Bearing Retainer Window Inspection With Image Matching and Laser Localization
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Solution Overview
Problem
Manual inspection of bearing holder windows is labor-intensive, slow, and prone to human errors, affecting the quality and pass rate of finished products.
Innovation Solution
A method and device using a rotating platform and camera for automated window inspection, combined with laser positioning, to accurately identify and classify window anomalies, reducing operator workload and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual inspection method is used, then operator can inspect bearing holder windows, but inspection process consumes a lot of manpower and time
Solution Approach 1:
The patent replaces the manual mechanical inspection system with an automated optical inspection system. A camera captures images of bearing holder windows, and image processing algorithms automatically analyze the images to detect anomalies such as missing windows, abnormal positions, and dimensional deviations. This substitution eliminates the need for manual visual inspection, significantly improving inspection efficiency and reducing time consumption.
Solution Approach 2:
The inspection system performs self-service by automatically capturing images, processing them through algorithms, and generating inspection results without human intervention. The system autonomously identifies window anomalies, calculates dimensional parameters, and determines pass/fail status, enabling the inspection process to serve itself and eliminating dependency on operator availability and expertise.
2Reliability
If manual inspection method is used, then operator can detect window anomalies, but human errors affect product quality
Solution Approach 1:
The patent replaces the human operator's visual inspection system with an automated optical measurement system. The camera and image processing algorithms objectively detect and measure window characteristics without being subject to human fatigue, distraction, or subjective judgment errors. This substitution eliminates human errors as a harmful factor while maintaining high inspection accuracy through precise digital image analysis.
Solution Approach 2:
The system implements feedback by automatically comparing measured window parameters against predetermined standards and specifications. The image processing algorithm provides real-time feedback on whether each window meets quality requirements, clearly indicating pass or fail status. This objective feedback mechanism eliminates the variability and potential errors associated with human judgment while ensuring consistent application of quality standards.
3Manufacturing precision
If detailed inspection of all window aspects is performed manually, then comprehensive quality check is achieved, but labor cost increases
Solution Approach 1:
The patent implements a universal inspection system that can detect multiple types of window anomalies using a single integrated setup. The camera and image processing algorithm simultaneously assess window presence, position, dimensions, and other characteristics, eliminating the need for multiple specialized inspection devices or methods. This multi-functional approach achieves comprehensive inspection detail while avoiding the complexity and cost of multiple separate systems.
Solution Approach 2:
The system creates a digital copy of the bearing holder windows through image capture, allowing comprehensive analysis of all window aspects without physically manipulating or disassembling the component. The digital image serves as a replica that can be measured, analyzed, and archived, enabling detailed inspection while reducing the need for complex physical inspection fixtures or measurement tools.
Data Source
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AI summary
A window inspecting method and device for a bearing holder, belonging to the technical field of bearing holder inspection, are disclosed to solve the technical problem that the windows of existing bearing holders are generally inspected manually, which consumes a lot of manpower and makes the quality inspection slow, thus affecting the pass rate of the finished products of bearing holders. The method includes: comparing and matching each window image in an initial window image set with each other; selecting a plurality of anomaly-matched window images from comparing and matching results to obtain a plurality of anomaly window images; identifying and labeling an anomaly region image in the anomaly-matched window image to obtain an anomaly-labeled region; performing window anomaly classification on the anomaly-labeled region to obtain a window anomaly type; photographing an anomaly window corresponding to a window anomaly type for a second time to obtain an actual window anomaly image; and performing two-dimensional coordinate system related ray localization to obtain position information of the anomaly window.