Digital twins: Good things come in pairs
This new predictive and visualization tool is gaining momentum in enterprise organizations.
Imagine having the ability to visualize a product’s performance before it goes into production, or being able to minimize unplanned downtime of critical machines in your manufacturing environment. What if you had the capability to do destructive stress tests on crucial machine components without impacting production cycles?
If these sound like scenarios that can help your business, you may want to look into digital twin technology. And if you’re wondering how this is different from the computer-aided design (CAD) 3D prototypes currently in use, read on.
For a technology that has been featured in Gartner’s Top 10 Strategic Technology Trends in 2017 and 2018, digital twinning has not garnered the same level of attention as artificial intelligence (AI), blockchain and augmented reality/virtual reality (AR/VR).
That lack of hype may have something to do with its early applications being based primarily within the asset-heavy industrial sector. However, digital twins are finding implementations that go beyond manufacturing to help businesses improve their products, processes and services.
What is a digital twin?
Digital twinning refers to the process of creating a virtual replica of a physical asset, a process or a system. The virtual twin typically resides in the cloud and is paired with its “connected” physical counterpart to mirror all the changes — in near-real-time — that the physical object undergoes.
Digital twins allow companies to create simulations and “what-if” scenarios of a physical device enabling stakeholders to preview the impact of such changes before rolling them out into production.
Based on the results of the simulations on the digital twin, the physical device or system can be modified to ensure optimal performance. Unlike static CAD models, digital twins are dynamic and reflect the current state of the physical device at all times.
Two kinds of twins
According to Dr. Michael Grieves, one of the earliest proponents of the concept, digital twins can fall into two categories: Digital Twin Prototype and Digital Twin Instance.
- Digital Twin Prototype (DTP) entails the creation of a digital version of the physical object first. Once the virtual model meets the required parameters and performance thresholds, the specs for the creation of the physical equivalent is made available and can be used to build it. The changes to the physical asset are mirrored on the virtual twin throughout the lifecycle of the physical device.
- Digital Twin Instance (DTI) is about mapping a single physical asset to a digital twin using current and historical data available through a bill of materials, build specs and other operational metrics. The digital and physical instances are kept paired to ensure that updates and changes to the physical device are accurately reflected in the virtual instance. Reliance on a single digital twin can be mitigated by aggregating several DTIs into a Digital Twin Aggregate (DTA).
How the technology works
Connected things and IIoT provide the perfect backdrop for digital twins. Physics-based simulation tools are used to create an exact 3D digital replica of a physical object.
The model, referred to as the digital twin, reflects the known physical and operational characteristics of the physical asset. Leveraging the sensors, actuators, and edge computing technologies, operational and machine-to-machine data collected from the IoT-enabled physical device is forwarded to a cloud-based system which in turn updates the digital twin in near-real-time to mirror the state of the physical entity.
The virtual twin can be further augmented with historical data, intelligence from other machines, industry knowledge and input from humans, making it possible for businesses to simulate, preview and predict future operational impacts.
The advantages of digital twins
The predictive capabilities of digital twins are invaluable to businesses who traditionally relied on blueprints and physical prototypes to create and manage assets. The costs and limitations associated with physical models are mitigated through the use of digital simulations, risk-modelling and even destructive tests that in a physical world would be cost prohibitive.
Some of the clear benefits include:
- Design Integrity: The ability to digitally simulate and visualize a product through its design phase ensures the integrity of the final product.
- Interworking: System integration conflicts and constraints can be identified and resolved to ensure smooth interactions of systems.
- Predictability: Potential failures can be identified in advance to take corrective and remedial actions without impacting the production environment.
- Diagnostics: The capability to recreate failures and breakpoints of non-accessible components of a machine can assist in troubleshooting.
Applications for your business
As you would expect, digital twins have received the most traction in asset-heavy industries such as manufacturing, power generation, oil and gas, aerospace and automotive.
However, the potential benefits of digital twins as a predictive and visualization tool is gaining momentum in enterprise organizations. As per Deloitte Insights, smart cities, retail, healthcare and other industries are piloting digital twin technology currently. General Electric demonstrates the power of digital twins through this video:
Digital twin technology is not for every organization. There are other trends such as IoT, AI, AR/VR and cloud/edge computing that may need prioritization as part of your overall digital transformation goals. However, for the right business environment, the predictability and the foresight the digital twin technology provides is indisputable.