Course unit, curriculum year 2023–2024
KONE.532
Virtual Commissioning of Robot Systems, 5 cr
Tampere University
- Description
- Completion options
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.532Language of instruction
EnglishAcademic years
2022–2023, 2023–2024Level of study
Advanced studiesGrading scale
General scale, 0-5Persons responsible
Responsible teacher:
Minna LanzResponsible teacher:
Jyrki LatokartanoResponsible 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)