Honeycomb Structure Geometric Regularity Recognition via Binary Image Processing
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Solution Overview
Problem
Existing methods for evaluating the geometric regularity of honeycomb structures are time-consuming and cumbersome, affecting the efficiency and accuracy of quality assessment.
Innovation Solution
A method and system that includes image acquisition, image processing, vertex extraction, cell reconstruction, and quality evaluation, utilizing binaryzation, wall thickness determination, pixel assignment, and corner response functions to calculate angular and linear deviations, determining the quality of honeycomb structures by comparing with a tolerance zone.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point-by-point scanning method is used for honeycomb core surface measurement, then measurement precision is improved, but productivity deteriorates due to time-consuming and cumbersome process
Solution Approach 1:
The patent uses image copying technology to create a digital replica of the honeycomb core surface. Instead of point-by-point scanning, a complete image of the honeycomb structure is captured and copied into digital form, allowing all measurements to be performed on this digital copy. This eliminates the time-consuming sequential scanning process while maintaining measurement precision.
Solution Approach 2:
The patent replaces the mechanical point-by-point scanning system with an optical imaging system combined with digital image processing. The mechanical scanner is substituted by a camera or imaging device that captures the entire honeycomb surface at once, and subsequent processing is done through computer algorithms rather than mechanical measurement probes.
2Measurement precision
If complex image processing steps are performed for vertex extraction and cell reconstruction, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the image processing task into distinct modules: binaryzation to separate honeycomb structure from background, vertex extraction to identify cell corners, and cell reconstruction to rebuild the geometric structure. Each module performs a specific function with simple algorithms, making the overall complex task manageable and implementable with straightforward processing steps.
Solution Approach 2:
The patent performs preliminary binaryzation of the image before vertex extraction. By converting the image to binary form (black and white) first, the honeycomb structure is clearly separated from the background, simplifying subsequent vertex detection. This preliminary processing step prepares the data in an optimal format for the next processing stage.
Data Source
AI summary
The present invention discloses a method and system for recognizing a geometric regularity image of a honeycomb structure. The method includes the steps of image acquisition, image processing, vertex extraction, cell reconstruction, and quality evaluation, wherein a step of binaryzation is set between the step of image processing and the step of vertex extraction, and is to set a pixel value of a background in the image to be 0 and set a pixel value of a honeycomb skeleton in the image to be 1 to form a binary image, and the step of quality evaluation is to calculate angular deviation values of all the cells and an average thereof as well as linear deviation values and an average thereof based on the reconstructed cell image, and determine whether the honeycomb structure is qualified or not by comparing with a set tolerance zone.