Machine vision (4 cr)
Code: KL25KONÄKÖ4-3014
General information
- Enrollment
- 22.04.2024 - 09.10.2024
- Registration for the implementation has ended.
- Timing
- 07.10.2024 - 15.12.2024
- Implementation has ended.
- Number of ECTS credits allocated
- 4 cr
- Local portion
- 2 cr
- Virtual portion
- 2 cr
- Mode of delivery
- Blended learning
- Unit
- SeAMK Automation Engineering and Information Technology
- Campus
- SeAMK Seinäjoki, Frami
- Teaching languages
- Finnish
- Degree programmes
- Bachelor of Engineering, Automation Engineering
- Teachers
- Toni Luomanmäki
- Course
- KL25KONÄKÖ4
Evaluation scale
1-5
Content scheduling
- Fundamental principles of the machine vision
- Applications of the machine vision
- Typical components of the machine vision system
- Basic functions of the Cognex InSight EasyBuilder machine vision software.
- Laboratory exercises
Objective
After completing the course the student will;
- Know the different machine vision technologies and knows where they are usually applied in industry
- Know the key components and their functions in the machine vision system
- Be able to use a machine vision software to create basic level image analysis and extract features from the image
Content
The basic principles of the machine vision, applications of the machine vision, typical components of the machine vision system, machine vision software, defining a machine vision system. The basic functions of the Cognex In-Sight EasyBuilder machine vision software. Basics of the machine vision laboratory equipment.
Materials
Lecture handouts, machine vision related manuals, articles, videos
Teaching methods
Lectures, interactive assignments, software exercises, laboratory assignments. Part of the course can be complete remotely.
Student workload
Total work load of the course: 95 h (scheduled lectures 35 h, independent work 60 h)
Assessment criteria, satisfactory (1)
The student knows the basics of the course contents.The student knows the principle of machine vision and knows where it can be applied.
Assessment criteria, good (3)
The student knows well the course contents.The student knows the principle of machine vision and knows where it can be applied. The student is able to use programs related to machine vision.
Assessment criteria, excellent (5)
The student knows excellent the course contents.The student knows the principle of machine vision and knows where it can be applied. The student is able to use programs related to machine vision. The student is able to design and implement vision-based applications that implement separation of parts and materials or quality control.