Sekundarni povzetek: |
Improving of the current production and machining systems demands for constant upgrading and integrating latest technologies into manufacturing processes. Increasing manufacturing variables affect also increase of machining information, which has to be processed, and with increased information classic analytical methods of optimization fail. Due to demanding market, complex product design, tendency for more successful manufacturing management with minimal production costs, it is of the essence to use optimal manufacturing resources. Therefore, an advanced approach for problem solution has to be used. For such advanced problems, most commonly are used different approaches of artificial intelligence, especially machine learning. Review of the researches made so far, has shown, that current developed systems are much specialised, and not flexible. In presented dissertation we propose a completely new approach for modelling CNC-machining processes using new Gravitational search algorithm (GSA), which is in essence swarm intelligence method. Developed intelligent system works on basic Newtonian laws, on elementary mass objects interaction in search space. For GSA modelling verification particle swarm optimisation (PSO) method was used. Comparative method PSO has shown, that the GSA-algorithm is appropriate for machining parameters processing, as the deviations are minimal. Acquired models described the cutting procedure by turning successfully. Important fact is also, that the GSA method has shown an improvement in terms of data processing time, as the procedure reduces processing time for a minimum of half. Results have shown, that the processing time of the GSA method is at least twice as fast, comparing to the PSO method. Appropriately designed model of CNC-machining can be used for optimizing machining parameters for the purpose of achieving optimal surface roughness, cutting forces, and increasing of the tool life. Each of the dependent variables adds a share to optimal functioning of the machine tool, which reduces production costs. Multi-objective optimisation is most efficiently made by using NSGA-II algorithm, with most accurate results. For the purpose of multi-objective optimisation constraints have to be determined. These were based on theoretical calculations and confirmed with experimental measurements. Due to dissertation comprehension, machining processes have been limited to turning, yet in the dissertation were presented also required basic differences for successful adaptation of the optimization for other machining processes. |