CASTING IMPROVEMENT BASED ON METAHEURISTIC OPTIMIZATION AND NUMERICAL SIMULATION

Abstract

This paper presents the use of metaheuristic optimization techniques to support the improvement of casting process. Genetic algorithm (GA), Ant Colony Optimization (ACO), Simulated annealing (SA) and Particle Swarm Optimization (PSO) have been considered as optimization tools to define the geometry of the casting part’s feeder. The proposed methodology has been demonstrated in the design of the feeder for casting Pelton turbine bucket. The results of the optimization are dimensional characteristics of the feeder, and the best result from all the implemented optimization processes has been adopted. Numerical simulation has been used to verify the validity of the presented design methodology and the feeding system optimization in the casting system of the Pelton turbine bucket.