Digital Twins for industrial automation

Digital Twin and Its Future in Industrial Automation

Industrial automation is undergoing a digital revolution as ‘Industry 4.0’ and Digital Twin technologies introduce new and innovative ways to execute manufacturing processes.

With IoT, artificial intelligence (AI), machine learning, and deep learning at the forefront, manufacturers will have spent close to $70 billion on these new technologies by 2020 to meet new demands.

Investments in new IoT and AI technologies for digital twins

Among these technologies, Digital Twin is changing how industrial manufacturers approach product design, operations, and post-sale services.

“Digital Twin solutions open new opportunities for industrial firms to speed up their Big Data- and AI-driven innovation transformation efforts,” Forrester reports.

A combination of vendor marketing and education—along with real-world benefits realized in Digital Twin use cases—are driving Digital Twin adoption among industrial manufacturers as they become an integral part of companies’ IoT and digital strategy investments.

What is a ‘Digital Twin’?

“Digital twins — digital representations of people, processes and things — will become more connected, providing a rich and real-time view of company processes and assets. Enterprise architecture and technology innovation leaders must plan for a diverse environment with integration challenges.”

Gartner, Top 10 Strategic Technology Trends for 2019: Digital Twins, 2019

A ‘Digital Twin’ is a digital model of a real-life object, most often a physical machine. Gartner defines a digital twin as “a software design pattern that represents a physical object with the objective of understanding the asset’s state, responding to changes, improving business operations and adding value.”

For industrial manufacturers, a digital twin allows them to create digital models of real machines, then combine both digital and physical data to deliver a better understanding of the performance and potential value of those physical machines.

This applies to all aspects of the machine’s functionality—production, maintenance, user experience, and more.

Each Digital Twin is used as a proxy for a real machine, allowing manufacturers to test concepts and hypotheses before applying them to physical machines themselves.

This digital technique—called ‘encapsulation’—allows manufacturers to adapt the digital ‘machine’ before applying changes to real hardware, which is costly and may impact other connected systems.

Digital Twins therefore have a wide variety of benefits and applications. Manufacturers can simulate plans, processes, and systems with zero risk, helping them visualize any variety of new strategies—and their results—before investing real resources in their development.

Conceptually, Digital Twins are simple. There is one Digital Twin ‘per thing,’ and that Digital Twin is updated alongside the ‘thing.’

This allows for the best possible results during experimentation. But despite its simplicity, Digital Twins have a wide range of applications, and are suitable for just about any machine model.

How to use Digital Twin for industrial automation?

Digital Twins have proven especially useful when integrated with IoT systems in industrial manufacturing.

“We see digital twin adoption in all kinds of organizations,” says Benoit Lheureux, research vice president at Gartner. “However, manufacturers of IoT-connected products are the most progressive, as the opportunity to differentiate their product and establish new service and revenue streams is a clear business driver.”

This is especially critical as IoT becomes more commonplace in industrial manufacturing environments. IoT sensors are already collecting data on real-world machines for analysis in cloud environments—building a congruous Digital Twin with each machine is a cost-effective addition to this already connected model, and can only help manufacturers improve machines and processes in the supply chain.

“The Digital Twin is not a new idea: Designers and makers of machines have long recognized the value of being able to simulate those machines on a computer screen. But growing enthusiasm for connecting machines to the Internet of Things (IoT), and for delivering services instead of only selling products, means that the Digital Twin is becoming relevant to a broader audience.”

Forrester, “Digital Twins Combine Enterprise Data And IoT To Drive New Business Value,” 2019

In this way, Digital Twins significantly reduce the cost and work required to maintain and enhance physical things.

AI, machine learning, and deep learning allow for increased automation in these processes as well. Manufacturers can introduce new concepts in digital environments, then count on digital tools to both optimize them and execute them among their physical counterparts.

Digital Twin and industrial automation use cases

Industrial automation streamlines processes in multiple manufacturing environments and use cases, and its integration with Digital Twins is no exception. Humans can manipulate digital environments, suggest or make changes, and introduce additional resources to optimize Digital Twin (and subsequently machine) performance.

Industrial automation allows for these processes to unfold automatically in digital environments before applying new revelations to physical ones. This relieves humans of these responsibilities but safeguarding equipment until automated solutions are confirmed in digital environments.

As Gartner describes, “When combining the twin data with business rules, optimization algorithms or other prescriptive analytics technologies, digital twins can support human decisions or even automate decision making.”

This is especially useful in IoT environments, where data from machines can be fed directly into integrated Digital Twin and business intelligence (BI) solutions. IoT sensors allow BI solutions to pinpoint problem areas and opportunities to optimize, before developing a comprehensive solution on which manufacturers can act.

In one scenario, a Digital Twin for industrial robots was implemented. The Digital Twin allowed manufacturers to program the robots offline for multiple scenarios, where virtual commissioning of the system could be tested.

This dramatically speeds up testing processes and minimizes downtime for the physical machines themselves, “resulting in a higher availability as well as a higher reliability with less failure risk.” The introduction of automation into this scenario rapidly speeds up the virtual commissioning process, delivering results faster and with greater efficacy.

As machines provide real-time feedback via IoT into a Digital Twin, manufacturers can use the Digital Twin to automated predictive aspects of machine performance and maintenance.

In another scenario, the Digital Twin of a locomotive can analyze real-time and historical data and provide recommendations about on-board system status for predictive maintenance and remote monitoring, with minimal human oversight.

The shared evolution of industrial automation, IoT and Digital Twin technologies

“The rise of Digital Twins coincides with the rise of the IoT. When buying machines and other assets, support for Digital Twins and continuous development of twin capabilities should be a selection factor.”

W. Roy Schulte, Distinguished Vice President Analyst, Gartner

IoT empowers industrial manufacturers to learn more about their equipment and better understand processes in real time. However, IoT adds a new layer of complexity to manufacturing environments, where the optimal application and investment strategy for IoT sensors isn’t always clear upfront.

Industrial BI leaders can use Digital Twins to optimize IoT applications and get the most out of their data. Unlike incongruous systems, Digital Twins help manufacturers improve their IoT strategy, clarifying what are the highest-impact IoT applications to render desirable results in Digital Twin environments that reflect their physical counterparts.

Digital Twins streamline the delivery of real-world IoT data as well, where Digital Twin systems are designed to be congruous with those of physical machines. This is a desirable alternative to discerning IoT data in other analytical environments, where the data must be processed to suit the system.

Manufacturers and other companies are realizing the value of marrying IoT and Digital Twin technologies. Already, 13% of organizations implementing IoT projects use at least one Digital Twin; 62% are either establishing or plan to establish a Digital Twin alongside IoT processes, Gartner reports.

Optimizing Digital Twin with manufacturing intelligence

“Rapidly expanding Internet of Things and analytics programs powered by machine learning and artificial intelligence — as well as improving models and simulations — mean that Digital Twins are coming to fill a number of roles. And they’re going to make life better.”

Forbes, How Orlando’s Work with Digital Twins May Change How We Engineer Everything,” 2019

Industrial manufacturers have already identified dozens of successful use cases for automated and BI tools like machine learning, AI, and deep learning systems.

Digital Twins lend themselves to these environments as they connect seamlessly in existing machine applications. They simply serve as an alternative to physical machines in the application of these technologies to optimize results.

Scholars define this application as an ‘Intelligent Digital Twin’. It “consists of all the characteristics of a Digital Twin as well as artificial intelligence to realize an autonomous system”:

The Intelligent Digital Twin can therefore implement machine learning algorithms on available models and data of the Digital Twin to optimize operation as well as continuously test what-if-scenarios, paving the way for predictive maintenance and an overall more flexible and efficient production through plug and produce scenarios.

Automation Technology, “An architecture of an Intelligent Digital Twin in a Cyber-Physical Production System,” 2019

The introduction of automation allows manufacturers to increasingly remove human beings from processes where they are no longer required, instead applying human resources only to mission critical areas in much larger processes.

In this way, Digital Twins can realize their true potential—simulating vast industrial environments with intense precision, and without increasing workloads for human workers.

Conclusion: Digital Twins and our automated industrial future

Gartner predicts that by 2022, over two-thirds of companies who have already implemented IoT will have deployed at least one Digital Twin.

Today, Digital Twin solutions are most often delivered in coordination or even by the manufacturers of physical equipment in which companies invest. But while this model makes sense today, companies will increasingly look to integrate Digital Twins with one another to accommodate each ‘thing’ in increasingly integrated manufacturing environments.

It’s through edge analytics solutions—specifically, IoT-edge computing frameworks—that industrial manufacturers will realize their ideal Digital Twin integrations.

IoT Edge Framework architecture
The adoption of IoT Edge Frameworks – like Eurotech Everyware Software Framework – simplifies the abstraction of Digital Twins of real assets

Eurotech provides a user-friendly and simplified application development environment. It enables asset digitalization to manage their Digital Twins for advanced analytics and data management. Contact us today to learn more about how Digital Twins and industrial automation can transform manufacturing for your company.

Leave a Reply

Your email address will not be published. Required fields are marked *