From a business perspective, a digital twin duplicating every detail of the physical environment, process, or any other asset, has many benefits. Such a digital business solution facilitates performance tracking and continuous improvement, enables automation, and, in general, enhances predictivity.
To see how a process or system will work before investing in its development, create a digital twin solution (DT). This will allow you to plan for possible outcomes and shortcomings. Yet, to effectively apply digital twin technology, it is essential to understand its internal structure and know how it works.
In this blog, we briefly overview the digital twin architecture and talk about how to get started with digital twin product development.
Digital Twin Classification by Application
If you intend to use a digital twin model in the best possible way, you need to learn about the existing digital twin types. This will help you build an optimal digital twin simulation for your organization’s needs and extract the most value out of its usage.
As such, digital twins differ in application and hierarchy. Hierarchically, there are four categories of digital twins. The DTs may represent parts, systems, products, and processes.
We have already covered these categories in our digital twin development guide. Check it, if you want to get a solid understanding of this technology from the ground up.
For those of you, who need no introduction to digital twins, below is the categorization of DTs by application. Together with valuable data insights, this overview should help you to shape your digital twin requirements before you start the development.
Autonomy twins are capable of performing complex tasks such as image and object recognition. They are fully independent and can learn and act on behalf of users. Such twins have robust decentralized computing capabilities.
Their field of application includes manufacturing, where enterprise digital twins are used for various purposes. For example, to forecast the outcomes of process optimization, test and improve workflows, find the shortcomings of the system, and automate operations.
The digital twin simulation predicts the system’s future operational states and is an invaluable source of information and insights. Such twins are AI-based and are typically leveraged for predictive maintenance. For instance, a DT simulation of a residential building may help you predict which parts of it will age faster than others and need repairs. This may help the building owners to avoid accidents and schedule repairs.
You can use this digital twin for providing exhaustive data for decision-makers, such as tech support and reliability specialists. This DT includes control capabilities. The model is interactive, so the users may change its parameters. It allows them to see how a system will operate in different conditions.
For instance, a power plant system may adjust the amount of electricity produced to the current demand. Operational twins are also used in industrial manufacturing and are an essential part of smart city technology.
As a rule, enterprises use these twins in apps for basic status monitoring (alert systems, dashboards, etc). The purposes of such digital twin software are to monitor, capture, categorize and showcase operation parameters. They normally include visualization tools.
The use cases also include smart agriculture. DTs facilitate the process of monitoring the condition of soil and plants, air humidity, and the state of an environment.
Key Features and Capabilities of a Digital Twin
The next thing you should learn before you begin building a digital twin, are its key features and capabilities. The guide below will help you to understand the capabilities of digital twin software, and the key features and characteristics of the digital twin solution.
A digital copy of a physical device or a piece of equipment precisely replicates its properties and characteristics. Digital twins allow users to build models of physical objects in a virtual environment and track their behavior.
- 3D representation
A 3D representation is an exact visual copy created using all the parameters of a physical device. Such visualization creates a nearly real-life experience and allows you to easily grasp the data presented in analytic reports.
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The simulation software of a digital twin puts a virtual model of a physical device into a virtual environment. It also changes the environment’s parameters to track the device’s behavior in different conditions.
- Data model
A digital twin is capable of bringing users a comprehensive data model. This can help them to visualize the connections between the elements of a physical device or system. It permits you to label data and delivers a transparent view of operations and processes.
This feature allows users to view a graphic model on a screen of a standalone workstation or a mobile device. It gives us instant insight into details related to the functionality of a device or system.
- Model synchronization
This outstanding feature aligns the digital model with real-world parameters. Modern modeling software is capable of doing it in real-time. Model synchronization allows you to instantly view the representation of all the physical updates in a digital twin and make correct predictions and estimations.
- Connected analytics
This feature of digital twin software gives you access to analytics based on the measured properties of a physical device. A digital twin platform leverages ML algorithms and computations to bring insights into your device’s or system’s operations and behavior.
Surely, the final set of features as well as the resulting digital twin architecture will depend on your business needs and goals. However, the below overview of some key digital twin components will give you a basic understanding of how the digital twin infrastructure works.
The Main Components of a Digital Twin
The digital twin architecture is, essentially, both physical and digital. It includes three layers:
The hardware layer comprises the physical components of a digital twin such as routers, actuators IoT sensors, and edge servers.
The middleware layer is all about data governance, processing, integration, visualization, modeling, connectivity, and control.
The software layer consists of analytics engines, ML models, data dashboards, as well as modeling and simulation software.
The most important building blocks of digital twin layers deserve a detailed description.
The digital twin is all about monitoring, capturing, and processing the device’s or system’s data to deliver insights the decision-makers can act on. The Data Platform is one of the main digital twin components. It ensures secure data ingestion and processing, as well as steady performance, normalization, management, machine learning, AI analytics, microservices, and integration.
This module requires robust capacities for data storage and a cloud-based ML platform for analytics.
Tech Stack: AWS, Microsoft Azure, Amazon Dynamo BD.
A vital component of a digital twin platform. This model translates the status data, analytics insights, and forecasts into formats suitable for human perception. As a result, we get a connected environment of the virtual copy with the physical world.
The Visualization module is responsible for delivering data insights to end users, simulation, and intelligent operations. The digital twin’s dashboards and commands rely on this module for correct functionality.
Tech Stack: Unity, WebGL, three.js, PlayCanvas.
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Workflow, and APIs
This module serves to pull and share data from different sources for building a DT solution. It is also responsible for changing the digital twin parameters and synchronizing the copy with its prototype in the physical world.
It is essentially all about workflows, operations, processes, and event-based flows.
Tech Stack: AWS API Gateway, Node JS/ SpringBoot.
Governance & Operations
Important elements of the data platform architecture. The digital twin is, essentially, a data powerhouse. So the Governance & Operations module is necessary to ensure that data is structured and available on demand.
This module ensures proper data governance and its capability to deliver value.
Tech Stack: AWS Glue.
The digital twin infrastructure is complex and combines cloud and on-prem elements. Hybrid infrastructure is necessary to support continuous integration and delivery. Furthermore, it provides the capability to create, train, and bring into action machine learning modules and ensure efficiency.
Technologies used: cloud and edge computing.
Custom digital twin development is a complex process requiring robust technological capabilities, diverse skill sets, and flexibility. Because a digital twin implementation requires the integration of IoT, machine learning, robotics, VR, and advanced 3D visualizations, the development process involves scrupulous planning.
How to Approach Digital Twin Implementation?
How to create a digital twin? All in all, the stages of digital twin building follow the same logic as other products and services. Because of its multifaceted and complex nature, though, each step of the digital twin product development involves thorough preparation.
#1. Assess Process Opportunities for Your Business
At this stage, you should define the scope and scale of your DT solution. The list of questions to ask yourself at this point includes:
- Will you be replicating an entire system or device or just part of it?
- Who will be running the digital twin: your employees or end users?
- Will users be accessing it remotely or on-site?
- Which functions do you want to replicate?
At the end of this stage, you should have a precise vision of the functionality you want to perform and the goals you want to accomplish using a digital twin platform. Finally, when you have the complete feature set and specification ready, you may move on from the ideation stage to digital twin development.
#2. Create an MVP Digital Twin Solution
The set of features from the previous stage will dictate your project’s tech stack and architecture. Before you build a minimal viable product (MVP) version of your digital twin, decide which features are absolute must-haves and which you will be adding at later stages.
#3. Stabilize the MVP and Mature
Building an MVP version of your digital twin solution will help you test your product and implement changes. This will give you an understanding of what features or processes you need to add or exclude from your final product version. Allow your MVP to stabilize and mature before you proceed with the digital twin building.
#4. Add Additional Areas and Processes
When it comes to making changes and improvements, nothing works better than a real-life test drive. The MVP product stage should have helped you adjust your product requirements and decide which processes you want to replicate in the final version of your digital twin.
#5. Monitor Progress and Return on Investment
Once your digital twin platform is up and running, you’re not done yet. Digital twins are dynamic replicas of real-world assets capable of changing and developing in line with the current business needs. Tracking your progress and ROI, as well as continuous support and maintenance will ensure that the digital product you’ve built truly lives up to your expectations and delivers value.
Creating a digital twin is a complex process requiring knowledge and expertise in a wide range of technologies. Finding reliable digital twin providers competent in many fields from IoT and machine learning to AR/VR may be challenging in this respect. It is always advisable to look for companies with proven experience in DT building who can provide you with relevant digital twin case studies and examples.
At Visartech we have knowledge and expertise in every area required for successful digital twin development. Our product portfolio features a Business Process Simulation Platform, a VR-based solution providing real-time visualizations helping decision-makers estimate the resource intensity of company processes. We also have vast experience in building virtual tour apps, virtual labs, AR-driven inspection apps, simulations, virtual meeting rooms, etc.