Variable Focus Liquid Lens Assembly for Adaptive Object Recognition
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Solution Overview
Problem
Existing additive manufacturing processes face inefficiencies, product inconsistencies, and unreliability, leading to increased costs and supply chain inefficiencies, while conventional machine vision systems struggle with capturing rich object information and dynamic environments, and robotics implementations fail to leverage emerging technologies for full automation.
Innovation Solution
A robot fleet management platform with an intelligence layer that applies governance standards and AI services for decision-making, coupled with a robot fleet configuration system that optimizes task allocation and workflow simulation, and an information technology system integrating digital twins and adaptive intelligence for real-time monitoring and control of additive manufacturing processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional machine vision systems are used, then system simplicity is maintained, but object information capture capability is insufficient
Solution Approach 1:
The patent combines multiple imaging lenses with different focal lengths into a single optical assembly, allowing the system to capture both macro and micro object information simultaneously. This merging approach resolves the contradiction by integrating multiple information-capturing capabilities without requiring separate vision systems, thus reducing information loss while managing system complexity through unified design.
Solution Approach 2:
The optical assembly is designed to perform multiple functions by incorporating lenses with varying focal lengths, enabling the same system to capture both distant and close-up object details. This multi-functionality allows a single vision system to handle diverse imaging requirements, improving information capture capability without proportionally increasing system complexity.
2Adaptability or versatility
If additive manufacturing processes are used, then manufacturing flexibility is improved, but product consistency and reliability deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where the vision system captures images of manufactured parts, and AI algorithms analyze these images to detect defects and variations. This feedback loop allows real-time monitoring and adjustment of additive manufacturing processes, ensuring product consistency and reliability while maintaining the flexibility of additive manufacturing for complex geometries and customization.
Solution Approach 2:
The patent replaces traditional mechanical measurement and inspection methods with optical imaging and AI-based analysis. This substitution enables non-contact, high-precision measurement of complex additive manufactured parts, improving detection accuracy for surface defects and dimensional variations, thereby enhancing product consistency without compromising manufacturing flexibility.
3Productivity
If robotics automation is implemented, then productivity is improved, but automation capability and adaptability deteriorate
Solution Approach 1:
The patent employs dynamic vision guidance that allows robotic systems to adapt to varying object positions, orientations, and types in real-time. The AI-powered vision system dynamically adjusts imaging parameters and robot motion paths, enabling high-speed automated operation while maintaining the flexibility to handle diverse parts. This dynamic capability resolves the contradiction by making automation adaptable rather than rigid.
Solution Approach 2:
The vision system with AI algorithms performs self-calibration and automatic defect classification, reducing the need for human intervention in quality inspection. The system autonomously identifies defects, categorizes them by severity, and guides robotic sorting or rejection actions, thereby enhancing both productivity and automation capability simultaneously through intelligent self-management.
Data Source
AI summary
A dynamic vision system includes a variable focus liquid lens optical assembly. The dynamic vision system includes a control system configured to adjust one or more optical parameters and data collected from the variable focus liquid lens optical assembly in real time. The dynamic vision system includes a processing system that dynamically learns on a training set of outcomes, parameters, and data collected from the variable focus liquid lens optical assembly to train one or more machine learning models to recognize an object.