Bridge Failure Detection Using 2D and Height Data
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Solution Overview
Problem
Conventional 2D inspection methods are inadequate for accurately detecting bridge connecting failures between terminals on a board, especially when the board is distorted, leading to unreliable detection due to unstable component positioning.
Innovation Solution
A method that combines 2D image analysis with height-based information to enhance detection accuracy, using multiple light sources at different angles to acquire 2D and 3D data, and extracting bridge regions from both to determine the occurrence of bridge connecting failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a conventional 2D inspection method is used, then the inspection process is simple, but the detection accuracy of bridge connecting failure is insufficient
Solution Approach 1:
The patent transitions from 2D image inspection to 3D inspection by incorporating height-based information (height map, shadow map, visibility map) obtained through multiple light sources irradiating the board at different angles. This dimensional extension enables accurate detection of bridge connecting failures even when components are distorted or positioned unstably due to board distortion.
2Adaptability or versatility
If the board is distorted, then the board structure is flexible, but the position of inspection object components becomes unsteady
Solution Approach 1:
The patent performs preliminary acquisition of rotation information of components through 3D imaging before the actual bridge connecting failure inspection. By pre-obtaining the rotation state and establishing an inspection region based on this rotation information, the system can accurately locate and inspect bridge regions even when component positions are unstable due to board distortion.
3Measurement precision
If multiple lights are irradiated on the board to acquire 2D and 3D data, then the detection accuracy is enhanced, but the inspection process becomes more complex
Solution Approach 1:
The patent segments the inspection process into distinct functional steps: (1) acquiring 2D images and height-based information through multiple light sources, (2) extracting rotation information from the acquired data, (3) establishing an inspection region based on rotation information, and (4) extracting bridge regions and judging bridge connecting failures. This segmentation allows for efficient processing and reduces overall inspection time while maintaining high accuracy.
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
This approach allows for precise inspection of bridge connecting failures by establishing an inspection region based on rotation information and combining 2D and 3D data to accurately identify bridge regions, improving detection accuracy even when the board is distorted.
Implementation Method 1
acquiring a 2D image and height-based information through a plurality of lights which are irradiated on a board, on which the component is mounted and are reflected from the board
Data Source
AI summary
A method for detecting a bridge connecting failure to detect a bridge shorting terminals of a component includes acquiring a 2D image and height-based information through lights irradiated on a board, acquiring rotation information of the component using at least one of the 2D image and the height-based information, establishing an inspection region for detection of the bridge connecting failure using the rotation information, extracting a first bridge region within the inspection region using the 2D image, extracting a second bridge region within the inspection region using the height-based information, and judging whether the bridge connecting failure of the component occurs by using at least one of the first and second bridge regions. Thus, the method may inspect more precisely the bridge connecting failure through the first bridge region extracted from the 2D image and the second bridge region extracted from the height-based information.