Fuzzy Parameters and Cutting Forces Optimization via Genetic Algorithm Approach

Abstract

The classification of solved signal features for manufacturing process condition monitoring has been carried out using fuzzy parameters optimization processing. In cases where assumptions in respect of nonlinear behavior cannot be made, the need to describe mathematically, ever increasing complexity become difficult and perhaps infeasible. The optimization possibilities of the fuzzy system parameters using genetic algorithms are studied. An analytical function determines the positions of the output fuzzy sets in each mapping process, that substitute the fuzzy rule base used in conventional approach. We realize case adaptation by adjusting the fuzzy sets parameters. Fuzzy parameters within optimization procedure could be multiobjective. We solve also the system for cutting process simulation, which contains the experimental model and the simulation model based on genetic algorithms. There is developed a genetic algorithm based simulation procedure for the prediction of the cutting forces. These genetic algorithms methodologies are suitable for fuzzy implementation control and for solution of large-scale problems.KeywordsFuzzy parameter optimizationcutting forces optimizationgenetic algorithmfitness