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We're here to help you find itThe "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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Classroom Training price is on request
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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:
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.
This course offers comprehensive training in Digital Twins technology, integrating AR, VR, and MR for advanced model-based design.
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.
Playback in technology refers to the process of reproducing audio or video files that have been previously recorded or stored. It involves the decoding and rendering of digital files to present content through various devices like televisions, computers, or smartphones. Key components of playback technology include the media player software, which interprets the data, and the hardware (speakers and screens) that output the sound and visuals. This technology is essential in entertainment, communication, and education, enabling users to access multimedia content conveniently and efficiently, often with options to pause, rewind, or fast-forward.
Operations optimization is the process of enhancing an organization's operations to improve efficiency and effectiveness. It involves analyzing workflows, resource utilization, and output to identify areas for improvement. Techniques may include streamlining processes, reducing waste, and optimizing supply chain management, among others. By using digital tools such as digital twins—a virtual model of a process, product, or service—companies can simulate and predict outcomes to further refine operations, minimize costs, and maximize value, creating a smarter, more responsive operation.
Control system design involves creating systems that manage, command, direct, or regulate the behavior of other devices or systems using control loops. It's fundamental in engineering and technology, where precise adjustments to a system’s components lead to desired performance. This process is critical in ensuring that machines, processes, and technology operate efficiently, safely, and predictably. Techniques include modeling system behaviors, designing controllers using these models, and implementing these controllers in real-world applications. This ensures systems react appropriately to both internal and external changes, maintaining desired output despite disturbances.
Predictive maintenance refers to the use of data analysis tools and techniques to detect anomalies in equipment and predict when it might fail. This method uses historical data, machine learning, and real-time insights to forecast equipment malfunctions before they occur, thereby reducing downtime and maintenance costs. The goal is to perform maintenance at the precise moment it is needed, rather than on a fixed schedule, optimizing the lifespan and efficiency of machinery in industries like manufacturing and transportation.
MATLAB is a programming platform designed for engineers and scientists to analyze and design systems and products that transform our world. It's used for numerical computation, visualization, and programming in an easy-to-use environment. Simulink, on the other hand, is a block diagram environment for Model-Based Design. It supports simulation, automatic code generation, and continuous testing of embedded systems. Together, MATLAB and Simulink facilitate tasks like developing complex algorithms, analyzing large data sets, and creating simulations — vital in engineering and scientific applications.
Digital twin technology involves creating a virtual replica of a physical object, process, or system. This digital model simulates the real-world entity in real-time, allowing for analysis, troubleshooting, and testing without impacting the actual object. Businesses use digital twins to optimize operations, predict maintenance needs, and enhance product design. This technology is pivotal in fields like manufacturing, automotive, and smart cities, improving safety, efficiency, and innovation.
Augmented Reality (AR) is a technology that overlays digital information, such as images, videos, or text, onto the real world using devices like smartphones or AR glasses. This enhances the way we perceive our surroundings by integrating virtual components with the physical environment in real-time. This technology is utilized in various applications, from gaming and entertainment to education and training, helping users interact with digital information in a more engaging way.
Virtual Reality (VR) is a technology that creates a simulated environment, distinct from the real world. Users wear headsets that display 3D experiences, allowing them to interact with this artificial world in real-time. VR is utilized in various fields like gaming, training, education, and therapy. It immerses users completely in an interactive and engaging digital experience, making complex concepts more accessible and enjoyable. Through VR, professionals can visualize and manipulate virtual objects, enhancing learning and understanding in industries ranging from healthcare to engineering.
Mixed Reality (MR) is a technology that blends real-world and digital elements together. In MR, users see and interact with both physical and virtual environments and objects at the same time. This creates immersive experiences that can be used for gaming, education, training, and more. Mixed Reality uses a mix of hardware like headsets or glasses and software to create a seamless overlay of digital content onto the real world, enhancing your perception and interaction with your surroundings.
Surrogate models are simplified representations used in complex simulations to approximate more detailed and resource-intensive models. They serve as stand-ins to speed up calculations where high fidelity models would be too slow or expensive to use. Surrogate models are essential in engineering and design, helping professionals predict system behaviors under various scenarios efficiently. This concept is particularly pertinent in areas like optimization, real-time simulation, and the development of digital twins, where surrogate models can significantly enhance the accuracy and efficiency of digital representations in various courses and applications.
Data analysis involves examining, cleansing, transforming, and modeling data to discover useful information, make conclusions, and support decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains. It helps organizations to optimize their performances by identifying various facts and trends that can inform strategic business decisions. Techniques and tools vary but often include statistical analysis and visualization methods to summarize data history or predict future occurrences.
Simulation interpretation involves analyzing the results of a simulation to understand the performance and behavior of a modeled system under various conditions. It is particularly essential in fields like engineering, where simulations help predict the impacts of design choices, environmental factors, and other variables. This process aids decision-making by providing insights into potential outcomes, allowing for improvements before actual implementation. Understanding how different parameters affect a simulated environment contributes to more effective and efficient real-world applications and problem-solving.
Simscape is a MATLAB tool used for modeling and simulating physical systems. It allows users to create accurate models of systems, such as mechanical, electrical, hydraulic, and other physical domains, by using physical connections rather than traditional code. This enables a more intuitive development of systems that replicate real-world physics, which is essential for testing and designing prototypes before physical production. Simscape can help engineers visualize and optimize system performance, supporting effective decision-making in engineering projects.
Component modeling is a process in system engineering where various components of a software or hardware system are represented in models to analyze and predict the performance and interaction of different parts within a system. This technique is crucial for understanding complex systems by breaking them down into manageable parts, which can then be individually examined and optimized. This approach helps in improving system reliability, efficiency, and effectiveness, by allowing developers and engineers to foresee potential issues and make informed decisions during the design and implementation stages.
Fault simulation is a technique used in testing electronic systems to predict and analyze potential faults or errors in circuit designs. By simulating different error conditions, engineers can assess how a circuit would behave under various fault scenarios, helping to ensure the reliability and robustness of electronic components before they are built. This is crucial in developing systems that are fault-tolerant, which means they can continue to operate correctly even when parts have failed. This simulation process helps in significantly reducing design errors, thus saving time and cost in the product development cycle.
Model Based Systems Engineering (MBSE) is a method used to design and manage complex systems through models rather than traditional documents. It utilizes various digital tools to create and explore these models, improving understanding and communication. This approach allows teams to visualize system interconnections and behaviors effectively, enhancing analysis and decision-making processes. MBSE helps in identifying potential issues early, simplifies integration, and supports consistent updates, ensuring systems are efficient, scalable, and aligned with user needs throughout their life cycle.
Data-driven modeling is a method where data is at the heart of building models. This approach uses data to understand patterns, make predictions, or drive decisions without relying strongly on pre-established theories or models. Instead, algorithms learn from data, adapting and improving their predictions over time. Data-driven models are crucial in fields like finance, marketing, and healthcare, where large amounts of data can be analyzed to uncover insights or predict trends. Essentially, this modeling approach helps organizations convert raw data into actionable insights, enhancing decision-making processes and operational efficiency.
Machine learning is a subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed. It involves algorithms that parse data, learn from that data, and then apply what they've learned to make informed decisions. Essentially, it’s about building programs that can access data and use it to teach themselves to perform tasks by identifying patterns in the data. This process automates analytical model building and can be used in a wide range of applications such as prediction, classification, and recommendation systems.
Co-simulation is a technique where two or more simulation programs operate simultaneously to study complex systems. This method allows different systems, modeled individually, to interact and influence each other's behavior. It's particularly useful in the design and testing of complex engineering solutions, improving predictions by integrating different physical and technical interactions. The approach is increasingly prominent in sectors like automotive, aerospace, and electronics, where interactions between various subsystems are crucial for overall performance. Co-simulation enables more accurate simulations, leading to better designs and more efficient system integration.
Workflows refer to the sequence of processes through which a task goes from initiation to completion in a business environment. These processes involve the automated or manual activities necessary to complete a job, including the various tasks, procedural steps, people involved, and necessary systems or tools. Workflows help streamline and optimize routine processes, ensuring consistency, efficiency, and accountability in task management. By organizing and automating specific sequences, businesses can reduce errors, increase productivity, and effectively monitor performance throughout task execution.
ROS Toolbox is a tool in MATLAB and Simulink that allows you to design, simulate, and test robotics applications. It simplifies interfacing with Robot Operating System (ROS), a platform used for developing robot software. By using ROS Toolbox, you can directly access ROS messages, communicate with ROS network, visualize sensor data and robot behavior, and even generate standalone ROS nodes. This integration helps in efficiently building and testing robotics algorithms, minimizing the transition from simulation to real-world application, pivotal in designing advanced robotic systems.
This course offers comprehensive training in Digital Twins technology, integrating AR, VR, and MR for advanced model-based design.
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.
