beam is more easily able to penetrate the remainder of the path and effectively makes the entire long path appear less dense than normal since practical CT reconstruction algorithms use the same mono-energetic assumption as Eq 1. The result is that beam hardening creates density type shading errors in the CT images, which adversely affect measurement accuracy but still allow for flaw detection interpretation.
Photon starvation is a more severe problem leading to streaks that render images unusable. Photon starvation occurs when the signal arriving at the detector is below the electronic noise level of the data acquisition system so the resulting output goes to zero or nearly so. Zero values occurring in very low level data appear as noise spikes. The “filtered back-projection” method of image reconstruction used in commercial CT systems convolves the raw data with a filter kernel that effectively causes a certain degree of edge enhancement. The photon starvation noise spikes in the low level portion of the raw data are amplified by the convolution step of reconstruction resulting in significant streak artifacts in the finished image. This is illustrated in Figure 2.
Figure 2. Streak artifacts due to photon starvation on long steel paths are shown. An in-line four cylinder engine block with steel liners was imaged at 445 kV. Raising the energy level of the x-ray source to avoid photon starvation is one solution to the streak artifact problem. At higher energies, the attenuation coefficients are reduced so penetration is improved. Since high energy sources produce much higher flux rates than x-ray tubes, the low energy portion of the spectrum can be filtered out to reduce the effects of beam hardening. A 6 MV accelerator can penetrate path lengths of 850 mm and 275 mm in aluminum and steel. An example of a V6 engine block CT image taken at 6 MV using a linear accelerator is shown in Figure 3.
Figure 3. A 6 MV CT image of a V6 engine block with steel liners is shown. Artifacts such as photon starvation streaks and beam hardening shading are minimized. Accelerator CT systems are a good solution in terms of improved image quality. Their use in the automotive industry has been limited by significantly higher system and shielding costs.
CT DATA COLLECTION CT systems create cross section images by collecting radiographic images at multiple positions around the part. Single row line array detector based systems used for casting inspection normally collect data at regular angular increments while the part is rotated through 360°. CT system data is displayed in a coordinate system of turntable angular increment on the Y axis and detector channel number on the X axis. This data presentation is known as a sinogram since features away from the center of the turntable describe sinusoidal functions. An example sinogram from the image of Figure 2 is shown in Figure 4. The streak artifacts in the 450 kV image of Figure 2 are caused by low level signals through the steel liners and long aluminum paths seen in the sinogram as black areas.
ARTIFACT REDUCTION BY DATA CORRECTIONS Software correction of CT data prior to image formation improves image quality by minimizing problems due to the physical measurement system. Three separate methods designed to improve signal to noise ratio without reducing spatial resolution are presented. The goal is extension of existing CT systems to a wider variety of applications including engine blocks.
Figure 4. The “sinogram” data from a CT scan is shown. The Y axis is turntable angle and the X axis is detector channel number. Off-center features produce sinusoidal functions in the data.
Sinogram Data Median Filter CT systems operate in environments with high electrical noise. High power pulse-width modulated servo drives are often sources of RFI noise. The 450 kV x-ray systems also are capable of generating large transient electrical noise spikes. Despite judicious shielding and good grounding practice being used on sensitive low level signals in the detector array, noise spikes are sometimes seen in sinograms that cause streaks in images. We take advantage of the knowledge that the response of the detector system is limited by its modulation transfer function.2. Knowing the output of a given detector channel cannot change by more than a certain amount from one reading to the next, we can infer that outliers and “spikes” in the data are caused by noise injected into the system. These noise spikes are eliminated by median or rank order filtering the sinogram. The weighted median filter3 (WMF) used here is a 3x3 mask operator applied to the sinogram. The nine data points covered by the operator are rank ordered from lowest to highest value and the central data point is given greater weight by having multiple entries in the ordered list. The original center data point of the sinogram is replaced by the middle data point in the rank ordered list. The WMF operator removes single occurrences of noise spikes while preserving edge data. The CT image signal to noise ratio is improved with no loss of resolution.
Sinogram Data Adaptive Noise Filter When photon starvation occurs due to long path lengths or dense material, the detector signal will drop to zero output for more than one data point in a row. In this case, median filtering is not effective and severe streaking occurs in the images as seen in Figure 2. An additional method that is successful at eliminating photon starvation streaks has been developed for medical CT and has now been applied to the industrial case. In this case, different size Gaussian shaped filter kernels are convolved with the sinogram data based on signal level. Convolution of a filter kernel K with an image I is mathematically expressed as m
n
O (i, j ) = ∑∑ I (i + k − 1, j + l − 1) K ( k , l )
Eq 2
k =1 l =1
where the output pixel is O(i,j) and the filter kernel size is m x n. Gaussian shaped kernels are chosen for their noise smoothing characteristics. Based on a heuristic algorithm, sinogram data is segmented into several bands according to signal level. The band of data at the lowest signal level is filtered with the largest size kernel and thus experiences the greatest degree of smoothing. At higher signal levels, the kernel size is progressively reduced with an attendant reduction of smoothing. The objective of the method is to selectively filter only those areas of the data causing objectionable streaks due to photon starvation while avoiding global smoothing of the data with its corresponding loss of spatial resolution.
Ring Artifact Removal in Sinogram Data One of the characteristic problems of all CT systems using data collection based on relative rotational motion between the object and the source-detector assembly is circular ring artifacts in the images. Circles in the image are caused by vertical lines or line segments in the sinogram data due to many factors such as incorrect calibration,5 detector housing temperature drift, non-linear behavior of detector scintillator material, channel to channel variation of detector array output, mechanical misalignment between the x-ray source and detector, etc. System hardware is normally designed to mitigate ring artifact problems, but is not always capable of perfect performance, especially in the case of industrial applications where a wide variety of materials, energy ranges, and part sizes are encountered on any given system. Therefore, software corrections for ring artifacts have been developed.
For portions of the sinogram containing air (i.e. the white areas in Figure 4), standard gain calibration usually does a good job of preventing major rings when performed properly. However, many of the more unmanageable causes of rings manifest as partial vertical lines concentrated inside the shaded areas of the sinogram – the areas which contain the information important to making the CT reconstruction. The challenge posed to correction algorithms is to identify and remove these partial lines from the sinogram without removing part of the object itself. The correction used here works by first smoothing the sinogram data to combat noise. Then, for each datum in the sinogram, a fit is performed on the data in a small surrounding neighborhood to determine the expected value of that datum. The difference between the measured value and expected value is calculated, and the result is a ring-candidate sinogram. The ring-candidate sinogram contains many vertical lines – in general it will contain most if not all of the actual rings, as well as a large number of vertical lines corresponding to objectfeatures. A heuristic algorithm then evaluates these vertical lines based on the strength, length, and connectedness of each line, and separates the lines into ring-components and object-features. The ringcomponents are then assembled into a “rings-only” sinogram, which is subtracted from the original sinogram to provide the corrected data.
RESULTS An in-line four cylinder engine block having steel liners was scanned at the following parameters • • • • •
X-ray Technique: 445 kV, 2 ma., small focus; Slice Thickness: 1 mm at the part center; Number of Views: 800; Time per Slice: 26 seconds; Image Matrix & Diameter: 10242, 400 mm.
Scans made with these parameters have streak and ring artifacts that mask real defects (see Figure 2). These problems are caused by the steel path lengths exceeding the penetration capability the 450 kV source as determined by Equation 1. When the sinogram based corrections of Median Filter, Adaptive Noise Filter, and Ring Removal are applied to the data, the image is improved to the level seen in Figure 5. While image quality is not perfect, streak and ring removal reveals flaw conditions. Porosity is seen in the center left area of the casting at nine o’clock in Figure 5. This region is expanded in Figure 6.
ACKNOWLEDGMENTS We wish to thank Dr. Bernd Georgi and Dr. Ferdinand Hansen of Volkswagen Commercial Vehicles, Hanover for supplying the engine block shown in Figures 2, 3, 5, and 6.
REFERENCES 1.
2.
3. Figure 5. The engine block section of Figure 2 is shown after the sinogram data corrections of Median Filter, Adaptive Noise Filter, and Ring Removal. Artifacts are reduced so flaw detection is now possible.
Figure 6. The left center portion of Figure 5 is expanded to show porosity in detail. The largest flaw measures 1.1 mm horizontally by 3.8 mm vertically.
CONCLUSION Software based corrections of CT data have been designed to improve image quality for extreme application with long path lengths and dense materials that are normally quite difficult to image unless thick slices, long integration times, and the large focal spot are used. The utility of CT as an inspection technique has been improved by issuing a new revision of software to existing systems at customer sites.
4. 5.
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