Digital twin technology - seminar presentation

Describes how technology was developed and implemented

1. JSS ACADEMY OF TECHNICAL EDUCATION JSS Campus, Dr. Vishnuvardhan Road, Bangalore – 560060 DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING TECHNICAL SEMINAR 2023-2024 Digital Twin Technology Name: K S Spoorthi Usn:1JS20EC036 under the guidance of Dr.Saroja S Bhusare Dr.H S Aravind Associate Professor Associate Professor

2. Outline Indroduction Historical Background Literature Review Characteritics Tehnologies Architecture Applications Advantages and Limitations Conclusion

3. Indroduction A digital twin is a virtual representation of a physical object or system across its lifecycle, using real-time data to enable understanding, learning and reasoning. Digital twins are continuously updated and used throughout the product’s lifecycle-from design to manufacturing and construction, to operation and maintenance, and even for future use or reuse. Enhances decision-making through predictive capabilities .

4. Historical Background The concept of Digital Twin was First voiced by David Gelernter in 1991 and was called ‘Mirror Worlds’. NASA was one of the first organizations that used complex simulations of spacecrafts. The rise of the Internet of Things (IoT) and connected sensors in the 2010s revolutionized digital twin technology.

5. Features of DT

6. Literature Review SI NO TITLE YEAR OBSERVATIONS LIMITATIONS 1 Digital Twin Technology in Smart Grid, Transportation System and Smart City 2023 Different applications of DT in the development of the various aspects of energy management within a city including transportation systems, power grids, and microgrids Data analysis and data access,security , Standardization. 2 Empowering 6G Communication Systems With Digital Twin Technology 2022 This paper recognizes the importance of DT technology for the research & development of 6G communication systems Modularity and interfacing,digital twining of networks,network exposure management.

7. 3 Digital Twin in Aerospace Industry 2021 This paper unfolds the essential components of data acquisition and visualization. optimising massive data management (in terms of transferability, processing and analysis) to build high-fidelity aero-DTs for different vital aircraft systems (such as propulsion, landing gear, avionics, etc.). 4 Digital Twin for the Oil and Gas Industry 2020 From this review it was found integrity monitoring, project planning, and life cycle management are the key application areas of digital twin in the O&G industry cyber security, lack of standardization, and uncertainty in scope and focus are the key challenges of DT deployment in the O&G industry 5 The Digital Twin Revolution in Healthcare 2019 The Digital Twin of the patient is created as a result of transferring the patient's physical characteristics and changes in the body to the digital environment. Complexity and Scalability, Cost and Resource Constraints, User Acceptance and Adoption, Limited Understanding of Health Dynamics

8. Characteristics Connectivity : Real-time integration of data streams from physical assets or systems . Homogenization : Standardizing data formats and structures across digital twin representations. Reprogrammable and smart : Dynamic adaptation and intelligent decision-making capabilities within digital twin systems. Digital Traces : Recording and analyzing digital footprints for insights and optimization Modularity : Flexible design allowing components to be easily added or replaced .

9. Underlying Technologies

10. Internet of Things(IOT) IoT is based on  the collection of data acquired from real-world objects with the help of sensors. These data are  then used to create a digital duplicate of the physical object that can  be analyzed, manipulated, and optimized. Extended Reality ( xr ) It is the visualization technology which creates digital representations of objects. XR capabilities enable Digital Twins to digitally model physical objects, allowing users to interact with digital content .

11. Cloud computing Technology is used for efficient storing and accessing data over the Internet. As applications of Digital Twins operate with large volumes of data, cloud computing allows to store all data in the virtual cloud and easily access required information from any location . Artificial intelligence Is an advanced analytical tool that is able to automatically analyze obtained data and provide valuable insights. It can also make predications about possible outcomes and give suggestions as to how to avoid potential problems.

12. Architecture Diagram

13. Architecture The different stages in the architecture of Digital Twin service involves : Data and Data Collection Data Pipelines Data Integrity Data Egress

14. Data and data collection There is two types of data: Model data Used to construct digital representation of real world thing by using graph models. Time series Data Reprsent the observation of the state of some physical thing at a given time. Data pipelines Merge all the data source from data collection int o a single model and exported into element graph.

15. Data Integrity It also looks at the actual data stream for reducing issues with calibrations,connectivity , physical issues with the instrumentation that collect the physical data.This is a set of analysis on either single or multi v aried data. Data Egress The collected and organized data into digital twin is analyse to ensure the accuracy of that data.

16. DT In Manufacturing Methodology Create Communicate Aggregate Act Insights Analyze

17. Top use cases of digital twins in manufacturing Factory design and layout  – Optimize machine layouts, assembly flows, employee interactions, and more by spatially mapping factories.  Robotics Simulation - Build fundamentally safer systems by training robots in simulated environments. Operator training  – Increase the efficiency of knowledge transfer with immersive, interactive training applications that maximize safety and reduce costs. Monitoring, guided maintenance and repair  – Transform routine, time-consuming procedures into seamless processes with remote-enabled AR technologies.

18. IBM’S Digital Twin Open Industry Platform Cognitive Computing Dynamic Recalibration

19. Other Applicatons

20. Advantages Comparison of digital vs physical product Performance monitoring Improved productivity Increased reliability Performance tuning Customer support

21. Limitations Compatibility challenges Inconsistencies Handling data Security

22. Challenges Complexity of Integration Data Quality and Quantity Interoperability Security and Privacy Concerns Scalability

23. Conclusion Combined with the latest machine learning and artificial intelligence tools which helping companies across many industries reduce operational costs,increase productivity,improve performance,and change the way predictive maintenance is done.For product manufactures in particular,digital twin technology is crucial to achieving more efficient production lines and faster time-to-market .

24. Future scope The Digital Twin market is expected to grow from USD 3.8 Bn (2019) to USD 35.8 Bn by 2025, with CAGR = 37.8%

25. References https://ieeexplore.ieee.org/document/10351572/ https://ieeexplore.ieee.org/document/9540135/ https://ieeexplore.ieee.org/document/9899718/ https://ieeexplore.ieee.org/document/10099646/ https://vciba.springeropen.com/articles/10.1186/s42492-023-00137-4

26. THANK YOU …