I was thinking about digital twins and how far they had come to complement our engineering work. So, it felt utterly timely to overview and share insights that can help you choose if this one-of-a-kind tech is the next investment to push your business into the next level.
What Is a Digital Twin?
In essence, a digital twin is a virtual replica of a physical asset that continuously incorporates real-time data captured from the said asset. The main purpose is predicting behavior to enhance performance.
The complex model backing the digital system often merges IoT and Machine Learning. These technologies work together to reproduce realistic asset actions in full detail.
In turn, a dynamic living environment forms where teams gain confidence to:
- Access asset data remotely
- Explore What-If scenarios
- Predict threatening weaknesses
- Make informed and profitable decisions
- Boost agility and resilience
- Reduce risks.
Digital twins are also a terrific interface to optimize project execution. Their engaging environment propels the tryouts and testing of different settings. This experimentation phase always improves mechanical integrity, pre-assembly methods, and shipping strategies.
Types of Digital Twin
Level of Product Magnification
Hierarchy-based criteria aligned to product magnification.
a) Component/Parts Twin
It’s the most basic level of a virtual representation. Allows engineers to collect understanding about the properties and characteristics of a part through a single component.
b) Asset Twin
The pairing of two components makes an asset. Asset twins display the interoperability history between them. The data collected delivers actionable insights to enhance operational and maintenance KPIs, including mean time between failures and mean time to repair.
c) System Twin
This type of DT virtualizes the interactions between two or more assets joined together under a system. Engineers gain visibility and actionable insights for operational enhancements.
d) Process Twin
Process twins are macro DTs suited to disclose how systems work together. They screen the chain value of a process/workflow, revealing the transitions from raw materials to finished goods.
Another way to typify DTs is according to the data quality fed to models, which impacts the monitoring level capabilities.
a) Descriptive Twin
The descriptive twin is a visual representation built to disclose knowledge about the facility through its assets. The model’s data comes from live and editable versions grabbed from design and construction sets.
b) Informative Twin
Sensory and operational data are incorporated into the descriptive twin to build a more complete and robust model. The resulting product tightly connects to the physical asset, delivering high-quality data.
c) Predictive & Comprehensive Twin
Predictive analytics and actionable insights are available to engineers, empowering simulations of future scenarios and What-If sensibilities.
d) Autonomous Twin
The future of DT foresees an autonomous model that can make informed decisions based on previously learned behaviors.
Main Differences Between Digital Twins & Simulation
DTs and simulations are problem-solving tools relying on complex models to perform decisive calculations. Yet, they share pronounced differences when applied to an asset.
For starters, DTs operate on a large scale, becoming instant surrogates for multiple environments at once. Real-time data is taken from the asset (via IoT sensor devices) to unleash contextualization. Any changes happening to the physical asset instantly reflect on the digital replica. This event is called twinning.
Simulations are more limited. They focus on single environments and don’t interact with their physical counterpart.
The use of digital twin spreads across many industries, including:
- Oil and gas.
Check out two DTs examples,
Both were crafted by tech expert Sentient Computing.
Throughout the presentations, it becomes clear that each virtualization enabled better research capabilities, enhanced asset control, and widespread data accessibility.
What to Expect from a Digital Twin?
Implementing a DT will change the way teams interact with assets and each other! Expect optimized workflows, diligent communications, and prompt responses backed by:
- Early warnings about arising conditions
- Continuous predictive analytics and actionable insights
- Dynamic process optimization.
Moreover, DTs make it possible for whole organizations to access and manage data from a single source of truth. A great resource to cut error-prone procedures and save time!
Digital twin tech is ready to retool how we engineer and interact with our assets, shaking up our perceptions from the inside out!
As previously mentioned, this versatile software is key to expanding knowledge towards products, services, or processes. Always helping teams identify, streamline, and overcome existing limitations.
All in all, a fantastic program to rundown and analyze the happenings of an asset and boost its effectiveness. So, what’s to come in 2022?
VentureBeat did a great recap of this year’s trends, standing out:
- Cloud support expansion
- Neater interfaces
- Ecosystem growth
- Accelerated process intelligence
- Faster simulations
- Build Back Better tech support
- Enhancement of energy and structure modeling capabilities.
Top 10 Companies Leading the Digital Twin Market
At last, it’s vital to name the companies revolutionizing Industry 4.0 worldwide through DTs. Emergen Research did a fabulous recount on them.
Here’s the top 10, organized by revenue:
- Microsoft Corporation
- General Electric Company
- IBM Corporation
- Oracle Corporation
- Cisco Systems
- Dassault Systemes
- PTC Inc.
If you wish for more information or need consulting, please feel free to send me a message!