Golf Ball Concentricity Verification via 3D X-Ray Imaging
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Solution Overview
Problem
Existing methods for verifying the concentricity of multiple-layer golf balls are not sufficiently precise, as they rely on two-dimensional imaging which can lead to inaccuracies due to the complexity of the ball's layered structure.
Innovation Solution
A method and system utilizing X-ray image scanning with multiple X-ray sources and digital detectors to capture X, Z and Y, Z images of the golf ball, combined with edge detection algorithms to calculate the best fit diameters or ellipses of the inner and outer edges, and then determining the concentricity by comparing the center coordinates in three dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If two-dimensional imaging methods are used to verify concentricity, then the device complexity is reduced, but the measurement precision deteriorates due to inaccuracies from the complex layered structure
Solution Approach 1:
The patent transitions from two-dimensional imaging to three-dimensional imaging by introducing multiple X-ray sources positioned at different angles (e.g., 0度和90度). This dimensional change enables accurate measurement of concentricity in the complex layered structure of golf balls, resolving the measurement inaccuracies inherent in 2D methods while maintaining manageable system complexity through structured multi-angle acquisition.
Solution Approach 2:
The imaging process is segmented into multiple discrete angular positions (e.g., 0度, 90度, 180度, 270度), with each position capturing specific dimensional information. This segmentation allows the system to reconstruct complete 3D concentricity data from multiple 2D projections, achieving high measurement precision without requiring a single complex 3D imaging device.
2Measurement precision
If multiple X-ray sources and digital detectors are used to capture X, Z and Y, Z images, then the measurement precision improves through three-dimensional analysis, but the device complexity increases
Solution Approach 1:
The imaging system is designed with multi-functional capabilities where the same X-ray source and detector assembly can acquire images at multiple angular positions and orientations. This universality allows a single integrated system to perform both 2D and 3D imaging tasks, achieving high measurement precision without proportionally increasing device complexity through modular, reusable components.
Solution Approach 2:
The patent introduces image processing algorithms and coordinate transformation methods as intermediaries that bridge the gap between multiple 2D image acquisitions and 3D concentricity analysis. These computational intermediaries process the raw image data from multiple sources, reconstruct three-dimensional information, and calculate concentricity metrics, thereby achieving high measurement precision without requiring physically complex 3D imaging hardware.
3Manufacturing precision
If edge detection algorithms are used to calculate best fit diameters and ellipses, then the manufacturing precision improves, but the loss of information increases due to potential edge detection errors
Solution Approach 1:
The edge detection process incorporates feedback mechanisms where the algorithm iteratively refines edge location by comparing detected edges with expected geometric patterns (circles, ellipses). The best fit calculations use feedback from multiple measurement points around the perimeter, continuously adjusting the fitted parameters to minimize errors. This feedback loop compensates for individual edge detection inaccuracies, achieving high manufacturing precision even when individual edge measurements contain noise or errors.
Solution Approach 2:
The system performs edge detection at multiple angular positions and uses excessive sampling points around the perimeter of each layer. By collecting more measurement data than the minimum required (excessive action), the system can statistically filter out random errors and achieve more accurate best fit calculations. This approach trades increased data processing for improved precision, maintaining high manufacturing measurement accuracy while managing information loss through redundancy.
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
This approach allows for precise determination of concentricity by analyzing the three-dimensional structure of the golf ball, significantly improving the accuracy of concentricity verification compared to traditional two-dimensional methods.
Implementation Method 1
taking at least one X, Z image of the golf ball using a first x-ray source, a first camera and a first image intensifier
Implementation Method 2
The image intensifier converts X-ray photons into highly visible light at sufficient intensity to provide a viewable image
Data Source
AI summary
A method and system for determining concentricity of a multiple layer golf ball are disclosed herein. One or more images of a golf ball are generated using an X-ray source, a camera or a digital detector, and an image intensifier. An edge detection algorithm is preferably utilized. The method also includes calculating Y,Z center coordinates of the a best fit diameter or ellipse of the inner edge layer and outer edge layer of the multiple layer golf ball.