Assembly Image Comparison for Fastener Installation Defect Detection
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Solution Overview
Problem
Existing assembly processes for computing devices, such as smartphones, often result in quality control issues due to improperly installed fasteners like missing or incorrectly tightened screws, which are not efficiently detected.
Innovation Solution
A system utilizing multiple cameras and sensors to capture images of assemblies from different angles, comparing them to profile images for accurate installation validation, and generating a matching score to determine correct component placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual inspection methods are used for component installation validation, then human operators can visually check for missing or incorrect fasteners, but the process is time-consuming and prone to human error
Solution Approach 1:
The patent replaces manual visual inspection with an automated image processing system that uses computer vision algorithms to detect and validate component installations. The system captures images of the assembly and automatically analyzes them to identify missing, incorrect, or improperly installed fasteners, eliminating human operators from the inspection process while improving both accuracy and speed.
Solution Approach 2:
The system creates digital copies (images) of the physical assembly and performs validation on these copies rather than requiring physical manipulation or manual inspection of the actual components. This allows for rapid, non-contact inspection that can be performed on multiple assemblies simultaneously, significantly reducing inspection time while maintaining high detection accuracy.
2Reliability
If multiple cameras and sensors are deployed to capture images from different angles, then detection accuracy improves, but device complexity increases
Solution Approach 1:
The validation system is divided into modular functional components: image capture subsystem (cameras and sensors), image processing subsystem (algorithmic analysis), and validation subsystem (comparison against specifications). This segmentation allows each component to be optimized independently and facilitates easier maintenance and scaling without proportionally increasing overall system complexity.
Solution Approach 2:
The system employs a multi-functional validation platform that can inspect various types of components (fasteners, connectors, electronic components) across different assembly types using the same core technology. The image processing algorithms are designed to be adaptable to different component geometries and installation requirements, reducing the need for specialized equipment for each component type and thereby managing system complexity.
3Productivity
If automated image processing algorithms are used to compare component positions, then inspection speed increases, but measurement precision may be compromised without human judgment
Solution Approach 1:
The system incorporates feedback mechanisms where the image processing algorithms continuously refine their measurements by comparing detected component positions against known good references and specification tolerances. The system provides feedback on detection confidence levels and can adjust processing parameters to optimize both speed and precision, ensuring accurate measurement while maintaining high throughput.
Solution Approach 2:
The system performs preliminary actions by pre-loading specification data, tolerance ranges, and reference images into the processing system before inspection begins. This allows the automated algorithms to immediately compare captured images against predetermined criteria without requiring real-time human judgment, achieving both high speed and consistent precision through pre-programmed validation rules.
Data Source
AI summary
A method is disclosed that includes receiving, by a processing device, a plurality of images of a test assembly. The processing device selects a component in the test assembly and an image of the plurality of images of the test assembly as received. For the component as selected and the image as selected, the processing device compares a plurality of portions of the image as selected to a corresponding plurality of portions of a corresponding profile image and computing a matching score for each of the plurality of portions. The processing device selects a largest matching score from the matching score for each of the plurality of portions as a first matching score for the component as selected and the image as selected. The first matching score is stored for the component as selected and the image as selected.