Zone-Guided Defect Inspection for Faster Repair Scope Decisions
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Solution Overview
Problem
Existing inspection methods for components are inefficient, inconsistent, and prone to user error, particularly in the classification and repair of defects, with automated systems facing processor-intensive challenges in detecting small defects.
Innovation Solution
A zone-guided inspection system using a neural network and machine learning algorithms to classify defects and assign zones based on empirical data, including a classification module and zoning module to determine repair strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution imaging is used to detect small defects such as hairline fractures, then measurement precision is improved, but processing time and computational resources increase significantly
Solution Approach 1:
The inspection system divides the component surface into multiple zones based on defect probability and criticality. Different zones are inspected with different levels of resolution and processing intensity, allowing the system to maintain high measurement precision for critical areas while reducing processing time for less critical areas.
Solution Approach 2:
The system applies different inspection qualities to different regions of the component. High-resolution imaging is applied locally to zones with high defect probability or critical locations, while lower-resolution or automated inspection methods are used for less critical zones, optimizing the balance between precision and processing time.
2Reliability
If automated inspection systems are used to improve consistency, then reliability is improved, but device complexity increases
Solution Approach 1:
The automated inspection system is segmented into distinct functional modules: image capture, zone assignment, defect detection, and repair recommendation. Each module performs a specific function and can be independently optimized or replaced, reducing overall system complexity while maintaining reliability.
Solution Approach 2:
The system introduces an intermediary zone-assignment module that bridges the gap between raw image data and defect detection. This intermediary layer organizes the inspection process into manageable zones, simplifying the overall system architecture and making it more reliable through standardized processing steps.
3Device complexity
If manual inspection methods are used, then device complexity is reduced, but productivity and measurement precision deteriorate
Solution Approach 1:
The inspection system performs self-service through automated zone assignment and defect detection algorithms. The system automatically processes images, assigns zones based on predefined criteria, and generates repair recommendations without requiring manual intervention, thereby increasing productivity while keeping the system architecture relatively simple.
4Measurement precision
If comprehensive defect classification is performed, then measurement precision is improved, but processing time increases
Solution Approach 1:
The defect classification process is segmented into multiple stages: initial defect detection, zone assignment, and detailed classification. The system first identifies potential defects and assigns them to zones, then applies detailed classification algorithms only to confirmed defects, reducing overall processing time while maintaining classification accuracy.
Solution Approach 2:
The system performs preliminary zone assignment and defect identification before conducting detailed classification. This preliminary action filters out false positives and focuses computational resources on actual defects, improving measurement precision while reducing the time required for comprehensive analysis.
Data Source
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AI summary
An inspection system (100) is disclosed. The inspection system (100) includes an image capturing system (150) oriented to capture at least one component image(170) of a component (250) from at least one viewing angle, the at least one component image (170) including a set of features (171) selected to enable features of the components (250) and features of defects (274) on surfaces (260) of the component (250) to be identified (312), and a processor (120). The processor (120) is configured to classify a type of defect (274) detected on surfaces (260) of the component (250) in the at least one component image (170) and assign (308) zones (280) to surfaces (260) of the component (250) captured in the at least one component image (170).