Automated Display Inspection Using AI Image Learning
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Solution Overview
Problem
The manufacturing of display apparatuses is hindered by the time-consuming process of human inspection and the need for various environmental tests, leading to inefficiencies and increased defect rates.
Innovation Solution
An apparatus and method that utilize a shuttle portion with a lift pin, a capturing portion for image capture, a generator equipped with a learning model to create images of good and defective products, and a determining portion to assess images against comparative still images, thereby reducing defect detection time and improving manufacturing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human inspection by naked eye or captured images is used, then defect detection is performed, but manufacturing time increases and productivity decreases
Solution Approach 1:
The patent replaces manual human inspection with an automated machine vision system that captures images and uses AI algorithms to detect defects. This substitution eliminates the time-consuming nature of human inspection while maintaining or improving detection accuracy, directly resolving the contradiction between reliability and productivity.
Solution Approach 2:
The system creates digital copies (images) of the substrate and uses these copies for inspection instead of direct human observation. Multiple images are captured and processed to generate composite images that highlight defects, enabling rapid automated inspection without requiring human time investment.
2Reliability
If various environmental tests are performed after restarting the manufacturing apparatus, then product quality is ensured, but manufacturing efficiency deteriorates
Solution Approach 1:
The inspection system performs defect detection during the manufacturing process itself, before the manufacturing apparatus needs to be restarted or stopped for environmental tests. By identifying and flagging defective substrates in advance through automated imaging, the system prevents the need for time-consuming post-manufacturing quality verification, thus reducing manufacturing downtime while ensuring product quality.
3Measurement precision
If multiple images are captured and processed through learning models, then inspection accuracy improves, but processing time increases
Solution Approach 1:
The system captures multiple images at specific periodic intervals or at key manufacturing stages, then processes them through learning models to generate composite images. This periodic capture approach ensures comprehensive defect detection while managing processing time by only analyzing images at critical moments rather than continuously, balancing accuracy with efficiency.
Data Source
AI summary
An apparatus for manufacturing a display apparatus and a method of manufacturing a display apparatus, configured to automatically perform inspection according to a defect of a process product during a manufacturing process, and precisely determine whether a process product may be defective by learning the process product in which a defect does not occur.