Modular Robotic Cells With Digital Twin Auto-Calibration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing automation systems in manufacturing are costly, time-consuming, and lack integration with workflow solutions, requiring manual calibration due to inaccurate simulation models and high capital and engineering expenses, leading to inefficient reuse and deployment.
Innovation Solution
A software-defined manufacturing system utilizing modular robotic cells with integrated computer vision, auto-calibration, and recipe-based programming, enabling rapid reconfiguration, calibration, and deployment through a digital twin for accurate and efficient automation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If custom-tailored automation solutions are engineered for specific manufacturing projects, then the automation can be optimized for that specific task, but the time and cost to design, build, deploy, configure, program, and debug increases significantly
Solution Approach 1:
The system segments automation functionality into modular robotic cells that can be independently configured and reused. Each cell performs a specific manufacturing function and can be combined with other cells to create complete automation solutions, reducing overall system design time while maintaining task-specific optimization.
Solution Approach 2:
The system uses digital twins to create virtual copies of physical robotic cells for simulation and testing. This allows automation programs to be developed, validated, and debugged in the digital domain before deployment to physical systems, significantly reducing commissioning time and risk.
2Productivity
If simulation models are used to develop automation programs, then development speed improves, but the models often differ materially from real world physical equipment leading to inaccuracies
Solution Approach 1:
The system implements bidirectional synchronization between digital twins and physical robotic cells. Sensor data from physical cells continuously updates the digital models, and programs validated in digital twins are automatically deployed to physical systems. This closed-loop feedback ensures simulation accuracy matches real-world performance while maintaining fast development cycles.
3Manufacturing precision
If manual calibration is performed to correct real-world variations in automation equipment, then positioning accuracy improves, but the process becomes time-consuming and requires expert guidance
Solution Approach 1:
The system implements automated calibration where robotic cells self-adjust their parameters based on sensor feedback and digital twin comparisons. The system automatically detects deviations from expected performance and corrects positioning errors without requiring manual intervention or expert calibration, reducing calibration time while maintaining high accuracy.
4Reliability
If expert technicians are involved to deploy and calibrate automation solutions, then deployment accuracy improves, but the cost and complexity of implementation increases
Solution Approach 1:
The system replaces manual expert intervention with automated software-based deployment. Configuration parameters, calibration data, and control programs are automatically transferred from digital twins to physical robotic cells through standardized interfaces, eliminating the need for expert technicians while maintaining deployment accuracy and reducing implementation complexity.
Data Source
Figure 1
Figure 2
Figure 3A
AI summary
The present system is a software defined manufacturing (SDM) system that integrates several technologies and methods into a system that automates the process of engineering and operating automated manufacturing systems (aka "automating automation"). In one embodiment, some or all of the below aspects of the "automating automation" system are integrated: modular, configurable, reusable manufacturing cells; computer vision systems; autocalibration systems; a recipe-based programming environment; configuration management system; production analytics; and a marketplace for sharing recipes.