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Course unit, curriculum year 2023–2024
KONE.532

Virtual Commissioning of Robot Systems, 5 cr

Tampere University
Teaching periods
Active in period 3 (1.1.2024–3.3.2024)
Active in period 4 (4.3.2024–31.5.2024)
Course code
KONE.532
Language of instruction
English
Academic years
2022–2023, 2023–2024
Level of study
Advanced studies
Grading scale
General scale, 0-5
Persons responsible
Responsible teacher:
Minna Lanz
Responsible teacher:
Jyrki Latokartano
Responsible organisation
Faculty of Engineering and Natural Sciences 100 %
Coordinating organisation
Mechanical Engineering Studies 100 %
Core content
  • To learn to design effective robot workcells to perform specified task based on the knowledge gained in prerequisite courses.
  • Student will become acquainted with modern 3-D simulation tools and learns to use them effectively in robot cell design and simulation.
  • Workcell functionality is demonstrated and tested by comprehensive robot programming to perform complete workcell sequence and to simulate selected processes.
  • Groups get familiar with concurrent engineering in real life industrial development assignment.
Complementary knowledge
  • To be able to perform comparison between different workcell alternatives. Requires deeper understanding in performance values of different robot types and sizes.
  • Simulation tools are used to verify workcell performance by using different settings and basig data.
  • Workcell performance is increased by optimisation of robot programs based on simulation data and process analysis.
  • Students learn project management while interacting with customers and within own project group.
Specialist knowledge
  • To be able to perform measures and analysis in workcell performance and to optimize it.

  • When operator presence is required, human manikin simulation can also be implemented.
  • In some assignments robot programs can also be downloaded to real life workcell. In this case assignment includes also calibration of the workcell model.
  • Project management platforms can be used to optimize resource allocation and schedueling.
Learning outcomes
Prerequisites
Further information
Studies that include this course
Completion option 1
Students need to complete a simulation and off-line programming project and write a problems solved log of it.

Participation in teaching

08.01.2024 29.04.2024
Active in period 3 (1.1.2024–3.3.2024)
Active in period 4 (4.3.2024–31.5.2024)