The digital twin: revolutionising the product and the process

The Industry 4.0 evolution will not be possible without a number of technological breakthroughs, not least of which is the successful realisation of the digital twin and the threads that connect it to the physical world.

We are all familiar by now with the plethora of press and publicity surrounding the Industry 4.0 concept, alternatively referred to as the Industrial Internet and the Industrial Internet of Things (IIoT). While many would say these terms are not strictly equivalent, there is certainly much press linking them as being so. While the IIoT is more an extension of the IoT network concept of the commercial world, the first two terms are more central to the idea of industrial systems and manufacturing — and are both centred on the concept of cyber-physical systems.

The concept of a cyber-physical system is in itself very interesting and presents some interesting future possibilities, aside from the utilitarian purpose of business efficiency and competitive advantage for manufacturers. The concept represents the integration of computing, networking and physical processes, in which computers and networks monitor and control physical processes with feedback loops by which physical processes affect computations and vice versa. The cyber-physical system concept is not limited to manufacturing but extends to many other aspects of life and business, including communications, health care, transportation, energy and consumer goods.

Central to the idea of cyber-physical systems in relation to manufacturing (and perhaps in other areas too) is the concept of the digital twin.

The digital twin

The digital twin concept is not new. While the term was first coined by the Defense Advanced Research Projects Agency (DARPA) many years ago, it was first clearly conceptualised by Dr Michael Grieves at the University of Michigan in 2003 in his executive course on product lifecycle management (PLM) — although he called it the ‘virtual twin’. In the years that have followed, the technology supporting both the manufacturing of physical product and the development and maintenance of the virtual twin has grown exponentially.

In relation to manufactured products, virtual twins are digital representations of the products that are virtually indistinguishable from their physical counterparts. The development of 3D computer-aided design (CAD) and computer-aided manufacturing (CAM), as well as operational technology data systems that collect detailed manufacturing information — such as manufacturing execution systems (MES) — has resulted in a wealth of data collected and maintained on the production of the physical products. The data collection is now being collected by a wide range of sensors, lasers, vision systems, etc.

As a digital model of a particular asset, the digital twin includes design and engineering details describing its geometry, materials, components and behaviour, as well as the as-built and operational data unique to the specific physical asset.

Digital twins provide a way to model and manage the complexities inherent in many products today. Many products now have embedded software and therefore processing capability within them, are customisable and require complex manufacturing processes, supply chains and extended value chains to design, develop, produce and service. Automobiles are perhaps the best example. Virtually modelling a product helps companies avoid costly product quality issues or manufacturing rework because manufacturing and performance variables can be modelled before they occur. Digital twins can therefore help manufacturers make their products right the first time, without failures and recalls. Not only that, they help development and support teams to share the same knowledge about the product, regardless of location.

But perfect production, while highly desirable, is not the end goal. According to Grieves, “it is timely to explore how the Virtual Twin can move from an interesting and potentially useful concept that aids in understanding the relationship between a physical product and its underlying information to a critical component of an enterprise-wide closed-loop product lifecycle. These tasks will both reduce costs and foster innovation in the manufacture of quality products”.¹

Image source: GE Reports. For a larger image click here.

The linking thread

From a visionary perspective, the ultimate goal in the development of a product is to have sensing in the product itself, which feeds data back to its digital twin over the lifetime of the product, helping to inform future product improvements and to predict potential failures and future maintenance. General Electric and the Industrial Internet Consortium have referred to this concept of the linking of the digital twin with its physical counterpart as the digital thread.

The digital thread refers to the communication system that allows a connected data flow and integrated view of the asset’s data throughout its life cycle. The ability to connect the physical product with its digital twin via a digital thread could be seen as the Holy Grail of PLM — the process of managing the entire life cycle of a product from inception, through engineering design and manufacturing, to service/maintenance and disposal.

CAD and PLM software vendors are keen on the concept and its benefits, and believe that a digital twin can deliver efficiencies to the design and production processes, improve product quality and innovation, and foster better serviceability of products, ultimately benefiting customers.²

High-flying vision

Some of the most visionary concepts involving digital twins have come from the research of NASA and the US military. An extreme but interesting example of the digital twin vision was presented in the International Journal of Aerospace Engineering in the form of a hypothetical aircraft developed for the US Air Force.³ In the article, the example describes the USAF taking delivery of a physical aircraft along with two digital twins. The first digital twin is a 1000 billion degree-of-freedom (DOF) hierarchical, computational structures model of the aircraft that “is ultrarealistic in geometric detail, including manufacturing anomalies, and in material detail, including the statistical microstructure level, specific to this aircraft tail number”. This twin can be virtually flown in simulated real time on a high-performance computer, accumulating usage damage according to physics-based simulations. This testing identifies any unexpected failure modes so that repairs, redesigns and retrofits can be implemented on the physical aircraft before its first flight.

The second digital twin is linked to an actual structural sensing system installed on the physical aircraft:

“This sensor system records, at high frequency, actual, six-DOF accelerations, as well as surface temperature/pressure readings during each actual flight.” This data is used as input to the digital twin and “this model itself becomes a virtual sensor, interpolating sparse acquired data over the entire airframe.”⁴ The twin model is periodically updated to reflect actual usage of the aircraft on real missions and updates reliability estimates for all primary structural components.

While this example may seem like science fiction (the data from a single flight for one hour alone would result in a petabyte of data), the authors state that even if only a portion of this idea is realised, “the improvements in structural life prediction will be substantial”.

Twinning the process

The digital twin concept in relation to the design and manufacture of a product is certainly feasible, but the full life cycle twin concept as described above may yet be some way off, except in the most simple of examples. However, the concept of a digital twin of the manufacturing process itself is not so far-fetched, given that modern automation systems are already capable of providing a wealth of information.

The creation of a digital twin of the entire production system opens up new ways to improve productivity. Like any other product or device, production machines and even entire facilities can have a digital twin. Modelling the production line for a product works in harmony with the modelling of the product, and the wealth of data that comes back from manufacturing execution systems can be used to enable the feedback loop between the physical plant operation and the virtual representation in real time.

Extending the digital twin concept from product to production also opens up the idea of digital twins enhancing the operation of continuous processes as well, and not just discrete product manufacturing. A good example is the work done at GE Global Research on creating a digital twin of a sewage treatment process.⁵

The research team first installed chemical sensors inside a sewage plant and then built cloud-based algorithms to mimic the biochemical processes inside the plant. The team is using biochemical and physical simulations as the first step in creating a digital twin. Using the digital twin, the team expects to discover hidden patterns in plant operation, anticipate the amounts of sewage coming in and calibrate the oxygen level needed. Eventually the digital twin would not only alert plant operators to a problem, but identify what portion of the plant is most likely the cause and reduce response times for repairs. Early warning and rapid response in such cases would save plant operators from paying costly fines and ensure cleaner waterways for communities.

Image source: GE Reports. For a larger image click here.

Supporting human processes

According to Grieves, the virtual twin concept aligns well with three important aspects of how humans work with knowledge: conceptualising, comparing and collaborating.

Conceptualising

Virtualising a concept and presenting it visually allows human beings to better visualise a concept, problem or solution.

“Unlike computers, humans do not process information, at least not in the sense of sequential step-by-step processing that computers do. Instead, humans look at a situation and conceptualize the problem and the context of the problem… While they can do this looking at tables of numbers, reports, and other symbolic information, their most powerful and highest bandwidth input device is their visual sight.”⁶

Instead of looking at a report and trying to mentally visualise how the process is operating, viewing a digital twin representation will allow people to ‘see’ the actual process along with relevant information about the physical product, eliminating the counterproductive mental steps in which misunderstandings can occur.

Comparing

In assessing situations, we tend to compare our desired outcome with the actual result and try to find ways to eliminate the difference. When the desired detailed information is separate from the physical product information, we can compare, but it is inefficient.

“With the virtual twin model, we can view the ideal characteristic, the tolerance corridor around that ideal measurement, and our actual trend line to determine for a range of products whether we are where we want to be. Tolerance corridors are the positive and negative deviations we can allow before we deem a result unacceptable.”⁷

Using visual models, whether it be for the final product or the manufacturing process itself, we can not only directly and visually conceptualise what is going on, but have instant visual comparison information at the same time. We can instantly see ‘where we are’ and compare it with ‘where we want to be’.

Collaborating

Collaboration is the most powerful thing that we do — allowing us to bring more perspectives and knowledge to problem-solving and innovation.

The advantage proffered by a digital twin is that it “allows a shared conceptualization that can be visualized in exactly the same way by an unlimited amount of individuals and by individuals who do not need to share the same location.”⁸

It has been common over the years for factory managers and operators to have their office overlooking the factory so that they could see what was going on. A digital twin of a manufacturing process allows everyone associated with the process to be able to see it, from anywhere, without having to be physically present at the plant.

Conclusion

The digital twin is a means by which we can merge the virtual product or process with how the actual product or process manifests, making it possible to have a real-time perspective on how the manufactured product is meeting its design specification goals or how the process is functioning within desired tolerances and measures of efficiency. Still further, it will be possible in the future to have a physical product communicate with its digital twin throughout its life cycle, enhancing product development, maintenance and customer service.

However, there is still a long way to go in making the digital twin concept a reality in all the aspects mentioned above. While there have been great advances in product modelling at the design phase, and in the modelling of physical production processes (both continuous and discrete), the connection between the model and the physical process still in most cases lacks the digital thread tying them together.

One of the many goals of Industry 4.0 is to make the benefits of digital twins a reality. The plethora of information available in modern manufacturing and process plants lends itself well to this goal — it is now necessary to invest in the networking and data infrastructure to create the digital threads that will bind the virtual world of the product and the process data to their physical counterparts.

References

1. Grieves M 2014, Digital Twin: Manufacturing Excellence through Virtual Factory Replication, Michael W. Grieves LLC.
2. Stackpole B 2015, Digital Twins Land a Role in Product Design, Desktop Engineering, Peerless Media LLC, <http://www.deskeng.com/de/digital-twins-land-a-role-in-product-design/>
3. Tuegal EJ, Ingraffea AR, Eason TG, Spottswood SM 2011, ‘Reengineering Aircraft Structural Life Prediction Using a Digital Twin’, International Journal of Aerospace Engineering, Vol 2011, <http://www.hindawi.com/journals/ijae/2011/154798/>
4. ibid.
5. Egan M 2016, This Scientist Took A Deep Dive Into A Pool Of Sewage Treatment Plant Data. Here’s What He Fished Out, GE Reports, <http://www.gereports.com/this-scientist-took-a-deep-dive-into-a-pool-of-sewage-data-heres-what-he-fished-out/>
6. Grieves, op cit.
7. ibid.
8. ibid.

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