Design and Process Engineering
· Designing, construction, calculation, technology of manufacturing, technical preparation of manufacturing including assembly and testing,
· Thermal and nuclear power plant devices such as steam and combustion turbines, steam generators, steam power plants and heating plants including nuclear power stations, industrial power engineering and their environmental aspects,
· Water turbines, hydrodynamic and hydrostatic pumps, piping systems, hydroelectric power plants, and pumping stations,
· Machinary and devices for chemical industry, food-stuff industry, and biotechnological treatment lines,
· Construction, modelling and theoretical studies of machines and devices for cutting, forming machines, industrial robots, and manipulators,
· Machine parts and mechanisms, methodology of designing machine elements and working mechanisms of general application with consideration of stochastic qualities of inputs, including the application of special types of machines and devices,
· Cars, vans and lorries, buses, trailers, semi-trailers, and motorcycles,
· Combustion engines for all types of vehicle drives, simulation of combustion engine thermomechanical systems, dynamics of driving gear, engine accessories, ecology,
· Machines and devices for in-plant handling of material and handling between operations, for the mining and transport of building materials, for passenger conveyance in buildings,
· Aerodynamic calculation and designing, flight mechanics, fatigue and durability of aircraft constructions, aeroelasticity of aircraft,
· Quality of machine industry production.

prof. Ing. Ivan Křupka, Ph.D.

1. Implementation of innovative approaches for increasing the production accuracy of CNC machine tools

   The aim of this dissertation is based on the system approach to create an innovative approach for the production accuracy of CNC machine toolsin industry. Innovative approaches can lead to great advances in manufacturing precision. The introduction of these elements in production puts high demands on the demonstration of functionality in the production plant. Part of this work is to propose procedures that will lead to the increase of production accuracy using modern progressive methods, as well as methods of efficient implementation of these methods in the production. The expected output of the thesis is the proposal of a methodical procedure for increasing and introducing production accuracy of CNC machine tools using innovative approaches.

   Tutor: Blecha Petr, doc. Ing., Ph.D., FEng.

2. Influence of machining technology on the form accuracy of the workpiece

   The aim of this dissertation is based on a system approach analysis and description of the influence of machining technology settings on the resulting form accuracy of the workpiece. The setting of cutting conditions is subject to many factors for which optimal cutting conditions are sought. In the field of precision manufacturing, these are mainly finishing operations, where the requirement is to machine the part in the required dimensional and form tolerances, including the prescribed surface quality. The geometric accuracy of the machine in finishing operations is closely linked to the setting of cutting conditions, which leads to the question of how much we are able to influence the resulting dimensional and form accuracy by setting the geometry of the machine and setting the cutting conditions. The expected outcome of the work is the analysis and description of the influence of setting cutting conditions on the final form of the workpiece and the use of this knowledge to further refine the prediction of dimensional and form accuracy of machined parts in precision engineering.

   Tutor: Blecha Petr, doc. Ing., Ph.D., FEng.

3. Machine learning methods for assesment and prediction of the machine state in the context of Industry 4.0

   One of the accompanying phenomena that can be associated with the phenomenon of the so-called Industry 4.0 is a targeted increase in the sensors equipment of machine-tools, which results in a more difficult contextualization of the collected data. The effort to obtain more accurate information about the condition of the machine is therefore usually not successful. The aim of this dissertation is to create a self-sustaining system for sufficiently accurate indication and prediction of the state of the machine- tool. The core of which will be machine learning methods.

   Tutor: Marek Jiří, prof. Dr. Ing., Ph.D., DBA, FEng.