A System-level Digital Twin for Smart Manufacturing Systems Using Software Defined Control
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Software-Defined Control (SDC) is a revolutionary methodology for controlling manufacturing systems that uses a global view of the entire manufacturing system, including all of the physical components (machines, robots, and parts to be processed) as well as the cyber components (logic controllers, RFID readers, and networks). As manufacturing systems become more complex and more connected, they become more susceptible to small faults that could cascade into major failures or even cyber-attacks that enter the plant, such as, through the internet. In this project, models of both the cyber and physical components will be used to predict the expected behavior of the manufacturing system. Since the components of the manufacturing system are tightly coupled in both time and space, such a temporal-physical coupling, together with high-fidelity models of the system, allows any fault or attack that changes the behavior of the system to be detected and classified. Once detected and identified, the system will compute new routes for the physical parts through the plant, thus avoiding the affected locations. These new routes will be directly downloaded to the low-level controllers that communicate with the machines and robots, and will keep production operating (albeit at a reduced level), even in the face of an otherwise catastrophic fault. These algorithms will be inspired by the successful approach of Software-Defined Networking.
To support such a SDC strategy, high-fidelity modeling of the manufacturing system at run-time is required. In this project, we develop a system-level digital twin that sits in the core of SDC to bridge the physical world (production plane) and the virtual world (a formal model). By processing run-time sensor data and machine operational variables (in the form of OPC tags) collected on the industrial network, real-time states of the system can be reconstructed. The system states are modeled by the SDCWorks framework, a formal model for manufacturing systems. Such states can be fed into a formally verified state-transition simulator to predict future states. This enables the proposed system-level digital twin to foresee future events and further predict the system’s manufacturing performance.
Submitted by Sibin Mohan
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Software-Defined Control (SDC) is a revolutionary methodology for controlling manufacturing systems that uses a global view of the entire manufacturing system, including all of the physical components (machines, robots, and parts to be processed) as well as the cyber components (logic controllers, RFID readers, and networks). As manufacturing systems become more complex and more connected, they become more susceptible to small faults that could cascade into major failures or even cyber-attacks that enter the plant, such as, through the internet. In this project, models of both the cyber and physical components will be used to predict the expected behavior of the manufacturing system. Since the components of the manufacturing system are tightly coupled in both time and space, such a temporal-physical coupling, together with high-fidelity models of the system, allows any fault or attack that changes the behavior of the system to be detected and classified. Once detected and identified, the system will compute new routes for the physical parts through the plant, thus avoiding the affected locations. These new routes will be directly downloaded to the low-level controllers that communicate with the machines and robots, and will keep production operating (albeit at a reduced level), even in the face of an otherwise catastrophic fault. These algorithms will be inspired by the successful approach of Software-Defined Networking.
To support such a SDC strategy, high-fidelity modeling of the manufacturing system at run-time is required. In this project, we develop a system-level digital twin that sits in the core of SDC to bridge the physical world (production plane) and the virtual world (a formal model). By processing run-time sensor data and machine operational variables (in the form of OPC tags) collected on the industrial network, real-time states of the system can be reconstructed. The system states are modeled by the SDCWorks framework, a formal model for manufacturing systems. Such states can be fed into a formally verified state-transition simulator to predict future states. This enables the proposed system-level digital twin to foresee future events and further predict the system’s manufacturing performance.