AI Vision Inspection Region Targeting to Reduce Duplicate Checks
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Solution Overview
Problem
Existing vision inspection systems often perform duplicate inspections on the same parts across different manufacturing processes, leading to inefficiencies and unnecessary resource utilization.
Innovation Solution
A vision inspection management system utilizing an AI model to identify intensive inspection regions based on process data, enabling optimized vision inspection by intensifying the inspection on specific areas with higher defect probabilities, thereby reducing redundant inspections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vision inspection is performed on all areas of a product across multiple manufacturing processes, then defect detection coverage is improved, but inspection efficiency deteriorates due to duplicate inspections
Solution Approach 1:
The patent segments the product inspection into different regions based on defect probability. The AI model divides the product into high-probability regions (requiring intensive inspection) and low-probability regions (requiring standard inspection), allowing differentiated inspection strategies that eliminate redundant inspections while maintaining comprehensive defect detection coverage
Solution Approach 2:
The patent applies local quality by assigning different inspection intensities to different regions of the product. High-probability regions receive intensive inspection with higher inspection levels, while low-probability regions receive standard inspection, optimizing resource allocation and eliminating duplicate inspections in areas where defects are unlikely
2Measurement precision
If intensive inspection is performed on all products, then defect detection accuracy is improved, but resource utilization deteriorates due to unnecessary inspections
Solution Approach 1:
The patent applies partial action by performing intensive inspection only on high-probability regions where defects are most likely to occur, rather than applying intensive inspection to the entire product. This partial intensive inspection maintains high defect detection accuracy in critical areas while significantly reducing overall resource consumption
Solution Approach 2:
The patent dynamically changes the inspection level parameter based on defect probability. The AI model adjusts the inspection level (e.g., from level 1 to level 3) for different regions based on predicted defect probability, allowing the system to concentrate resources on areas requiring higher accuracy while reducing inspection intensity in low-risk areas
Data Source
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AI summary
A method, performed by a process management apparatus, of managing vision inspection using an artificial intelligence (AI) model and an apparatus therefor are provided. The method includes obtaining first process data related to a first manufacturing process through which a first object passes, identifying a first region on which intensive inspection is to be performed in an entire region of the first object using the AI model and the first process data, controlling a first vision inspector to inspect the identified first region, and determining whether a defect is present in the identified first region.