The document presents a technical seminar on digital twin technology, detailing its introduction, historical background, key characteristics, underlying technologies, architecture, applications, and advantages and limitations. Digital twins are virtual representations that enhance decision-making across a product's lifecycle, leveraging real-time data for predictive capabilities. The seminar emphasizes the growing importance and future market potential of digital twin technology, predicting substantial growth from $3.8 billion in 2019 to $35.8 billion by 2025.

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 into 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…