Production automation (5 cr)
Code: 8I00CG72-3005
General information
- Enrollment
- 22.04.2025 - 03.09.2025
- Registration for the implementation has begun.
- Timing
- 29.08.2025 - 31.12.2025
- The implementation has not yet started.
- Number of ECTS credits allocated
- 5 cr
- Local portion
- 5 cr
- Mode of delivery
- Contact learning
- Unit
- SeAMK Master School
- Campus
- SeAMK Seinäjoki, Frami
- Teaching languages
- Finnish
- Degree programmes
- Master's Degree Programme in Automation Engineering
- Teachers
- Jarkko Pakkanen
- Juha Hirvonen
- Scheduling groups
- Avoin AMK (Ei koske tutkinto-opiskelijaa) (Size: 10 . Open UAS : 10.)
- Groups
-
YAUTE25Master of Engineering, Automation Engineering
- Small groups
- Open UAS (Doesn't apply to degree student)
- Course
- 8I00CG72
Evaluation scale
1-5
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
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.