Konenäkö (5 cr)
Code: TSAAA320-0K0H3
General information
- Timing
-
01.01.2020 - 31.07.2020
Implementation has ended.
- Number of ECTS credits allocated
- 5 cr
- Local portion
- 5 cr
- Mode of delivery
- Face-to-face
- Unit
- School of Technology
- Teaching languages
- Finnish
- Degree programmes
- Bachelor's Degree Programme in Electrical and Automation Engineering
Evaluation scale
0-5
Objective
The student knows and is able to use the image acquisition, preprocessing and image analysis methods for grey scale and color cameras. He/she knows the properties of machine vision components (cameras, image processing components, light sources, optics, interfaces). The student understands the functions of a machine vision system, its limitations and opportunities and is able to do the hardware design and algorithm programming according to the requirements of the end user.
EA-EN:Engineering analysis
The student is able to analyze the production or quality control process and choose the suitable machine vision solution.
EA-EE:Engineering design
The student is able to select and engineer the machine vision components, system as a whole and do the programming of machine vision algorithms.
EA-ER:Engineering practice
The student is able to select the suitable machine vision components to each application and take into account the needs of installation environment .
Content
The content areas are camera and light source technology, optical engineering, camera and light source selections, image acquisition, image processing, machine vision system design, programming of machine vision application and algorithms, interface to automation systems, robots or manipulators. The installation requirements and environmental shielding is also covered in the course content.
Materials
The teacher’s own materials. Materials of machine vision suppliers.
Completion alternatives
Exercises during lessons 10% Laboratory exercises 40% Machine vision exercises 50%
Student workload
-lessons 24 h -laboratory exercises 40 h -machine vision exercises 45 h -group exercises 26 h Total135 h
Assessment criteria, satisfactory (1)
Sufficient (1):The student’s hardware and software design of machine vision system and installation is of poor quality and contains deficiencies.
Satisfactory (2):The student is mainly able to do the hardware and software design of machine vision system and installation according to the requirements of the application. The student is able to design and implement partly the machine vision application concerning component selections, programming and installation but the results are incomplete.
Assessment criteria, good (3)
Good (3):The student can solve of the hardware and software design of machine vision system and installation needs according to the requirements of the application. The student is able to design and implement the machine vision applications using feasible and working solutions concerning component selections, programming and installation.
Very good (4):The student shows mastering of the hardware and software design of machine vision system and installation aspects according to the requirements of the application. The student is able to design and implement the machine vision applications using very good solutions concerning component selections, programming and installation.
Assessment criteria, excellent (5)
Excellent (5): The student masters the hardware and software design of machine vision system and installation aspects according to the requirements of the application. The student is able to design and implement the machine vision applications in optimal way concerning component selections, programming and installation.
Qualifications
Basic studies of automation engineering including LabView – graphic programming tool