
| 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 |
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.
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.
| 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 |
| 1. | A.Y.C. Nee, ? S.K. Ong, Digital Twins in Industry, 2021, MDPI AG |
| Type of Assessment | Count | Percent |
|---|---|---|
| Midterm Examination | 1 | %30 |
| Final Examination | 1 | %70 |
| Activities | Count | Preparation | Time | Total Work Load (hours) |
|---|---|---|---|---|
| Lecture - Theory | 14 | 3 | 4 | 98 |
| Assignment | 7 | 0 | 5 | 35 |
| Individual Work | 7 | 3 | 3 | 42 |
| Midterm Examination | 1 | 9 | 2 | 11 |
| Final Examination | 1 | 12 | 2 | 14 |
| TOTAL WORKLOAD (hours) | 200 | |||
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 | ||||