Visual Inspection Profile Switching for Multi-Stage Production Items
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Solution Overview
Problem
Current visual inspection systems for production lines are inflexible, expensive, and time-consuming to set up, limiting their ability to adapt to multiple products and product stages, and they fail to provide a correlated defect determination for entire products comprising multiple stages.
Innovation Solution
An automated visual inspection system using a single controller with a defect detection algorithm that can easily transition between inspecting multiple products and product stages, allowing for flexible inspection without tailored integration or rigid positioning, and enabling correlation of defect status across different product stages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a dedicated visual inspection system is configured for a specific product and stage, then inspection precision and reliability are improved, but adaptability to different products and stages deteriorates
Solution Approach 1:
The inspection system is designed to inspect multiple product types and stages using a single unified platform. The system captures images of different products (e.g., first product type and second product type) and their respective stages (e.g., first stage, second stage, third stage) using the same imaging设备和检测算法, eliminating the need for dedicated systems for each product-stage combination.
Solution Approach 2:
The system dynamically adapts its inspection parameters and algorithms based on the detected product type and stage. By using machine learning models that can be trained and adjusted for different products, the system maintains high detection precision while accommodating versatility, transitioning from static dedicated configurations to dynamic adaptive inspection.
2Measurement precision
If a dedicated visual inspection system is customized for a specific task, then defect detection precision is improved, but setup time and complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models on diverse product data and pre-configuring inspection parameters for multiple product types and stages. This preliminary preparation enables the system to quickly adapt to new inspection tasks without requiring time-consuming custom setup, as the foundational detection capabilities are already in place.
Solution Approach 2:
The system uses image copying and data replication techniques where inspection models trained on one product type can be adapted to similar products. By maintaining a library of trained models and inspection configurations that can be copied and adjusted, the system reduces setup time while preserving detection precision through proven algorithms.
3Reliability
If multiple dedicated inspection systems are deployed for different products, then comprehensive defect coverage is improved, but system complexity and cost increase
Solution Approach 1:
The patent merges multiple dedicated inspection systems into a single unified system that handles multiple product types and stages. By combining imaging capabilities, processing units, and detection algorithms into one integrated platform, the system achieves comprehensive defect coverage across all products while reducing overall system complexity and eliminating redundant components.
Solution Approach 2:
A single inspection system is designed to perform multiple inspection functions across different products and stages simultaneously. The universal system uses adaptable algorithms that can be configured for various product types (first product type, second product type) and stages (first stage, second stage, third stage), providing comprehensive coverage without requiring separate dedicated systems for each function.
4Measurement precision
If inspection systems are highly customized for specific products, then inspection accuracy is improved, but flexibility to switch between products deteriorates
Solution Approach 1:
The inspection system transitions from static customized configurations to dynamic adaptive inspection. Machine learning models are designed to dynamically adjust their parameters and detection strategies based on the input product type and stage, maintaining high inspection accuracy while providing flexibility to switch between different products without requiring physical reconfiguration.
Data Source
AI summary
An appliance and method for automated visual inspection of at least two items from different categories, on a production line, include automatically switching between an item profile of a first of the at least two items for inspection; and an item profile of a second of the at least two items for inspection, based on detection of the first item in an image of the production line.