Digital Twins: Enhancing Model-based Design with AR, VR and MR Course Overview

Digital Twins: Enhancing Model-based Design with AR, VR and MR Course Overview

The "Digital Twins: Enhancing Model-based Design with AR, VR and MR" course is a comprehensive program designed to educate learners on the integration of digital twin technology with advanced visualization techniques like Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR). Through this course, participants will delve into the application of digital twins in operations optimization, control system design, predictive maintenance, and deployment within production systems. They will gain hands-on experience with MATLAB & Simulink, exploring data analysis and simulation interpretation, and they will understand different simulation approaches, including the creation of synthetic data.

Learners will also be introduced to Simscape for component modeling and fault simulation, and will revisit the role of digital twins in Model Based Systems Engineering (MBSE). The course includes a focus on data-driven modeling using machine learning to create surrogate models, and it demonstrates how digital twins can be enhanced with AI for tasks like detection. A significant highlight of the course is the integration of digital twins with VR-AR-MR, where learners will explore playback, co-simulation, and workflows using tools like the ROS Toolbox. By completing this digital twins course, participants will be equipped with cutting-edge skills to improve model-based design practices.

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  • Live Online Training (Duration : 24 Hours)
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Classroom Training price is on request

  • Live Online Training (Duration : 24 Hours)
  • Per Participant

♱ Excluding VAT/GST

Classroom Training price is on request

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Course Prerequisites

To ensure that participants can successfully engage with and benefit from the Digital Twins: Enhancing Model-based Design with AR, VR, and MR course, the following minimum prerequisites are recommended:


  • Basic understanding of engineering concepts and terminology, particularly in the context of system design and operation.
  • Familiarity with the basic principles of computer science and programming. Prior experience with any programming language (e.g., Python, C/C++, or Java) can be beneficial.
  • Knowledge of mathematical concepts, including algebra and basic calculus, to comfortably follow simulation and data analysis lessons.
  • Exposure to MATLAB and Simulink is advantageous but not strictly necessary, as an introduction to these tools is provided in the course.
  • An interest in learning about advanced technologies such as digital twins, artificial intelligence, machine learning, and immersive technologies (AR, VR, MR).

These prerequisites are intended to help participants fully engage with the course material and maximize the learning outcomes. The course is designed to accommodate learners with diverse backgrounds, and additional support will be provided to ensure that all participants can follow the curriculum effectively.


Target Audience for Digital Twins: Enhancing Model-based Design with AR, VR and MR

  1. This course offers comprehensive training in Digital Twins technology, integrating AR, VR, and MR for advanced model-based design.


  • Engineers in mechatronics, mechanical, electrical, and control systems
  • Systems and design engineers exploring model-based engineering (MBE)
  • Data scientists and analysts working on predictive maintenance and operations optimization
  • IT professionals specializing in cloud deployment for production systems
  • MATLAB and Simulink users seeking advanced simulation techniques
  • Professionals in manufacturing looking to implement digital twins for process improvement
  • R&D engineers focusing on product lifecycle management and innovation
  • AI specialists developing machine learning models for digital twins
  • Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) developers creating immersive simulations
  • Robotics engineers utilizing ROS for integration with digital twins
  • Academics and researchers studying model-based systems engineering (MBSE)
  • Technical managers overseeing digital transformation projects
  • Quality assurance engineers interested in virtual testing and fault simulation
  • Software developers in the field of simulation and digital twinning technology
  • Product managers and strategists planning digital twin implementation


Learning Objectives - What you will Learn in this Digital Twins: Enhancing Model-based Design with AR, VR and MR?

Introduction to Course Learning Outcomes:

Explore the integration of Digital Twins with AR, VR, and MR to optimize operations, design control systems, perform predictive maintenance, and enhance model-based design through hands-on simulations and data-driven approaches.

Learning Objectives and Outcomes:

  • Understand the application of digital twins in operations optimization, control system design, and predictive maintenance.
  • Learn about cloud deployment and its role in production systems for digital twins.
  • Gain proficiency in MATLAB & Simulink for analyzing data and interpreting results for digital twin models.
  • Comprehend various simulation approaches including first-principle, componentized, and data-driven simulations.
  • Develop skills to generate synthetic data from simulations for training and analysis purposes.
  • Acquire the ability to create and simulate component models using Simscape, including simulating electrical faults in motors.
  • Recap the role of digital twins in Model-Based Systems Engineering (MBSE).
  • Learn to fit machine learning models to create surrogate models for digital twins and understand the fundamentals of data-driven modeling.
  • Apply machine learning models to digital twins for fault detection and other predictive capabilities.
  • Explore the integration of digital twins with AR, VR, and MR including playback, co-simulation, and workflows using ROS Toolbox.