Distortion-Free X-Ray Imaging for Curved Surfaces
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Solution Overview
Problem
Existing x-ray backscatter systems struggle to maintain constant orientation and distance when imaging units under test with curved sidewalls or complex curvature skins, leading to image distortion that is carried into three-dimensional models.
Innovation Solution
The use of a machine vision system to determine the orientation of the imaging system, allowing for the processing of scanned images into substantially distortion-free images by mapping detector signals to appropriate coordinates or correcting identified distortion, and integrating these images into a pre-existing design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the x-ray backscatter system is moved closer to or farther from the UUT or the angular orientation is changed to accommodate curved sidewalls, then the system can image complex curvature skins, but image distortion occurs
Solution Approach 1:
The system performs preliminary actions by capturing orientation data from machine vision sensors and collecting position information at the time each image pixel is collected before the actual x-ray imaging occurs. This pre-captured data is then used to guide the image assembly process, ensuring that images are stitched together with correct geometric relationships even when the system moves to accommodate curved surfaces.
Solution Approach 2:
The system uses machine vision sensors to continuously monitor and determine the orientation of the imaging system relative to the UUT. This orientation information provides feedback that is used to adjust the image assembly process, allowing the system to compensate for changes in position and orientation and maintain accurate geometric relationships in the final composite image.
2Manufacturing precision
If the x-ray backscatter system maintains constant orientation and distance, then image distortion is minimized, but the system cannot effectively image units with curved sidewalls or complex curvature skins
Solution Approach 1:
The system transitions from a two-dimensional image grid to a three-dimensional coordinate system by incorporating position and orientation data. Instead of simply mapping detector signals to a flat grid matrix, the system uses spatial coordinates that account for the imaging system's movement in three-dimensional space, allowing accurate representation of curved surfaces without distortion.
Solution Approach 2:
The system introduces machine vision sensors and position tracking as intermediary components between the x-ray imaging system and the final image output. These intermediaries capture orientation and position data that mediate the relationship between the moving imaging system and the UUT, enabling accurate image assembly even when the system moves to accommodate curved surfaces.
3Ease of manufacture
If images are assembled one pixel at a time mapping detector signals to a grid matrix, then the process is straightforward, but distortion is carried forward into three-dimensional models
Solution Approach 1:
The system changes the parameters used for image assembly from simple grid coordinates to spatial coordinates incorporating position and orientation data. By transforming the coordinate system and using parameters that reflect the actual imaging geometry, the system maintains ease of automated processing while preserving geometric accuracy for subsequent three-dimensional modeling.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables the creation of accurate, distortion-free images and models of structures with curved sidewalls and complex curvature skins, facilitating effective reverse engineering and non-destructive evaluation without the need for costly re-design.
Implementation Method 1
Orientation of an imaging system is determined with a machine vision system
Implementation Method 2
Some imaging systems, such as x-ray backscatter systems, can reduce time and labor involved in reverse engineering by enabling imaging of 'hidden' components or systems or structural details
Data Source
AI summary
Exemplary systems and methods are provided for imaging a unit under test. Orientation of an imaging system is determined with a machine vision system, a unit under test is scanned with the imaging system, and the scanned image is processed into a substantially distortion-free image. The scanned image may be processed into a substantially distortion-free image by mapping a scanned image to coordinates determined by the machine vision system. By combining the position and orientation information collected at the time each image pixel is collected, the image can be assembled without distortion by mapping a detector signal to the appropriate image coordinate. Alternately, the scanned image may be processed into a substantially distortion-free image by mapping a scanned image to a predetermined matrix grid of coordinates, identifying distortion in the scanned image, and correcting identified distortion in the scanned image.