X-ray CT Volume Raw Image Flaw Detection via Dark Region Subtraction
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Solution Overview
Problem
Current non-destructive testing methods require CAD models and are not economically viable for testing unique objects, as they demand significant computing power and are limited to series production, making real-time testing of individual parts challenging.
Innovation Solution
The method employs X-ray computer tomography to form three-dimensional images of test objects, identifies non-material regions, generates flaw images through filtering and subtraction, and classifies flaws using automated processes, enabling real-time inline inspection of series-produced parts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CAD model-based testing methods are used, then measurement precision is improved, but device complexity and computing power requirements increase
Solution Approach 1:
The testing method is segmented into distinct processing stages: volume raw image acquisition, dark region identification, embedding verification, and difference image generation. Each stage processes only necessary data portions, reducing overall computational complexity while maintaining measurement precision through systematic breakdown of the testing workflow.
Solution Approach 2:
The method performs preliminary identification of dark regions and verification of their embedding status before generating the final difference image. This preliminary processing filters out non-flaw regions early in the workflow, reducing the computational burden on subsequent analysis stages while preserving accurate flaw detection capability.
2Measurement precision
If CAD model alignment is performed, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The method extracts and processes only the essential features needed for flaw detection - specifically identifying dark regions and their embedding relationships - rather than performing complete CAD model alignment. This extraction of critical information maintains measurement precision for flaw detection while eliminating time-consuming registration procedures.
3Productivity
If automated flaw classification is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The system performs automated flaw classification by having the processing unit automatically generate difference images and identify flaw regions without requiring external intervention or complex classification algorithms. The method serves itself by using the inherent image processing capabilities to directly produce actionable results, improving productivity while keeping the automation system relatively simple.
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 reliable, automated detection and classification of flaws in real-time, improving interpretability and reducing artifacts, making it suitable for inline inspection of series-produced components.
Implementation Method 1
recording a volume raw image of the test object by means of a suitable non-destructive imaging testing method, such as X-ray computer tomography
Data Source
AI summary
A method for the non-destructive testing of the volume of a test object, during the course of which a volume raw image of the test object is recorded by a suitable non-destructive imaging testing method. Then, those regions of the volume raw image are identified that are not to be attributed to the test object material. It is checked whether an identified region is completely embedded in regions that are to be associated with the test object material. If necessary, such a region is assimilated to those regions that are to be associated with the test object material, forming a filled volume raw image. Finally, a difference is generated between the volume raw image and the filled volume raw image, forming a first flaw image.