X-ray Inspection Device Cross-Section Reconstruction
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Solution Overview
Problem
Current X-ray inspection methods for three-dimensional targets require numerous X-ray images, leading to increased imaging time and exposure doses, making them inefficient for obtaining precise cross-sectional data.
Innovation Solution
An X-ray inspection method and device that uses an X-ray source and detector to obtain two X-ray images from different directions and elevation angles, with a computing device processing these images to derive cross-sectional data by extracting highly reliable regions and synthesizing partial data, thereby reducing the number of images needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple X-ray images are taken from different directions to obtain cross-sectional data, then measurement precision is improved, but imaging time and exposure dose increase
Solution Approach 1:
The patent extracts only the essential information needed for cross-sectional reconstruction by taking X-ray images from just two strategic directions rather than capturing complete 360-degree data. This extraction approach obtains sufficient cross-sectional shape information while minimizing the number of images required, thus reducing imaging time and exposure dose while maintaining measurement precision.
2Measurement precision
If multiple X-ray images are taken from different directions to obtain cross-sectional data, then measurement precision is improved, but X-ray exposure dose increases
Solution Approach 1:
The patent extracts only the essential information needed for cross-sectional reconstruction by taking X-ray images from just two strategic directions rather than capturing complete 360-degree data. This extraction approach obtains sufficient cross-sectional shape information while minimizing the number of images required, thus reducing X-ray exposure dose to the inspection target while maintaining measurement precision.
3Measurement precision
If numerous X-ray images are taken to reconstruct three-dimensional shape, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent extracts only the essential information needed for cross-sectional reconstruction by taking X-ray images from just two strategic directions rather than capturing complete 360-degree data. This extraction approach obtains sufficient cross-sectional shape information while minimizing the number of images required, thus improving inspection speed and productivity while maintaining measurement precision through intelligent data selection and synthesis.
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 cross-sectional data acquisition with fewer X-ray images, reducing imaging time and exposure dose, while ensuring accuracy through the synthesis of highly reliable regions.
Implementation Method 1
transmitting X-rays through the target using an X-ray source for radiating X-rays, an X-ray detector for detecting the X-rays
Data Source
AI summary
A first X-ray image is obtained by imaging a target in a first direction and at a first elevation angle, and a second X-ray image is obtained by imaging the target in a second direction and at a second elevation angle. Based on these two X-ray images, cross-section data of the target is obtained. The first and second X-ray images are converted into first and second thickness data, and first cross-section data based on a first surface side of the target and second cross-section data based on a second surface side of the target are obtained based on the first thickness data. Similar third cross-section data and four cross-section data are obtained based on the second thickness data. The cross-section data of the target is obtained by partially extracting and synthesizing cross-section data of a highly reliable region from these pieces of cross-section data.