Overview
Today, the successful digital twin implementation requires high levels of integration between the virtual models and the physical configuration of product and assets in the field. While it is essential that the complete configuration of the physical equipment be captured along with real-time operational data, the virtual models developed are the critical component that allow engineers and operational staff to accurately assess the state and performance of products, processes, and equipment.
Product design and test have become much more of a concurrent exercise with the advent of advanced simulation platforms that allow the design engineer to test design concepts as they go, not only accelerating the design process, but eliminating fit, form, and function concepts that don’t meet design criteria before they are “baked” into the design.
Developing a digital twin using advanced simulation models takes the notion of concurrent design and test to the next step, where the product or equipment is in the field and operational. This is where the concept of a real-time digital twin functions in a closed loop construct continuously feeds operational data to analytic engines that assess the current state and use AI/ML to improve performance. However, the prerequisite to this optimization process is an accurate virtual model of the product/process that provides a virtual baseline of the desired optimal state of the physical component.
Digital twins offer a more comprehensive solution to IoT and the industrial IoT because they are based on a deeper and more accurate simulation process than CAE-based simulation. The distinct advantage to digital twins is being able to collect and analyze authentic real-time data from an asset’s continual operation across a lifecycle.
The Scope of Digital Twin Implementation Continues to Evolve
Digital twins are increasingly being used as a tool to help inform wider business decisions as well as fundamentally changing business models. They are only limited by the scope and robustness of the data being accessed. Additionally, when implemented in an IIoT environment and across a digital thread, digital twins can share data between different systems providing a clearer and more precise picture of performance on a larger scale, such as entire factories and supply chains.
Where simulation can provide the means to understand what may or can happen in the real world, a digital twin allows engineers and operations technicians to compare what may happen concurrent with what is actually happening in real-time. The digital transformation of production processes or assets in the field can enhance product design, optimize asset performance, and provide insights into building the next generation of systems and factories. However, this is very much de-pendent on the quality of the data, and the level of intelligence in smart edge devices and sensors.
Digital twins are components of larger IIoT ecosystems based on multi-tiered architecture that ranges from enterprise level cloud network infrastructure to edge computing services and platforms and culminates at the far edge of intelligent devices and IIoT endpoints. Digital twins are dependent on the digital thread that is supported by this multi-tiered architecture. The design/build/operate/maintain lifecycle is connected by the digital thread. The digital twin is an operational implementation of the digital thread.
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Keywords: Comprehensive Close-loop Digital Twin, Virtual Modeling, CAE, Digital Thread, Model-based Simulation, Real-time Operational Data, ARC Advisory Group.