Information Package / Course Catalogue
Robotics and Artificial Intelligence Powered Digital Twins
Course Code: MME544
Course Type: Area Elective
Couse Group: Second Cycle (Master's Degree)
Education Language: English
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 8
Objectives of the Course

Applying digital twins to 'model-based design.' Model-based design will help students design and implement complex dynamic systems using virtual (digital) modelling technologies. At the end of this course, iteration designs through fast, repeatable tests will be possible for students to utilize. In addition, students will be able to automate the end-to-end lifecycle of your project by connecting virtual replicas of physical components in a digital space. Once systems are modelled as a twins, various existing and new engineering problems, such as predictive maintenance and anomaly detection, can be modeled and simulated.

Course Content

Implementing digital twins . Application of digital twins to manufacturing and construction problems Using the digital twin to design and implement use cases and services in the metaverse The course takes a case study approach in the form of motivating case studies where we apply digital twin perspectives to real-life problems. The course involves code walkthroughs but not hands-on coding.

Name of Lecturer(s)
Learning Outcomes
1.Students will learn the use of Machine learning and Deep Learning techniques (collectively referred to as artificial intelligence (AI)) in developing and deploying digital twins
2.Students will learn how to use simulation techniques with digital twins.
3.Students will learn modelling digital twins using augmented reality (AR), virtual reality (VR), and other strategies for complex problems.
4.Students will gain knowledge about responsible AI for digital twins
5.Students will learn simulation techniques for digital twins: agent-based modelling, systems dynamics, discrete event simulation
Recommended or Required Reading
1.A.Y.C. Nee, ? S.K. Ong, Digital Twins in Industry, 2021, MDPI AG
Weekly Detailed Course Contents
Week 1 - Theoretical
Definition of Digital Twin
Week 2 - Theoretical
The evolution of the Digital Twin
Week 3 - Theoretical
Timeline of the Digital Twin
Week 4 - Theoretical
Characteristics of the Digital Twin
Week 5 - Theoretical
Digital Twin types
Week 6 - Theoretical
Digital Twin and data scientist
Week 7 - Theoretical
Digital Twin in the company
Week 8 - Intermediate Exam
Digital Twin in the company, Midterm Exam
Week 9 - Theoretical
The Human Supervisor in the DT
Week 10 - Theoretical
Digital Twin architecture
Week 11 - Theoretical
The Digital Twin in the new technology network
Week 12 - Theoretical
Digital Twin in the new technological network- Data ownership
Week 13 - Theoretical
The Digital Twin in the new technological network – a case of cybersecurity
Week 14 - Theoretical
How Digital Twins simplify the IoT
Week 15 - Final Exam
Final Exam
Week 16 - Final Exam
Final Exam
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%30
Final Examination1%70
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory143498
Assignment70535
Individual Work73342
Midterm Examination19211
Final Examination112214
TOTAL WORKLOAD (hours)200
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
PÇ-12
OÇ-1
4
5
5
4
5
4
4
5
OÇ-2
3
4
4
4
4
4
4
4
OÇ-3
4
5
5
4
4
5
4
5
OÇ-4
3
3
3
3
4
4
4
4
OÇ-5
4
4
4
4
4
4
4
5
Adnan Menderes University - Information Package / Course Catalogue
2026