Manufacturing even the simplest product requires expertise in materials, equipment, operations, and process flow. And the interdependencies of these elements make it extremely difficult — if not impossible — to “get it right the first time” when setting up a new manufacturing line or modifying an existing process. Every element, from the product design to the manufacturing layout to the control system, introduces multiple possibilities for errors or inefficiencies that are difficult to uncover until the system is up and running. But digital twins reduce or eliminate these uncertainties by providing a way for designers and programmers to test and verify the real-world system before physical assets are ever put into place.

According to the Digital Twin Consortium, a digital twin is “a virtual representation of real-world entities and processes, synchronized at a specified frequency and fidelity.”

More specifically, a digital twin is a digital version of a real-world component, machine, or process that exactly replicates the real-world system. The digital twin is created from data regarding the physical and operational characteristics of the product, machine, or process — including everything from the bill of material and mechanical properties, to control logic and operational status, to machine performance and diagnostics. This data is transferred between the real-world system and its digital twin via a digital thread, allowing the digital twin to not only replicate the physical product or process, but also to exactly mimic its behavior.

A key component of the Digital Twin Consortium’s definition of the digital twin is synchronization at a specific frequency and fidelity. In other words, a digital twin is regularly updated — preferably in real-time — to ensure it remains in sync with the physical product, machine, or process. This is especially important for digital twins that are developed to help with design and commissioning, before a product is created or a process is implemented. In these scenarios, once the real-world version is in place, the digital twin will likely need to be updated to reflect any changes made in the real-world that weren’t captured by the digital version. Otherwise, the digital twin becomes an inaccurate representation of the real-world situation, and using it for future updates, modifications, or maintenance could lead to wasted effort, time, and cost.

The digital thread is a communication framework that captures disparate data types and formats from a product or process across its lifetime — from development and design through service and decommissioning. This data is extracted from systems such as CAD, PLM (product lifecycle management), IIoT devices, ERP (enterprise resource planning), and MES (manufacturing execution systems). In addition to capturing and consolidating data, the digital twin uses machine learning and AI to analyze the data and make it available to stakeholders in a consistent, reliable way. 

In the context of digital twins, the digital thread is the link between the real-world product, machine, or process and its digital twin.

For industrial manufacturing and automation, digital twins make it possible to design and commission a system — including motion control programs and operating logic — in the virtual world, before building prototypes and installing equipment. So designers and control engineers can test scenarios and optimize the system before it’s installed, significantly reducing the actual build, programming, and troubleshooting time. And for existing systems, digital twins allow manufacturers to simulate new product designs, processes, or production methods, validating and optimizing these changes before implementing them on the manufacturing floor, so downtime and disruptions to production are minimized.

Beyond design and manufacturing, digital twins can be used to diagnose and troubleshoot problems with a process, machine, or product. For example, by comparing the performance or outcome generated by the virtual twin with the performance or outcome generated by the physical twin, discrepancies can be pinpointed and issues with the physical twin are easier to identify. And when coupled with AR (augmented reality) tools, digital twins can be used to help technicians determine the most effective, efficient ways to make repairs.

Even though all the data necessary to create digital twins resides in the enterprise, this data is often “siloed” in different departments and transmitted over different networks using multiple protocols. To overcome these challenges, many traditional enterprise software companies, industrial equipment providers, and product lifecycle management companies have created software and processes that aid in both digital thread and digital twin implementations.