How digital twins, IIoT technologies benefit operations
Examine how digital twins and Industrial Internet of Things (IIoT) technologies work together. Digital twins benefit industrial operations and systems. Digital twin updates can be automated.
- Define digital twins and determine level of details they should involve.
- Learn about automating digital twin updates.
- Review two digital twin examples.
Digital twins, which have been made possible in large part by the Industrial Internet of Things (IIoT), are enabling easier optimization of industrial operations, devices, and systems. Digital twins and IIoT technologies work together. Implementing digital twins can benefit industrial operations and systems. See questions, answers and key points below.
Digital twin, defined
First, let’s define what a digital twin is (and is not).
A digital twin is a digital representation of a real-world machine, device, or process. But a digital twin is more than just a simulation. A digital twin exactly replicates the function and behavior of the real-world system. This replication is enabled by the digital thread, a communication framework that extracts data from platforms that are connected to the real-world system, and feeds this data to the digital twin. Systems connect to digital twins may include IIoT devices, computer-aided design (CAD) software, product lifecycle management (PLM) software, manufacturing execution systems (MES), and enterprise resource planning (ERP) systems. The digital thread also makes it possible for users to run simulations on the digital twin and make decisions based on objective, real-world scenarios, outcomes, and data.
Without technologies such as the IIoT and cloud-based data management, which provide granular data in real time, digital twins would be impossible to implement, or, at least, much less effective.
Digital twins: How much detail
How much detail is needed to make digital twins useful?
The short answer is, it depends. The amount of detail needed for a digital twin depends on what the user is trying to achieve. In a perfect world, the digital twin would exactly replicate an entire manufacturing process, including front-end and back-end logistics. Or, in the case of product design, the digital twin would replicate the entire design process, from concept to build. For most manufacturers, and their purposes for using digital twins, such detail isn’t practical or feasible.
In the real world, most digital twin implementations are focused on a specific process or product. When digital twins are used to optimize a process, the information fed into the digital twin should encompass the core process, such as process timing, flow, quantities, and reject or rework rates. If possible, the digital twin also should include one or two steps upstream and downstream of the core process. The most important thing is once the focus area is determined, as much information should be gathered within the realm as possible.
Key point: When a digital twin is used to assist with the development of a process or product, keep in mind the digital representation is being created before the product or process is built or implemented. Therefore, it’s important to update the digital twin once the physical system is put into place. Without updates, the digital version will likely be an inaccurate representation of the real-world system. And if an inaccurate digital twin is used down the road (for optimization, further development, or even troubleshooting a problem), it can lead to wasted time, effort, and of course, cost.
Frequency of digital twin updates
How often do digital twins need to be updated to continue to be useful?
Key point: The digital representation should be updated any time the real-world system changes, even if the change seems insignificant. In a manufacturing or production process, changes can happen that aren’t immediately noticeable, such as a part slipping or a line losing speed over time. Having a digital twin that’s accurate and up to date allows those involved to compare the real-world process and outcome to that predicted by the digital twin. Users can then determine, with very high accuracy and reliability, where the issue is happening or what’s causing it. This is one of the key ongoing benefits of digital twins, making them valuable even after the initial design, development and commissioning.
Although keeping the digital twin up to date is simple in theory, some manufacturers find it to be a daunting task, since industrial processes involve so many variables that can constantly change.
Automating digital twin updates
Can the updates be automated?
Some of the biggest challenges when digital twins were first being adopted were communication and standardization. Even if all the information were available, it was in disparate devices, such as sensors, controllers, 3D models, ERP systems, and so on. There were challenges getting the information into the same format or language, aggregating it, and transferring it to the digital twin.
Key point: Digital twin information integration still can be a challenge, but machine learning and artificial intelligence (AI) technologies have helped resolve these issues. Now, digital thread platforms can capture data from different systems, standardize it, and provide a seamless link between the physical process or product and the digital twin. With automated capabilities, the digital thread handles updates to a large extent, particularly when applied to individual products or processes.
However, it’s more challenging to automate digital twin updates for very large-scale processes and entire industrial operations. If we’re talking about a complete production line or plant, the timing of every event and sequence needs be transferred to the digital twin – and this information can be difficult to extract from the real-world components and systems with existing technology.
With that in mind, let’s look at the scenarios in which digital twins can provide a real benefit to manufacturers.
Where are digital twins best applied?
Where do digital twins make sense?
Digital twins can be applied to just about any product or process. In fact, digital twins are often used in non-manufacturing industries, such as software development. I think their real value comes from how digital twins can be used across the lifecycle of a product, process, or system.
Digital twins can be used during the development of a product or process, to simulate the design, function, and/or workflow. Digital twins are helpful in manufacturing applications where there are multiple axes or movement or processes occurring at the same time or in tight sequence with one another.
Digital twin example for an automotive industry assembly line: A part might travel down the production line on a conveyor, with multiple robots working on the part at different stations. All this motion and these processes must be very tightly coordinated and controlled – from the time the part is loaded onto the conveyor to the time it’s unloaded. If the part slips on the conveyor, or if there’s a fault at some location, how will this affect the rest of the production line? Or if the goal is to increase conveyor speed 20%, users need to know how the cycle time of the robots and end effectors need to change to keep up with the increased conveyor speed. These, along with other scenarios, can be simulated and resolved with digital twins.
Digital twin example for product or process modification, testing: Digital twins also are valuable for testing the outcome of modifications to a product or process. The digital twin lets designers and control engineers test a wide range of scenarios and cases without disrupting production or investing in physical prototypes and testing equipment. And when the final iteration is locked in, implementation and startup are much faster and smoother since the long list of “what-ifs” that would normally have to be addressed at this stage have already been resolved. Using digital twins in this scenario takes a significant amount of the uncertainty and risk out of the process.
Uncertainty can lead to expensive mistakes and wasted time and effort. This is why the monetary value of digital twins is often determined by looking at how much time and cost were (or could be) saved during development, startup and commissioning, modifications throughout the product or process lifecycle and troubleshooting efforts.
Cost of digital twins: Time, money
Isn’t there a significant cost and time investment required to implement digital twins?
Key point: One of the biggest misconceptions about implementing digital twins is the upfront cost and time will make it difficult to justify. The reality is the digital twin system already is being designed digitally as a product using product design software or a control system being programmed in a programmable logic controller (PLC). Digital thread platforms can bring in the 3D models, programs and all the data from various devices, so engineers and the design team don’t have to worry about the data transfer and communications among all the pieces.
A digital twin has an upfront investment, but a digital twin can save a significant amount of design, programming, and setup time and prevent the “gotchas” and last-second disasters. These benefits, plus the long-term benefits for simulating changes and troubleshooting problems, are much more valuable than the upfront time and cost a digital twin requires.
Sam Hoff is the founder and president/CEO of Patti Engineering Inc., a certified member of the Control System Integrators Association (CSIA). CSIA and Patti Engineering are CFE Media content partners. Edited by Mark T. Hoske, content manager, Control Engineering, CFE Media, firstname.lastname@example.org.
KEYWORDS: Digital twin, Industrial Internet of Things (IIoT)
Did you know AI is helping with automated updates to digital twins?