Course detail
Experimental Methods in Tribology
FSI-9EXT Acad. year: 2024/2025 Both semester
Supervisor
Department
Learning outcomes of the course unit
Prerequisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Conditions for gaining the exam:
- submission and defense of individual work on measurement or experimental analysis of selected tribological problem. The work would include theoretical description, error and measurement quality analysis and design of results evaluation.
Absence from lessons may be compensated for according to instructions of the teacher.
Language of instruction
Czech
Aims
The main aim is to provide basic knowledge of the experimental methods, theory of measurement and experiments in the field of tribology with respect to the topic of PhD thesis.
- The ability to identify the key problems for experimental validation of tribological problems.
- The ability to select proper experimental methods with respect to the specific problems in the field of tribology.
- The ability to design experiments and assess quality of measurements.
- The ability to statistically evaluate results.
Specification of controlled education, way of implementation and compensation for absences
The study programmes with the given course
Programme D-KPI-K: Design and Process Engineering, Doctoral
branch ---: no specialisation, 0 credits, recommended course
Programme D-KPI-P: Design and Process Engineering, Doctoral
branch ---: no specialisation, 0 credits, recommended course
Type of course unit
Lecture
20 hours, compulsory
Syllabus
- Samples and Characterization of Test Specimens. Lubricant and Process Fluid and Solids Analysis.
- Sample Preparation. Control of the Test Environment.
- Surface Topography Measurement.
- Tribometers. Controlling of Load, Measurement of Friction and Wear.
- Optical Methods for Analysis of Tribological Processes.
- Wear Analysis, Surface and Subsurface Micrography, Chemical Analysis.
- Design of Experiment.
- Statistical Analysis of Data.
- Measurement Errors, Noise, Precision and Accuracy.