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

Course Prerequisites

• Basic Computer Knowledge.

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.