Conveyor Digital Twin Configuration for Automated Parameter Tuning
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Solution Overview
Problem
Existing systems for configuring and diagnosing automation equipment, particularly conveyor systems, are limited by manual configuration processes that are time-consuming and lack precision, failing to adequately address complex parameters and operational efficiency.
Innovation Solution
A method and system for conveyor configuration and testing that involves receiving input data, simulating the conveyor system, monitoring operational parameters such as power usage and temperature, and automatically adjusting configuration parameters using machine learning models to optimize system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual configuration methods are used for conveyor systems, then system setup can be completed, but the process is time-consuming and lacks precision
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the physical conveyor system that replicates all configuration parameters, operational characteristics, and performance metrics. This digital model allows for precise configuration testing and optimization without affecting the physical system, enabling rapid iteration and precise parameter determination while eliminating time-consuming manual trial-and-error processes
Solution Approach 2:
The system performs preliminary configuration validation and performance prediction through simulation before actual system implementation. By pre-testing configuration parameters in the digital twin environment, the system identifies optimal settings in advance, preventing time-consuming adjustments during physical system commissioning and ensuring configuration precision from the start
2Productivity
If comprehensive monitoring of operational parameters is implemented, then system performance can be optimized, but system complexity increases
Solution Approach 1:
The digital twin platform serves multiple functions simultaneously: it monitors operational parameters, predicts system performance, validates configurations, and provides diagnostic capabilities. This multi-functional approach consolidates what would otherwise require separate monitoring systems into a single unified platform, reducing overall system complexity while enabling comprehensive parameter monitoring for productivity optimization
Solution Approach 2:
The digital twin acts as an intermediary layer between the physical conveyor system and the control/monitoring infrastructure. It receives data from sensors, processes it through physics-based models and machine learning algorithms, and provides refined insights back to the control system. This intermediary approach filters and structures raw data, reducing the complexity burden on the actual monitoring hardware and software while enabling comprehensive parameter analysis
3Manufacturing precision
If automated adjustment using machine learning models is implemented, then configuration precision improves, but computational requirements and system complexity increase
Solution Approach 1:
The system pre-trains machine learning models using historical operational data and physics-based simulations during the digital twin setup phase. This preliminary training establishes baseline performance relationships and configuration optimizations before actual system operation. During runtime, the pre-trained models provide rapid predictions and recommendations without requiring intensive real-time computation, achieving high configuration accuracy while managing computational complexity
Solution Approach 2:
The system implements a feedback loop where actual operational data from the physical conveyor system continuously refines the digital twin's machine learning models. Configuration parameters and performance metrics are monitored, compared against predictions, and used to update the models iteratively. This feedback mechanism improves configuration accuracy over time while the incremental nature of updates manages computational complexity by building upon existing model knowledge rather than requiring complete re-computation
Data Source
AI summary
A system and method for conveyor configuration and testing. The system is configured to execute the method, which includes: receive input data relating to configuration of a conveyor system; prepare a simulation of the configured conveyor system; operate the simulation of the conveyor system; determine at least one operational parameter related to the conveyor system to be monitored; monitor the at least one operational parameter during operation of the simulation of the conveyor system; determine if the configuration of the conveyor system needs to be adjusted based on the monitored operational parameter; if the configuration needs to be adjusted, automatically make an adjustment and return to operate the simulation of the conveyor system; and continue the simulation until otherwise terminated. In some cases, the monitoring operational parameters uses a machine learning model based on actual data from operating conveyors.