Examination arrangement

Course content

Course content

- The concept of modelling and virtual representations of physical worlds

- Understanding of the concept of Digital Twin for manufacturing

- Measurement and data generation

- Predictive maintenance with Digital Twin

- Tools for data exploration, analysis and visualisation

- Characteristics of the physical system to implement digital twin

- Enablers for implementation of Cyber-Physical systems in manufacturing

- Use of digital twin for implementation of sustainable manufacturing principles

- Introduction to Digital communication and Internet of Things and its relation to digital twins

- Tutorials on Learning Factory Cyber-Physical System

Learning outcome

After completing the course, the student is supposed to:

Knowledge:

- General understanding of digital twin concept and its applications in manufacturing

- Advanced knowledge of analytical tools in the context of sustainable manufacturing

- Knowledge of the implementation processes for Digital twin system

- Understanding of the structure of Cyber-Physical systems in sustainable Manufacturing

Skills:

- Training on digital twin software CIRUS.

- Using of digital twin for predictive maintenance and other aspects of manufacturing systems

- Ability to measure and create sustainable development indicators in the context of Cyber-Physical systems

- Ability to think critically when interpreting simulation data from the digital twin

General competence:

- Be able to convert data into information that gives value in decision-making in the manufacturing process and management

- Have a holistic understanding of the life cycle of data.

- Have a critical and analytical approach to sustainability assessment and decision making for sustainable development solutions in Cyber-Physical systems.

Learning methods and activities

Teaching Methods: •

- Seminars in the from of Flipped classroom

- Group work •

- Laboratory exercises in the Learning Factory

The seminars in the course will be given on campus. Videos that sum up the main issues in the lecture will also be available on internet through the learning management system.

Tutoring is given at campus and online in accordance to announced times.

Further on evaluation

- The course will be graded based on a term paper written by each individual student. - Re-sit: Whole course must be re-taken.

Recommended previous knowledge

Basic Knowledge on probability and statistics Basic knowledge on mathematics

Required previous knowledge

Basic Knowledge on manufacturing processes

Course materials

- Scientific articles, hand-outs and online resources/materials provided by lecturers.

- Industry cases

- Some material must be read before attending the course