Cable End Configuration Inspection Using Neural Network Feedback
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Solution Overview
Problem
Existing methods for determining the end configuration of cables in cable processing systems are often unreliable and inflexible, leading to inaccuracies in error detection and processing.
Innovation Solution
A method utilizing a trained neural network, specifically a deep convolutional neural network, to rapidly and accurately identify regions of interest and classify error patterns in cable end configurations, enabling real-time monitoring and control of the cable processing device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If classic rule-based object recognition is used for cable configuration detection, then the system can process images, but the system becomes inflexible and complex to implement reliably
Solution Approach 1:
The patent replaces classic rule-based object recognition systems with AI-based models, specifically neural networks, to detect and evaluate cable configurations. This substitution eliminates the need for complex manual rule programming while improving adaptability to different cable types and configurations, directly resolving the contradiction between flexibility and system complexity.
2Productivity
If AI-based models are used for cable defect detection, then processing speed improves, but reliability and accuracy are often not sufficiently high for production requirements
Solution Approach 1:
The patent segments the image analysis process into distinct stages: capturing multiple images from different perspectives (upper and lower images), preprocessing these images, and then applying AI-based defect detection. This segmentation allows the system to maintain high processing speed while improving reliability through comprehensive multi-angle inspection and systematic analysis of cable configurations.
Solution Approach 2:
The system implements feedback mechanisms where detected defects and evaluation results are used to adjust and improve the detection process. The control device receives evaluation results from the AI model and can adjust processing parameters or trigger corrective actions, thereby continuously improving detection reliability while maintaining production throughput.
3Measurement precision
If multiple images are captured from different perspectives, then detection accuracy improves, but processing time increases
Solution Approach 1:
The patent employs periodic action by capturing images at specific, predetermined positions and angles during the cable processing workflow. Instead of continuous imaging, the system captures upper and lower images at key processing stages, which maintains detection accuracy while minimizing the time added to the overall process. This periodic imaging approach balances the need for comprehensive defect detection with production speed requirements.
Data Source
AI summary
Embodiments herein relate to operating a cable processing device by processing at least one cable section by a cable processing unit from an initial configuration to an end configuration, capturing at least one image of the at least one end configuration, applying a trained neural network to the at least one captured image to determine at least one region of interest from the at least one captured image in a first step, and the same neural network being designed to, in a second step, perform a classification of the at least one determined region of interest with regard to the presence of at least one learned error pattern of the at least one end configuration and determining a result associated with the classification. Moreover, a control signal is generated depending on the determined region of interest or depending on the result of the classification.