Computer Vision Inspection of TIM Patch Installation Anomalies
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Solution Overview
Problem
Existing computer vision techniques are too slow and inaccurate for real-time inspection of component installations in mass production, and deep learning models require extensive training data and are costly to maintain, leading to unacceptable errors and production line slowdowns.
Innovation Solution
A sequence of geometric approximations is used to determine an oriented bounding box for components, reducing the computational complexity from O(n^2) to O(n) and enabling fast, accurate inspection by identifying edges and vertices of components like thermal interface material patches using OpenCV and HoughLine transforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computer vision techniques are used for inspection, then measurement capability is provided, but inspection speed is too slow for real-time production lines
Solution Approach 1:
The inspection process is segmented into distinct stages: image capture, feature extraction (edges, corners, vertices), geometric calculation, and anomaly detection. This segmentation allows each stage to be optimized independently, with the geometric approach providing fast calculation for position and orientation verification.
Solution Approach 2:
The patent replaces traditional mechanical measurement systems with a computer vision-based geometric measurement system. By using image processing and geometric calculations instead of physical measurement tools, the system achieves both high speed and acceptable precision for inspection tasks.
2Productivity
If deep learning models are used to improve inspection speed, then processing speed increases, but error rate becomes unacceptably high and training data requirements are excessive
Solution Approach 1:
Instead of using complex, expensive deep learning models that require extensive training, the patent employs simple, lightweight geometric algorithms that can be quickly deployed and adjusted. These algorithms provide sufficient accuracy for inspection tasks without the overhead of training large neural networks.
Solution Approach 2:
The patent changes the approach from learning-based parameter optimization to direct geometric parameter calculation. By computing position, orientation, and dimensions directly from image features using geometric relationships, the system achieves fast inspection with reliable accuracy without needing to learn from training data.
3Measurement precision
If deep learning models are trained for specific assembly types, then inspection accuracy improves, but training data generation and model maintenance become costly and time-consuming
Solution Approach 1:
The geometric inspection system is designed to be universal and adaptable to different assembly types without requiring retraining. The same edge detection, corner identification, and geometric calculation algorithms can inspect various components by adjusting geometric parameters, eliminating the need for separate trained models for each assembly type.
Solution Approach 2:
Instead of training complex models on大量 training data, the patent uses simplified geometric representations that copy the essential measurement capabilities needed for inspection. The system captures the necessary geometric information directly from images and performs calculations based on these representations, avoiding the need for extensive training datasets.
Data Source
AI summary
Approaches presented herein provide for the automatic inspection of material element having an expected size, shape, and location. Such automatic analysis can be useful for inspecting the installation of an element, such as a patch of thermal interface material (TIM) attached to a heat sink of a graphics card. A digital image can be captured and cropped to a region of interest including an element to be inspected. A contour of the element can be identified and used to determine the location of a first edge of the element. Test lines can be swept across the area of the contour until at least one edge criterion is satisfied for additional edges of the element. The intersections of these edges can be identified and used as approximations of the corners or vertexes of the element. The coordinates of these elements, or values calculated therefrom, can be compared to expected coordinates from a reference standard to determine whether the element satisfies one or more inspection criteria.