Machine Vision in Production
Tampere University
Core content
· Machine vision in production automation: Typical applications
(2D/3D). Typical system structures. Commonly used 2D and 3D imaging
methods.
· Machine vision hardware:
· Different system types (PC based system, smart cameras)
· Camera types and selection principles: Specifying camera resolution (field-of-view, spatial resolution) and resulting expected measurement resolution.
· Lenses and other optical components: Specifying lens' focal length.
· Illumination in machine vision: Importance of illumination concerning the resulting image. Illumination methods and light sources.
· Machine vision software and image processing:
Digital image. Typical functionality and special properties of machine vision software.
Common programming concepts and methods in machine vision.
· Typical machine vision applications/tasks in production automation:
Checking the presence/counting parts – methods and algorithms
Locating parts for robot pickup - robot and machine vision calibration
Dimensional measurements - measurement accuracy and/or uncertainty
Complementary knowledge
· Typical color camera vs. grey-scale camera. Shutter types.
Concepts of depth-of-focus/depth-of-field and optical resolution.
· Effect of different illumination colors (wavelengths).
· Understanding basic operating principles of the most used machine vision software algorithms.
Programming simple machine vision application.
· Communicating with other equipment.
Calibrating machine vision system and combining camera and robot coordinates.
· Calculating measurement uncertainty.
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