Length: 2 Days
Print Friendly, PDF & Email

Fundamentals of Digital Twins

There’s likely digital twins in your future.

That’s because more and more organizations are turning to digital twins for all the benefits this technology offers.

A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making.

Organizations from healthcare and facilities management to product design have turned to digital twin technology. Digital twins have been particularly useful in the manufacturing sector, where comparing products and their manufacturing conditions can help identify why some batches have more variability, defects, or inefficiencies.

In general, other digital twin benefits include:

  • Accelerated risk assessment and production time
  • Predictive maintenance
  • Real-time remote monitoring
  • Better team collaboration
  • Better financial decision-making

Building and scaling a digital twin requires a three-step approach: creating a blueprint, building the initial digital twin, and then boosting its capabilities.

Many experts in this field believe that the first step, creating the blueprint, is the most crucial stage in building a digital twin. This includes aligning stakeholders on a clear vision of a digital twin.

A blueprint should define the types of twins the organization will pursue, the order for building them to maximize value and reusability, the way their capabilities will evolve, and their ownership and governance structures.

Analysts agree that forward-thinking companies across industries are implementing digital twins in increasingly fascinating and ground-breaking ways.

With the help of a digital twin, companies can test and validate a product before it even exists in the real world. By creating a replica of the planned production process, a digital twin enables engineers to identify any process failures before the product goes into production.

Digital twins are a constant exchange of data between the real and the virtual. This is different from a simulation which only takes information from the physical one time without ever really having a back and forth.

While it’s common for digital twins to utilize a 3D visual representation of some kind, this is not always the case. What is essential is the data. Consequently, some organizations prefer to visualize data through a 2D or 3D model.

According to one study, digital twins are expected to become a standard feature of IoT applications by 2025, with 88% of platforms containing at least some functionality.

Fundamentals of Digital Twins Course by Tonex

Fundamentals of Digital Twins covers the key principles of Digital Twins and how it relates to integration of digital engineering, modeling and simulations, AI/ML, 3D and integration for service and product-related data and systems. The concept of digital twins is a response to the increasing digitalization of service and product development, production, and digital products worldwide.

Digital twins are virtual replicas of physical systems, devices, services, assets, or processes used to run real time simulations. Digital twins are designed to analyze events, if scenarios, detect and prevent operational and production issues, predict performance, and optimize processes through real-time analytics to deliver and optimize business value.

A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making.

digital twin

Program Outline

Digital Twin 101

  • Key concepts behind digital engineering and digital twin
  • History of digital twin technology
  • How does a digital twin work?
  • Virtual representation of an object or system that spans its lifecycle
  • Digital twin use cases and applications
  • Advantages and benefits of digital twins
  • Key enabling technologies
  • Systems engineering and System of Systems Engineering (SosE)
  • Key concepts behind Model-based Systems Engineering (MBSE) and digital engineering
  • Modeling and simulation 101
  • Overview of UML, SysML, DoDAF and UAF
  • Data science and data analytics 101
  • AI/ML 101
  • Digital twins vs. simulations
  • Integration of 5G, VR/AR and 3D printing

Types of Digital Twins

  • Capability twin
  • System of Systems (SoS) twin
  • System or Unit twins
  • Subsystem twins
  • Component twins/Parts twins
  • Asset twins
  • Process twins
  • Performance measures
  • Master, the shadow and the twin
  • Related digital twin solutions

Digital Twin Market and Industries

  • Defense
  • Aerospace
  • Space
  • Engineering (systems)
  • Automobile manufacturing
  • Aircraft production
  • Railcar design
  • Building construction
  • Manufacturing
  • Power utilities
  • The future of digital twin

Practical Applications

  • Case studies
  • Workshops
  • Build your own digital twin using Tonex framework

Fundamentals of Digital Twins

Request More Information

Please enter contact information followed by your questions, comments and/or request(s):
  • Please complete the following form and a Tonex Training Specialist will contact you as soon as is possible.

    * Indicates required fields

  • This field is for validation purposes and should be left unchanged.

Request More Information

  • Please complete the following form and a Tonex Training Specialist will contact you as soon as is possible.

    * Indicates required fields

  • This field is for validation purposes and should be left unchanged.