Inspection Image Defect Screening With Crack Growth Prediction
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Solution Overview
Problem
Current nondestructive inspection methods for industrial facilities, such as pipelines, are inefficient in defect detection due to the time-consuming interpretation of images obtained through light or radiation irradiation, lacking techniques to enhance precision and efficiency in identifying defects like stains, cracks, and chipping.
Innovation Solution
A defect inspection apparatus and method that includes image obtaining, processing, and simulation means to analyze defect occurrence and growth, displaying calculated results alongside historical defect information, allowing for precise and efficient defect detection by predicting defect growth and displaying relevant data on the inspection images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual interpretation of inspection images is used, then flexibility in defect analysis is maintained, but inspection time and labor effort increase significantly
Solution Approach 1:
The patent replaces manual mechanical interpretation with automated image processing systems that calculate defect positions and features, and perform simulations to predict defect growth. This substitution dramatically reduces inspection time while maintaining comprehensive defect analysis capabilities.
Solution Approach 2:
The system creates simulated copies of defect growth patterns based on calculated defect features. By generating simulation results that predict future defect states, the system provides additional analytical value without requiring additional manual interpretation time, effectively multiplying the utility of each inspection.
2Measurement precision
If comprehensive defect analysis is performed on all detected defects, then detection precision is improved, but the time and complexity of inspection increase
Solution Approach 1:
The patent applies different levels of analysis to different defects based on their characteristics. The simulation means predicts defect growth for specific defects with higher precision requirements, while other defects receive standard analysis. This localized approach to analysis depth maintains overall precision while reducing unnecessary complexity.
Solution Approach 2:
The image processing means performs preliminary calculations of defect positions and features before full simulation analysis. By pre-processing and categorizing defects based on initial calculations, the system prepares data structures that enable efficient subsequent simulation, reducing the complexity of comprehensive analysis.
3Measurement precision
If simulation analysis is performed for all possible defects, then prediction accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The simulation means applies detailed growth prediction analysis selectively to defects that require it based on their calculated features and risk characteristics. Not all defects undergo full simulation analysis - only those where prediction accuracy is critical. This selective approach maintains necessary precision while avoiding unnecessary processing time for low-risk defects.
Solution Approach 2:
The system performs simulation analysis at different levels of detail. For high-priority defects, comprehensive simulation provides excessive (more than sufficient) accuracy. For lower-priority defects, partial simulation provides adequate prediction. This graduated approach optimizes the balance between prediction accuracy and processing time across the entire defect population.
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
The apparatus significantly increases precision and efficiency in defect detection by enabling image interpreters to visualize defect occurrence, growth, and historical data, reducing interpretation time and effort, while allowing for continuous adjustment of feature ranges for more accurate defect identification.
Implementation Method 1
a received-light image created on the basis of reflected light or transmitted light from a test object, the reflected light or the transmitted light being obtained as a result of irradiation of the test object with light rays or radiation rays
Implementation Method 2
a received-light image created on the basis of reflected light or transmitted light from a test object, the reflected light or the transmitted light being obtained as a result of irradiation of the test object with light rays or radiation rays
Data Source
AI summary
There are provided a defect inspection apparatus, method, and program for, in a case of using an image of an inspection-target industrial product (test object) to conduct an inspection to check whether defects are present, allowing an image interpreter to precisely and efficiently detect defects. A possible-defect image D3 indicating a crack-like defect and a simulation result image P3 indicating the predicted growth of the crack-like defect are displayed. Sliders L1 and L2 and checkboxes CB1 are used to enable selection of possible defects and simulation results to be displayed. An image interpreter can use the checkboxes CB1 to select a type of possible defect to be displayed, and can use the sliders L1 and L2 to select possible defects to be displayed on the basis of the wall thickness of portions in which possible defects are detected and the size of possible defects.