CASTING IMPROVEMENT BASED ON METAHEURISTIC OPTIMIZATION AND NUMERICAL SIMULATION

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...

Full description

Saved in:
Bibliographic Details
Published in:Facta Universitatis. Series: Mechanical Engineering 2017-12, Vol.15 (3), p.397-411
Main Authors: Radiša, Radomir, Dučić, Nedeljko, Manasijević, Srećko, Marković, Nemanja, Ćojbašić, Žarko
Format: Article
Language:English
Subjects:
Ant colony optimization
Casting
Computer simulation
Design optimization
Genetic algorithms
Heuristic methods
Optimization techniques
Particle swarm optimization
Simulated annealing
Turbines
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0354-2025
2335-0164
DOI:10.22190/FUME170505022R