Production automation (5 cr)
Code: 8I00CG72-3004
General information
Enrollment
17.04.2023 - 06.09.2023
Timing
28.08.2023 - 17.12.2023
Credits
5 op
Teaching languages
- Finnish
Degree programmes
- Master's Degree Programme in Automation Engineering
Teachers
- Jarkko Pakkanen
- Juha Hirvonen
Student groups
-
YAUTE23Master of Engineering, Automation Engineering
Objective
Student knows the principles of machine vision systems and can apply machine vision technology in industrial automation. Student can develop a production cell, which utilizes industrial robots and machine vision system. Student can utilize mobile and collaboration robotics in industrial automation.
Content
- Machine vision systems in manufacturing industry
- Robotics and its applications
- Robot simulation and offline programming
- Robot programming
- Collaborative robotics
- Mobile robotics
Materials
Will be distributed during the course.
Teaching methods
Five independent exercise works that are related to robotics, machine vision and collaboration between them.
Contact lessons that support the exercise works.
Student workload
Contact sessions: 28h
Independent studying
Evaluation scale
1-5
Assessment criteria, satisfactory (1)
Student knows the basics of machine vision systems. Student knows how the industrial robots are used in industrial automation.
Assessment criteria, good (3)
Student knows the principles of machine systems and can apply machine vision technology in industrial automation. Student can develop a production cell, which utilizes industrial robots and machine vision system. Student can utilize mobile and collaboration robotics in industrial automation.
Assessment criteria, excellent (5)
Student have a good understanding of machine vision technology. Student can develop a production cell, which utilizes industrial robots and machine vision system. Student can utilize mobile and collaborative robotics in industrial automation.
Assessment methods and criteria
The accepted laboratory works are the basis of assessment. The assessment criteria are
- the extent of the work
- the rationality of the solution
- the neatness of the implementation
- reporting
Assessment criteria, satisfactory (1)
The student has not completed the laboratory works.
Assessment criteria, good (3)
The completed works are narrow and their implementation and reporting is a bit unclear.
Assessment criteria, excellent (5)
The completed works fulfill the requirements, their implementation is reasonable and the reporting is clear.
Assessment criteria, approved/failed
The completed works are highly extensive, their implementation is smooth and smart and the reporting is really clear and illustrative.