Zone-Guided Component Inspection for Faster Defect Classification
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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 high-resolution imaging being processor-intensive and time-consuming.
Innovation Solution
An inspection system that captures component images from multiple angles, classifies defects using machine learning, and assigns zones based on defect type and location, leveraging a neural network and zoning module to optimize 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 increases and processor intensity increases
Solution Approach 1:
The component surface is divided into multiple zones based on criticality and repair complexity. The inspection system processes different zones with different levels of imaging resolution and processing intensity, rather than applying uniform high-resolution imaging to the entire surface. This segmentation allows hairline fractures in critical zones to be detected with high resolution while reducing overall processing time for less critical areas.
Solution Approach 2:
The system applies different inspection qualities to different locations on the component surface. Critical zones requiring high-resolution imaging for defect detection are identified and processed with higher resource allocation, while non-critical zones use lower-resolution imaging. This local quality approach maintains measurement precision where needed while reducing overall processing time and processor intensity.
2Productivity
If automated inspection systems with machine learning algorithms are used, then productivity is improved, but device complexity increases
Solution Approach 1:
The inspection system segments the component surface into multiple zones with different inspection requirements. This segmentation simplifies the machine learning algorithm by dividing the complex task of defect detection into manageable zone-specific processing steps, reducing overall system complexity while maintaining high productivity through automated processing of multiple zones simultaneously.
Solution Approach 2:
The system performs preliminary zone identification and classification before defect detection. By pre-dividing the component surface into zones based on criticality and repair characteristics, the machine learning algorithm receives simplified input data, reducing computational complexity and enabling faster processing while maintaining high inspection productivity.
3Ease of operation
If operators perform subjective evaluations of defects, then ease of operation is maintained, but measurement precision and consistency deteriorate
Solution Approach 1:
The zone-based inspection system acts as an intermediary between the operator and the defect detection process. Operators simply identify defects and their locations, while the automated system handles the complex classification and measurement precision tasks by referencing pre-defined zone criteria. This intermediary approach maintains ease of operation for users while ensuring consistent, precise defect classification through automated zone-based evaluation.
Data Source
AI summary
An inspection system is disclosed. The inspection system includes an image capturing system oriented to capture at least one component image of a component from at least one viewing angle, the at least one component image including a set of features selected to enable features of the components and features of defects on surfaces of the component to be identified, and a processor. The processor is configured to classify a type of defect detected on surfaces of the component in the at least one component image and assign zones to surfaces of the component captured in the at least one component image.